1
|
Schwab RJ, Erus G. We Can Use Machine Learning to Predict Obstructive Sleep Apnea. Am J Respir Crit Care Med 2024. [PMID: 38701391 DOI: 10.1164/rccm.202403-0666ed] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Accepted: 05/02/2024] [Indexed: 05/05/2024] Open
Affiliation(s)
- Richard J Schwab
- University of Pennsylvania, 6572, Sleep Division, Philadelphia, Pennsylvania, United States;
| | - Guray Erus
- University of Pennsylvania, 6572, 2. Director of Research, Center for Biomedical Image Computing and Analytics (CBICA), , Philadelphia, Pennsylvania, United States
| |
Collapse
|
2
|
Skampardoni I, Nasrallah IM, Abdulkadir A, Wen J, Melhem R, Mamourian E, Erus G, Doshi J, Singh A, Yang Z, Cui Y, Hwang G, Ren Z, Pomponio R, Srinivasan D, Govindarajan ST, Parmpi P, Wittfeld K, Grabe HJ, Bülow R, Frenzel S, Tosun D, Bilgel M, An Y, Marcus DS, LaMontagne P, Heckbert SR, Austin TR, Launer LJ, Sotiras A, Espeland MA, Masters CL, Maruff P, Fripp J, Johnson SC, Morris JC, Albert MS, Bryan RN, Yaffe K, Völzke H, Ferrucci L, Benzinger TL, Ezzati A, Shinohara RT, Fan Y, Resnick SM, Habes M, Wolk D, Shou H, Nikita K, Davatzikos C. Genetic and Clinical Correlates of AI-Based Brain Aging Patterns in Cognitively Unimpaired Individuals. JAMA Psychiatry 2024; 81:456-467. [PMID: 38353984 PMCID: PMC10867779 DOI: 10.1001/jamapsychiatry.2023.5599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Accepted: 11/29/2023] [Indexed: 02/17/2024]
Abstract
Importance Brain aging elicits complex neuroanatomical changes influenced by multiple age-related pathologies. Understanding the heterogeneity of structural brain changes in aging may provide insights into preclinical stages of neurodegenerative diseases. Objective To derive subgroups with common patterns of variation in participants without diagnosed cognitive impairment (WODCI) in a data-driven manner and relate them to genetics, biomedical measures, and cognitive decline trajectories. Design, Setting, and Participants Data acquisition for this cohort study was performed from 1999 to 2020. Data consolidation and harmonization were conducted from July 2017 to July 2021. Age-specific subgroups of structural brain measures were modeled in 4 decade-long intervals spanning ages 45 to 85 years using a deep learning, semisupervised clustering method leveraging generative adversarial networks. Data were analyzed from July 2021 to February 2023 and were drawn from the Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) international consortium. Individuals WODCI at baseline spanning ages 45 to 85 years were included, with greater than 50 000 data time points. Exposures Individuals WODCI at baseline scan. Main Outcomes and Measures Three subgroups, consistent across decades, were identified within the WODCI population. Associations with genetics, cardiovascular risk factors (CVRFs), amyloid β (Aβ), and future cognitive decline were assessed. Results In a sample of 27 402 individuals (mean [SD] age, 63.0 [8.3] years; 15 146 female [55%]) WODCI, 3 subgroups were identified in contrast with the reference group: a typical aging subgroup, A1, with a specific pattern of modest atrophy and white matter hyperintensity (WMH) load, and 2 accelerated aging subgroups, A2 and A3, with characteristics that were more distinct at age 65 years and older. A2 was associated with hypertension, WMH, and vascular disease-related genetic variants and was enriched for Aβ positivity (ages ≥65 years) and apolipoprotein E (APOE) ε4 carriers. A3 showed severe, widespread atrophy, moderate presence of CVRFs, and greater cognitive decline. Genetic variants associated with A1 were protective for WMH (rs7209235: mean [SD] B = -0.07 [0.01]; P value = 2.31 × 10-9) and Alzheimer disease (rs72932727: mean [SD] B = 0.1 [0.02]; P value = 6.49 × 10-9), whereas the converse was observed for A2 (rs7209235: mean [SD] B = 0.1 [0.01]; P value = 1.73 × 10-15 and rs72932727: mean [SD] B = -0.09 [0.02]; P value = 4.05 × 10-7, respectively); variants in A3 were associated with regional atrophy (rs167684: mean [SD] B = 0.08 [0.01]; P value = 7.22 × 10-12) and white matter integrity measures (rs1636250: mean [SD] B = 0.06 [0.01]; P value = 4.90 × 10-7). Conclusions and Relevance The 3 subgroups showed distinct associations with CVRFs, genetics, and subsequent cognitive decline. These subgroups likely reflect multiple underlying neuropathologic processes and affect susceptibility to Alzheimer disease, paving pathways toward patient stratification at early asymptomatic stages and promoting precision medicine in clinical trials and health care.
Collapse
Affiliation(s)
- Ioanna Skampardoni
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | - Ilya M. Nasrallah
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Department of Radiology, University of Pennsylvania, Philadelphia
| | - Ahmed Abdulkadir
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Junhao Wen
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Laboratory of AI and Biomedical Science, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles
| | - Randa Melhem
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Elizabeth Mamourian
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Guray Erus
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Jimit Doshi
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Ashish Singh
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Zhijian Yang
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Yuhan Cui
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Gyujoon Hwang
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Zheng Ren
- Laboratory of AI and Biomedical Science, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles
| | - Raymond Pomponio
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Dhivya Srinivasan
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | | | - Paraskevi Parmpi
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
- German Centre for Neurodegenerative Diseases, Site Greifswald, Greifswald, Germany
| | - Hans J. Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
- German Centre for Neurodegenerative Diseases, Site Greifswald, Greifswald, Germany
| | - Robin Bülow
- Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Stefan Frenzel
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California, San Francisco
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Daniel S. Marcus
- Department of Radiology, Washington University School of Medicine, St Louis, Missouri
| | - Pamela LaMontagne
- Department of Radiology, Washington University School of Medicine, St Louis, Missouri
| | - Susan R. Heckbert
- Cardiovascular Health Research Unit, University of Washington, Seattle
- Department of Epidemiology, University of Washington, Seattle
| | - Thomas R. Austin
- Cardiovascular Health Research Unit, University of Washington, Seattle
- Department of Epidemiology, University of Washington, Seattle
| | - Lenore J. Launer
- Neuroepidemiology Section, Intramural Research Program, National Institute on Aging, Bethesda, Maryland
| | - Aristeidis Sotiras
- Department of Radiology and Institute of Informatics, Washington University in St Louis, St Louis, Missouri
| | - Mark A. Espeland
- Sticht Centre for Healthy Aging and Alzheimer’s Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Colin L. Masters
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Paul Maruff
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Brisbane, Queensland, Australia
| | - Sterling C. Johnson
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison
| | - John C. Morris
- Knight Alzheimer Disease Research Centre, Washington University in St Louis, St Louis, Missouri
| | - Marilyn S. Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - R. Nick Bryan
- Department of Radiology, University of Pennsylvania, Philadelphia
| | - Kristine Yaffe
- Departments of Neurology, Psychiatry and Epidemiology and Biostatistics, University of California San Francisco, San Francisco
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Luigi Ferrucci
- Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Tammie L.S. Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Ali Ezzati
- Department of Neurology, University of California, Irvine
| | - Russell T. Shinohara
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia
| | - Yong Fan
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Mohamad Habes
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Neuroimage Analytics Laboratory and the Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio, San Antonio
| | - David Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia
| | - Haochang Shou
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia
| | - Konstantina Nikita
- School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | - Christos Davatzikos
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| |
Collapse
|
3
|
Dark HE, An Y, Duggan MR, Joynes C, Davatzikos C, Erus G, Lewis A, Moghekar AR, Resnick SM, Walker KA. Alzheimer's and neurodegenerative disease biomarkers in blood predict brain atrophy and cognitive decline. Alzheimers Res Ther 2024; 16:94. [PMID: 38689358 PMCID: PMC11059745 DOI: 10.1186/s13195-024-01459-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 04/16/2024] [Indexed: 05/02/2024]
Abstract
BACKGROUND Although blood-based biomarkers have been identified as cost-effective and scalable alternatives to PET and CSF markers of neurodegenerative disease, little is known about how these biomarkers predict future brain atrophy and cognitive decline in cognitively unimpaired individuals. Using data from the Baltimore Longitudinal Study of Aging (BLSA), we examined whether plasma biomarkers of Alzheimer's disease (AD) pathology (amyloid-β [Aβ42/40], phosphorylated tau [pTau-181]), astrogliosis (glial fibrillary acidic protein [GFAP]), and neuronal injury (neurofilament light chain [NfL]) were associated with longitudinal brain volume loss and cognitive decline. Additionally, we determined whether sex, APOEε4 status, and plasma amyloid-β status modified these associations. METHODS Plasma biomarkers were measured using Quanterix SIMOA assays. Regional brain volumes were measured by 3T MRI, and a battery of neuropsychological tests assessed five cognitive domains. Linear mixed effects models adjusted for demographic factors, kidney function, and intracranial volume (MRI analyses) were completed to relate baseline plasma biomarkers to baseline and longitudinal brain volume and cognitive performance. RESULTS Brain volume analyses included 622 participants (mean age ± SD: 70.9 ± 10.2) with an average of 3.3 MRI scans over 4.7 years. Cognitive performance analyses included 674 participants (mean age ± SD: 71.2 ± 10.0) with an average of 3.9 cognitive assessments over 5.7 years. Higher baseline pTau-181 was associated with steeper declines in total gray matter volume and steeper regional declines in several medial temporal regions, whereas higher baseline GFAP was associated with greater longitudinal increases in ventricular volume. Baseline Aβ42/40 and NfL levels were not associated with changes in brain volume. Lower baseline Aβ42/40 (higher Aβ burden) was associated with a faster decline in verbal memory and visuospatial performance, whereas higher baseline GFAP was associated with a faster decline in verbal fluency. Results were generally consistent across sex and APOEε4 status. However, the associations of higher pTau-181 with increasing ventricular volume and memory declines were significantly stronger among individuals with higher Aβ burden, as was the association of higher GFAP with memory decline. CONCLUSIONS Among cognitively unimpaired older adults, plasma biomarkers of AD pathology (pTau-181) and astrogliosis (GFAP), but not neuronal injury (NfL), serve as markers of future brain atrophy and cognitive decline.
Collapse
Affiliation(s)
- Heather E Dark
- Laboratory of Behavioral Neuroscience, National Institute On Aging, NIH BRC BG RM 04B311, 251 Bayview Blvd, Baltimore, MD, 21224, USA.
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute On Aging, NIH BRC BG RM 04B311, 251 Bayview Blvd, Baltimore, MD, 21224, USA
| | - Michael R Duggan
- Laboratory of Behavioral Neuroscience, National Institute On Aging, NIH BRC BG RM 04B311, 251 Bayview Blvd, Baltimore, MD, 21224, USA
| | - Cassandra Joynes
- Laboratory of Behavioral Neuroscience, National Institute On Aging, NIH BRC BG RM 04B311, 251 Bayview Blvd, Baltimore, MD, 21224, USA
| | | | - Guray Erus
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Alexandria Lewis
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Abhay R Moghekar
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute On Aging, NIH BRC BG RM 04B311, 251 Bayview Blvd, Baltimore, MD, 21224, USA
| | - Keenan A Walker
- Laboratory of Behavioral Neuroscience, National Institute On Aging, NIH BRC BG RM 04B311, 251 Bayview Blvd, Baltimore, MD, 21224, USA.
| |
Collapse
|
4
|
Wright RS, Allan AC, Gamaldo AA, Morgan AA, Lee AK, Erus G, Davatzikos C, Bygrave DC. Neighborhood disadvantage is associated with working memory and hippocampal volumes among older adults. Neuropsychol Dev Cogn B Aging Neuropsychol Cogn 2024:1-14. [PMID: 38656243 DOI: 10.1080/13825585.2024.2345926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 04/15/2024] [Indexed: 04/26/2024]
Abstract
It is not well understood how neighborhood disadvantage is associated with specific domains of cognitive function and underlying brain health within older adults. Thus, the objective was to examine associations between neighborhood disadvantage, brain health, and cognitive performance, and examine whether associations were more pronounced among women. The study included 136 older adults who underwent cognitive testing and MRI. Neighborhood disadvantage was characterized using the Area Deprivation Index (ADI). Descriptive statistics, bivariate correlations, and multiple regressions were run. Multiple regressions, adjusted for age, sex, education, and depression, showed that higher ADI state rankings (greater disadvantage) were associated with poorer working memory performance (p < .01) and lower hippocampal volumes (p < .01), but not total, frontal, and white matter lesion volumes, nor visual and verbal memory performance. There were no significant sex interactions. Findings suggest that greater neighborhood disadvantage may play a role in working memory and underlying brain structure.
Collapse
Affiliation(s)
| | - Alexa C Allan
- Department of Human Development and Family Studies, The Pennsylvania State University, State College, PA, USA
| | | | | | - Anna K Lee
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Guray Erus
- Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Desirée C Bygrave
- Department of Psychology, North Carolina Agricultural and Technical State University, Greensboro, NC, USA
| |
Collapse
|
5
|
Wen J, Zhao B, Yang Z, Erus G, Skampardoni I, Mamourian E, Cui Y, Hwang G, Bao J, Boquet-Pujadas A, Zhou Z, Veturi Y, Ritchie MD, Shou H, Thompson PM, Shen L, Toga AW, Davatzikos C. The genetic architecture of multimodal human brain age. Nat Commun 2024; 15:2604. [PMID: 38521789 PMCID: PMC10960798 DOI: 10.1038/s41467-024-46796-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Accepted: 03/06/2024] [Indexed: 03/25/2024] Open
Abstract
The complex biological mechanisms underlying human brain aging remain incompletely understood. This study investigated the genetic architecture of three brain age gaps (BAG) derived from gray matter volume (GM-BAG), white matter microstructure (WM-BAG), and functional connectivity (FC-BAG). We identified sixteen genomic loci that reached genome-wide significance (P-value < 5×10-8). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG displayed the most pronounced heritability enrichment in genetic variants within conserved regions. Oligodendrocytes and astrocytes, but not neurons, exhibited notable heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several chronic diseases on brain aging, such as type 2 diabetes on GM-BAG and AD on WM-BAG. Our results provide insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at https://labs.loni.usc.edu/medicine .
Collapse
Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ioanna Skampardoni
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Mamourian
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Gyujoon Hwang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | | | - Zhen Zhou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yogasudha Veturi
- Department of Biobehavioral Health and Statistics, Penn State University, University Park, PA, USA
| | - Marylyn D Ritchie
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Haochang Shou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, CA, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Arthur W Toga
- Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| |
Collapse
|
6
|
Browndyke JN, Tomalin LE, Erus G, Overbey JR, Kuceyeski A, Moskowitz AJ, Bagiella E, Iribarne A, Acker M, Mack M, Mathew J, O'Gara P, Gelijns AC, Suarez‐Farinas M, Messé SR. Infarct-related structural disconnection and delirium in surgical aortic valve replacement patients. Ann Clin Transl Neurol 2024; 11:263-277. [PMID: 38155462 PMCID: PMC10863920 DOI: 10.1002/acn3.51949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 10/28/2023] [Indexed: 12/30/2023] Open
Abstract
OBJECTIVE Although acute brain infarcts are common after surgical aortic valve replacement (SAVR), they are often unassociated with clinical stroke symptoms. The relationship between clinically "silent" infarcts and in-hospital delirium remains uncertain; obscured, in part, by how infarcts have been traditionally summarized as global metrics, independent of location or structural consequence. We sought to determine if infarct location and related structural connectivity changes were associated with postoperative delirium after SAVR. METHODS A secondary analysis of a randomized multicenter SAVR trial of embolic protection devices (NCT02389894) was conducted, excluding participants with clinical stroke or incomplete neuroimaging (N = 298; 39% female, 7% non-White, 74 ± 7 years). Delirium during in-hospital recovery was serially screened using the Confusion Assessment Method. Parcellation and tractography atlas-based neuroimaging methods were used to determine infarct locations and cortical connectivity effects. Mixed-effect, zero-inflated gaussian modeling analyses, accounting for brain region-specific infarct characteristics, were conducted to examine for differences within and between groups by delirium status and perioperative neuroprotection device strategy. RESULTS 23.5% participants experienced postoperative delirium. Delirium was associated with significantly increased lesion volumes in the right cerebellum and temporal lobe white matter, while diffusion weighted imaging infarct-related structural disconnection (DWI-ISD) was observed in frontal and temporal lobe regions (p-FDR < 0.05). Fewer brain regions demonstrated DWI-ISD loss in the suction-based neuroprotection device group, relative to filtration-based device or standard aortic cannula. INTERPRETATION Structural disconnection from acute infarcts was greater in patients who experienced postoperative delirium, suggesting that the impact from covert perioperative infarcts may not be as clinically "silent" as commonly assumed.
Collapse
Affiliation(s)
- Jeffrey N. Browndyke
- Division of Behavioral Medicine and Neurosciences, Department of Psychiatry and Behavioral SciencesDuke University Medical CenterDurhamNorth CarolinaUSA
- Division of Cardiovascular and Thoracic Surgery, Department of SurgeryDuke University Medical CenterDurhamNorth CarolinaUSA
| | - Lewis E. Tomalin
- Department of Population Health Science and PolicyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Guray Erus
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Jessica R. Overbey
- Department of Population Health Science and PolicyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Amy Kuceyeski
- Department of RadiologyWeill Cornell Medical CollegeNew YorkNew YorkUSA
- Brain and Mind Research InstituteWeill Cornell Medical CollegeNew YorkNew YorkUSA
| | - Alan J. Moskowitz
- Department of Population Health Science and PolicyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Emilia Bagiella
- Department of Population Health Science and PolicyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Alexander Iribarne
- Department of Cardiothoracic SurgeryStaten Island University Hospital, Northwell Health Staten IslandNew YorkNew YorkUSA
| | - Michael Acker
- Division of Cardiovascular Surgery, Department of SurgeryUniversity of Pennsylvania School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Michael Mack
- Department of Cardiothoracic SurgeryBaylor Research Institute, Baylor Scott and White HealthPlanoTexasUSA
| | - Joseph Mathew
- Department of AnesthesiologyDuke University Medical CenterDurhamNorth CarolinaUSA
| | - Patrick O'Gara
- Cardiovascular Division, Department of MedicineBrigham and Women's HospitalBostonMassachusettsUSA
| | - Annetine C. Gelijns
- Department of Population Health Science and PolicyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Mayte Suarez‐Farinas
- Department of Population Health Science and PolicyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Steven R. Messé
- Department of NeurologyUniversity of Pennsylvania School of MedicinePhiladelphiaPennsylvaniaUSA
| |
Collapse
|
7
|
Messé SR, Overbey JR, Thourani VH, Moskowitz AJ, Gelijns AC, Groh MA, Mack MJ, Ailawadi G, Furie KL, Southerland AM, James ML, Moy CS, Gupta L, Voisine P, Perrault LP, Bowdish ME, Gillinov AM, O'Gara PT, Ouzounian M, Whitson BA, Mullen JC, Miller MA, Gammie JS, Pan S, Erus G, Browndyke JN. The impact of perioperative stroke and delirium on outcomes after surgical aortic valve replacement. J Thorac Cardiovasc Surg 2024; 167:624-633.e4. [PMID: 35483981 PMCID: PMC9996687 DOI: 10.1016/j.jtcvs.2022.01.053] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 12/14/2021] [Accepted: 01/23/2022] [Indexed: 01/05/2023]
Abstract
OBJECTIVE The effects of stroke and delirium on postdischarge cognition and patient-centered health outcomes after surgical aortic valve replacement (SAVR) are not well characterized. Here, we assess the impact of postoperative stroke and delirium on these health outcomes in SAVR patients at 90 days. METHODS Patients (N = 383) undergoing SAVR (41% received concomitant coronary artery bypass graft) enrolled in a randomized trial of embolic protection devices underwent serial neurologic and delirium evaluations at postoperative days 1, 3, and 7 and magnetic resonance imaging at day 7. Outcomes included 90-day functional status, neurocognitive decline from presurgical baseline, and quality of life. RESULTS By postoperative day 7, 25 (6.6%) patients experienced clinical stroke and 103 (28.5%) manifested delirium. During index hospitalization, time to discharge was longer in patients experiencing stroke (hazard ratio, 0.62; 95% confidence interval [CI], 0.42-0.94; P = .02) and patients experiencing delirium (hazard ratio, 0.68; 95% CI, 0.54-0.86; P = .001). At day 90, patients experiencing stroke were more likely to have a modified Rankin score >2 (odds ratio [OR], 5.9; 95% CI, 1.7-20.1; P = .01), depression (OR, 5.3; 95% CI, 1.6-17.3; P = .006), a lower 12-Item Short Form Survey physical health score (adjusted mean difference -3.3 ± 1.9; P = .08), and neurocognitive decline (OR, 7.8; 95% CI, 2.3-26.4; P = .001). Delirium was associated with depression (OR, 2.2; 95% CI, 0.9-5.3; P = .08), lower 12-Item Short Form Survey physical health (adjusted mean difference -2.3 ± 1.1; P = .03), and neurocognitive decline (OR, 2.2; 95% CI, 1.2-4.0; P = .01). CONCLUSIONS Stroke and delirium occur more frequently after SAVR than is commonly recognized, and these events are associated with disability, depression, cognitive decline, and poorer quality of life at 90 days postoperatively. These findings support the need for new interventions to reduce these events and improve patient-centered outcomes.
Collapse
Affiliation(s)
- Steven R Messé
- Department of Stroke and Neurocritical Care, Perelman School of Medicine University of Pennsylvania, Philadelphia, Pa
| | - Jessica R Overbey
- International Center for Health Outcomes and Innovation Research (InCHOIR), The Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Vinod H Thourani
- Marcus Valve Center, Department of Cardiovascular Surgery, Piedmont Heart Institute, Atlanta, Ga
| | - Alan J Moskowitz
- International Center for Health Outcomes and Innovation Research (InCHOIR), The Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Annetine C Gelijns
- International Center for Health Outcomes and Innovation Research (InCHOIR), The Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY.
| | - Mark A Groh
- Asheville Heart, Mission Health and Hospitals, Asheville, NC
| | - Michael J Mack
- Cardiovascular Surgery, Baylor Scott & White Health, Plano, Tex
| | - Gorav Ailawadi
- Departments of Cardiac Surgery and Surgery, University of Michigan Health System, Ann Arbor, Mich
| | - Karen L Furie
- Department of Neurology, Alpert Medical School of Brown University, Providence, RI
| | - Andrew M Southerland
- Division of Vascular Neurology, University of Virginia Health System, Charlottesville, Va
| | - Michael L James
- Department of Anesthesiology, Duke University Medical Center, Durham, NC; Department of Neurology, Duke University Medical Center, Durham, NC
| | - Claudia Scala Moy
- Division of Clinical Research, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Md
| | - Lopa Gupta
- International Center for Health Outcomes and Innovation Research (InCHOIR), The Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Pierre Voisine
- Department of Surgery, Institut de Cardiologie et Pneumologie de Québec, Québec, Canada
| | | | - Michael E Bowdish
- Surgery and Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, Calif
| | - A Marc Gillinov
- Department of Thoracic and Cardiovascular Surgery, Cleveland Clinic, Cleveland, Ohio
| | - Patrick T O'Gara
- Cardiovascular Division, Brigham and Women's Hospital, Boston, Mass
| | - Maral Ouzounian
- Division of Cardiac Surgery, Department of Surgery, Peter Munk Cardiac Centre, UHN-Toronto General Hospital, Toronto, Ontario, Canada
| | - Bryan A Whitson
- Division of Cardiac Surgery, Department of Surgery, The Ohio State University, Columbus, Ohio
| | - John C Mullen
- Division of Cardiac Surgery, University of Alberta, Edmonton, Alberta, Canada
| | - Marissa A Miller
- Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Md
| | - James S Gammie
- Department of Cardiac Surgery, Johns Hopkins Heart and Vascular Institute, Baltimore, Md
| | - Stephanie Pan
- International Center for Health Outcomes and Innovation Research (InCHOIR), The Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Guray Erus
- Department of Radiology, Perelman School of Medicine University of Pennsylvania, Philadelphia, Pa
| | - Jeffrey N Browndyke
- Department of Psychiatry & Behavioral Sciences, Duke University Medical Center, Durham, NC
| |
Collapse
|
8
|
Heckbert SR, Jensen PN, Erus G, Nasrallah IM, Rashid T, Habes M, Austin TR, Floyd JS, Schaich CL, Redline S, Bryan RN, Costa MD. Heart rate fragmentation and brain MRI markers of small vessel disease in MESA. Alzheimers Dement 2024; 20:1397-1405. [PMID: 38009395 PMCID: PMC10917025 DOI: 10.1002/alz.13554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 10/12/2023] [Accepted: 10/23/2023] [Indexed: 11/28/2023]
Abstract
INTRODUCTION Heart rate (HR) fragmentation indices quantify breakdown of HR regulation and are associated with atrial fibrillation and cognitive impairment. Their association with brain magnetic resonance imaging (MRI) markers of small vessel disease is unexplored. METHODS In 606 stroke-free participants of the Multi-Ethnic Study of Atherosclerosis (mean age 67), HR fragmentation indices including percentage of inflection points (PIP) were derived from sleep study recordings. We examined PIP in relation to white matter hyperintensity (WMH) volume, total white matter fractional anisotropy (FA), and microbleeds from 3-Tesla brain MRI completed 7 years later. RESULTS In adjusted analyses, higher PIP was associated with greater WMH volume (14% per standard deviation [SD], 95% confidence interval [CI]: 2, 27%, P = 0.02) and lower WM FA (-0.09 SD per SD, 95% CI: -0.16, -0.01, P = 0.03). DISCUSSION HR fragmentation was associated with small vessel disease. HR fragmentation can be measured automatically from ambulatory electrocardiogram devices and may be useful as a biomarker of vascular brain injury.
Collapse
Affiliation(s)
- Susan R. Heckbert
- Cardiovascular Health Research UnitUniversity of WashingtonSeattleWashingtonUSA
- Department of EpidemiologyUniversity of WashingtonSeattleWashingtonUSA
| | - Paul N. Jensen
- Cardiovascular Health Research UnitUniversity of WashingtonSeattleWashingtonUSA
- Department of MedicineUniversity of WashingtonSeattleWashingtonUSA
| | - Guray Erus
- Center for AI and Data Science for Integrated Diagnostics and Center for Biomedical Image Computing and AnalyticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Ilya M. Nasrallah
- Center for AI and Data Science for Integrated Diagnostics and Center for Biomedical Image Computing and AnalyticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of RadiologyPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Tanweer Rashid
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging CoreGlenn Biggs Institute for Alzheimer's and Neurodegenerative DiseasesUniversity of Texas Health Science Center San AntonioSan AntonioTexasUSA
| | - Mohamad Habes
- Center for AI and Data Science for Integrated Diagnostics and Center for Biomedical Image Computing and AnalyticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging CoreGlenn Biggs Institute for Alzheimer's and Neurodegenerative DiseasesUniversity of Texas Health Science Center San AntonioSan AntonioTexasUSA
| | - Thomas R. Austin
- Cardiovascular Health Research UnitUniversity of WashingtonSeattleWashingtonUSA
- Department of EpidemiologyUniversity of WashingtonSeattleWashingtonUSA
| | - James S. Floyd
- Cardiovascular Health Research UnitUniversity of WashingtonSeattleWashingtonUSA
- Department of EpidemiologyUniversity of WashingtonSeattleWashingtonUSA
- Department of MedicineUniversity of WashingtonSeattleWashingtonUSA
| | - Christopher L. Schaich
- Department of SurgeryHypertension and Vascular Research CenterWake Forest University School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Susan Redline
- Brigham and Women's HospitalBostonMassachusettsUSA
- Harvard Medical SchoolBostonMassachusettsUSA
| | - R. Nick Bryan
- Department of RadiologyPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Madalena D. Costa
- Harvard Medical SchoolBostonMassachusettsUSA
- Department of MedicineBeth Israel Deaconess Medical CenterBostonMassachusettsUSA
| |
Collapse
|
9
|
Yang Z, Wen J, Abdulkadir A, Cui Y, Erus G, Mamourian E, Melhem R, Srinivasan D, Govindarajan ST, Chen J, Habes M, Masters CL, Maruff P, Fripp J, Ferrucci L, Albert MS, Johnson SC, Morris JC, LaMontagne P, Marcus DS, Benzinger TLS, Wolk DA, Shen L, Bao J, Resnick SM, Shou H, Nasrallah IM, Davatzikos C. Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering. Nat Commun 2024; 15:354. [PMID: 38191573 PMCID: PMC10774282 DOI: 10.1038/s41467-023-44271-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 12/06/2023] [Indexed: 01/10/2024] Open
Abstract
Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and single nucleotide polymorphism data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-associated neuroimaging phenotypes.
Collapse
Affiliation(s)
- Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Graduate Group in Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Junhao Wen
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Ahmed Abdulkadir
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Mamourian
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Randa Melhem
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dhivya Srinivasan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sindhuja T Govindarajan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jiong Chen
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mohamad Habes
- Biggs Alzheimer's Institute, University of Texas San Antonio Health Science Center, San Antonio, TX, USA
| | - Colin L Masters
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Paul Maruff
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Brisbane, QLD, Australia
| | - Luigi Ferrucci
- Translational Gerontology Branch, Longitudinal Studies Section, National Institute on Aging, National Institutes of Health, MedStar Harbor Hospital, 3001 S. Hanover Street, Baltimore, MD, USA
| | - Marilyn S Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sterling C Johnson
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - John C Morris
- Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Pamela LaMontagne
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Daniel S Marcus
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Tammie L S Benzinger
- Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Haochang Shou
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ilya M Nasrallah
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| |
Collapse
|
10
|
Yang Z, Wen J, Erus G, Govindarajan ST, Melhem R, Mamourian E, Cui Y, Srinivasan D, Abdulkadir A, Parmpi P, Wittfeld K, Grabe HJ, Bülow R, Frenzel S, Tosun D, Bilgel M, An Y, Yi D, Marcus DS, LaMontagne P, Benzinger TL, Heckbert SR, Austin TR, Waldstein SR, Evans MK, Zonderman AB, Launer LJ, Sotiras A, Espeland MA, Masters CL, Maruff P, Fripp J, Toga A, O’Bryant S, Chakravarty MM, Villeneuve S, Johnson SC, Morris JC, Albert MS, Yaffe K, Völzke H, Ferrucci L, Bryan NR, Shinohara RT, Fan Y, Habes M, Lalousis PA, Koutsouleris N, Wolk DA, Resnick SM, Shou H, Nasrallah IM, Davatzikos C. Five dominant dimensions of brain aging are identified via deep learning: associations with clinical, lifestyle, and genetic measures. medRxiv 2023:2023.12.29.23300642. [PMID: 38234857 PMCID: PMC10793523 DOI: 10.1101/2023.12.29.23300642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Brain aging is a complex process influenced by various lifestyle, environmental, and genetic factors, as well as by age-related and often co-existing pathologies. MRI and, more recently, AI methods have been instrumental in understanding the neuroanatomical changes that occur during aging in large and diverse populations. However, the multiplicity and mutual overlap of both pathologic processes and affected brain regions make it difficult to precisely characterize the underlying neurodegenerative profile of an individual from an MRI scan. Herein, we leverage a state-of-the art deep representation learning method, Surreal-GAN, and present both methodological advances and extensive experimental results that allow us to elucidate the heterogeneity of brain aging in a large and diverse cohort of 49,482 individuals from 11 studies. Five dominant patterns of neurodegeneration were identified and quantified for each individual by their respective (herein referred to as) R-indices. Significant associations between R-indices and distinct biomedical, lifestyle, and genetic factors provide insights into the etiology of observed variances. Furthermore, baseline R-indices showed predictive value for disease progression and mortality. These five R-indices contribute to MRI-based precision diagnostics, prognostication, and may inform stratification into clinical trials.
Collapse
Affiliation(s)
- Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Graduate Group in Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sindhuja T. Govindarajan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Randa Melhem
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Mamourian
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dhivya Srinivasan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ahmed Abdulkadir
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Paraskevi Parmpi
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
| | - Hans J. Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Germany
| | - Robin Bülow
- Institute of Diagnostic Radiology and Neuroradiology, University of Greifswald, Germany
| | - Stefan Frenzel
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Dahyun Yi
- Institute of Human Behavioral Medicine, Medical Research Center Seoul National University, Seoul, Republic of Korea
| | - Daniel S. Marcus
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Pamela LaMontagne
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Tammie L.S. Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Susan R. Heckbert
- Cardiovascular Health Research Unit and Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Thomas R. Austin
- Cardiovascular Health Research Unit and Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Shari R. Waldstein
- Department of Psychology, University of Maryland, Baltimore County, Catonsville, MD, USA
| | - Michele K. Evans
- Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, NIA/NIH/IRP, Baltimore, MD, USA
| | - Alan B. Zonderman
- Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, NIA/NIH/IRP, Baltimore, MD, USA
| | - Lenore J. Launer
- Neuroepidemiology Section, Intramural Research Program, National Institute on Aging, Bethesda, Maryland, USA
| | - Aristeidis Sotiras
- Department of Radiology and Institute of Informatics, Washington University in St. Luis, St. Luis, MO63110, USA
| | - Mark A. Espeland
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Colin L. Masters
- Florey Institute, The University of Melbourne, Parkville, VIC, 3052, Australia
| | - Paul Maruff
- Florey Institute, The University of Melbourne, Parkville, VIC, 3052, Australia
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Brisbane, Queensland, Australia
| | - Arthur Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Sid O’Bryant
- Institute for Translational Research University of North Texas Health Science Center Fort Worth Texas USA
| | - Mallar M. Chakravarty
- Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, McGill University, Verdun, Quebec, Canada
| | - Sylvia Villeneuve
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Sterling C. Johnson
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - John C. Morris
- Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Marilyn S. Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Kristine Yaffe
- Departments of Neurology, Psychiatry and Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Luigi Ferrucci
- Translational Gerontology Branch, Longitudinal Studies Section, National Institute on Aging, National Institutes of Health, MedStar Harbor Hospital, 3001 S. Hanover Street, Baltimore, MD, USA
| | - Nick R. Bryan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T. Shinohara
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Yong Fan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mohamad Habes
- Biggs Alzheimer’s Institute, University of Texas San Antonio Health Science Center, USA
| | - Paris Alexandros Lalousis
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Nikolaos Koutsouleris
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
- Section for Precision Psychiatry, Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University Munich, Munich, Germany
| | - David A. Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Haochang Shou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ilya M. Nasrallah
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| |
Collapse
|
11
|
Wen J, Nasrallah IM, Abdulkadir A, Satterthwaite TD, Yang Z, Erus G, Robert-Fitzgerald T, Singh A, Sotiras A, Boquet-Pujadas A, Mamourian E, Doshi J, Cui Y, Srinivasan D, Skampardoni I, Chen J, Hwang G, Bergman M, Bao J, Veturi Y, Zhou Z, Yang S, Dazzan P, Kahn RS, Schnack HG, Zanetti MV, Meisenzahl E, Busatto GF, Crespo-Facorro B, Pantelis C, Wood SJ, Zhuo C, Shinohara RT, Gur RC, Gur RE, Koutsouleris N, Wolf DH, Saykin AJ, Ritchie MD, Shen L, Thompson PM, Colliot O, Wittfeld K, Grabe HJ, Tosun D, Bilgel M, An Y, Marcus DS, LaMontagne P, Heckbert SR, Austin TR, Launer LJ, Espeland M, Masters CL, Maruff P, Fripp J, Johnson SC, Morris JC, Albert MS, Bryan RN, Resnick SM, Fan Y, Habes M, Wolk D, Shou H, Davatzikos C. Genomic loci influence patterns of structural covariance in the human brain. Proc Natl Acad Sci U S A 2023; 120:e2300842120. [PMID: 38127979 PMCID: PMC10756284 DOI: 10.1073/pnas.2300842120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 10/31/2023] [Indexed: 12/23/2023] Open
Abstract
Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are newly identified, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate genetic and biological underpinnings that influence structural covariance patterns in the human brain.
Collapse
Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science, Department of Neurology, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA90033
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Ilya M. Nasrallah
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Department of Radiology, University of Pennsylvania, Philadelphia, PA19104
| | - Ahmed Abdulkadir
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Theodore D. Satterthwaite
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Zhijian Yang
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Guray Erus
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Timothy Robert-Fitzgerald
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Ashish Singh
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Aristeidis Sotiras
- Department of Radiology, Washington University School of Medicine, St. Louis, MO63110
| | - Aleix Boquet-Pujadas
- Biomedical Imaging Group, Department of Biomedical Engineering, École Polytechnique Fédérale de Lausanne, Lausanne1015, Switzerland
| | - Elizabeth Mamourian
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Jimit Doshi
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Yuhan Cui
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Dhivya Srinivasan
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Ioanna Skampardoni
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Jiong Chen
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Gyujoon Hwang
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Mark Bergman
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA19104
| | - Yogasudha Veturi
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Zhen Zhou
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Shu Yang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA19104
| | - Paola Dazzan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, LondonWC2R 2LS, United Kingdom
| | - Rene S. Kahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Hugo G. Schnack
- Department of Psychiatry, University Medical Center Utrecht, Utrecht 3584 CX Ut, Netherlands
| | - Marcus V. Zanetti
- Institute of Psychiatry, Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo05508-070, Brazil
| | - Eva Meisenzahl
- Department of Psychiatry and Psychotherapy, Heinrich Heine University, Düsseldorf40204, Germany
| | - Geraldo F. Busatto
- Institute of Psychiatry, Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo05508-070, Brazil
| | - Benedicto Crespo-Facorro
- Hospital Universitario Virgen del Rocio, School of Medicine, University of Sevilla,Sevilla41004, Spain
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Stephen J. Wood
- Orygen and the Centre for Youth Mental Health, Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Chuanjun Zhuo
- Key Laboratory of Real Tine Tracing of Brain Circuits in Psychiatry and Neurology, Department of Psychiatry, Tianjin Medical University, Tianjin300070, China
| | - Russell T. Shinohara
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Ruben C. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Raquel E. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich 80539, Germany
| | - Daniel H. Wolf
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Andrew J. Saykin
- Indiana Alzheimer’s Disease Research Center, Department of Radiology, Indiana University School of Medicine, Indianapolis, IN46202-3082
| | - Marylyn D. Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA19104
| | - Paul M. Thompson
- Imaging Genetics Center, Department of Neurology, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA90033
| | - Olivier Colliot
- Institut du Cerveau, Sorbonne Université, Paris75013, France
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, German Center for Neurodegenerative Diseases, University Medicine Greifswald, Greifswald17475, Germany
| | - Hans J. Grabe
- Department of Psychiatry and Psychotherapy, German Center for Neurodegenerative Diseases, University Medicine Greifswald, Greifswald17475, Germany
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA 94143
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore21224, MD
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore21224, MD
| | - Daniel S. Marcus
- Department of Radiology, Washington University School of Medicine, St. Louis, MO63110
| | - Pamela LaMontagne
- Department of Radiology, Washington University School of Medicine, St. Louis, MO63110
| | - Susan R. Heckbert
- Department of Epidemiology, University of Washington, Seattle, WA98195
| | - Thomas R. Austin
- Department of Epidemiology, University of Washington, Seattle, WA98195
| | - Lenore J. Launer
- Neuroepidemiology Section, Intramural Research Program, National Institute on Aging, Washington, MD20817
| | - Mark Espeland
- Sticht Center for Healthy Aging and Alzheimer’s Prevention, Divisions of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, NC27101
| | - Colin L. Masters
- Florey Institute of Neuroscience and Mental Health, Medicine, Dentistry and Health Sciences, The University of Melbourne, Parkville, VIC3010, Australia
| | - Paul Maruff
- Florey Institute of Neuroscience and Mental Health, Medicine, Dentistry and Health Sciences, The University of Melbourne, Parkville, VIC3010, Australia
| | - Jurgen Fripp
- Health and Biosecurity, Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, QLD4029, Australia
| | - Sterling C. Johnson
- Wisconsin Alzheimer's Institute, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI53792
| | - John C. Morris
- Knight Alzheimer Disease Research Center, Department of Neurology, Washington University in St. Louis, St. Louis, MO63110
| | - Marilyn S. Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD21205
| | - R. Nick Bryan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA19104
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore21224, MD
| | - Yong Fan
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Mohamad Habes
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, Department of Radiology, University of Texas Health Science Center at San Antonio, San Antonio, TX78229
| | - David Wolk
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Department of Neurology, University of Pennsylvania, Philadelphia, PA19104
| | - Haochang Shou
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Christos Davatzikos
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| |
Collapse
|
12
|
Wang X, Salminen LE, Petkus AJ, Driscoll I, Millstein J, Beavers DP, Espeland MA, Erus G, Braskie MN, Thompson PM, Gatz M, Chui HC, Resnick SM, Kaufman JD, Rapp SR, Shumaker S, Brown M, Younan D, Chen JC. Association between late-life air pollution exposure and medial temporal lobe atrophy in older women. medRxiv 2023:2023.11.28.23298708. [PMID: 38077091 PMCID: PMC10705610 DOI: 10.1101/2023.11.28.23298708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Background Ambient air pollution exposures increase risk for Alzheimer's disease (AD) and related dementias, possibly due to structural changes in the medial temporal lobe (MTL). However, existing MRI studies examining exposure effects on the MTL were cross-sectional and focused on the hippocampus, yielding mixed results. Method To determine whether air pollution exposures were associated with MTL atrophy over time, we conducted a longitudinal study including 653 cognitively unimpaired community-dwelling older women from the Women's Health Initiative Memory Study with two MRI brain scans (MRI-1: 2005-6; MRI-2: 2009-10; Mage at MRI-1=77.3±3.5years). Using regionalized universal kriging models, exposures at residential locations were estimated as 3-year annual averages of fine particulate matter (PM2.5) and nitrogen dioxide (NO2) prior to MRI-1. Bilateral gray matter volumes of the hippocampus, amygdala, parahippocampal gyrus (PHG), and entorhinal cortex (ERC) were summed to operationalize the MTL. We used linear regressions to estimate exposure effects on 5-year volume changes in the MTL and its subregions, adjusting for intracranial volume, sociodemographic, lifestyle, and clinical characteristics. Results On average, MTL volume decreased by 0.53±1.00cm3 over 5 years. For each interquartile increase of PM2.5 (3.26μg/m3) and NO2 (6.77ppb), adjusted MTL volume had greater shrinkage by 0.32cm3 (95%CI=[-0.43, -0.21]) and 0.12cm3 (95%CI=[-0.22, -0.01]), respectively. The exposure effects did not differ by APOE ε4 genotype, sociodemographic, and cardiovascular risk factors, and remained among women with low-level PM2.5 exposure. Greater PHG atrophy was associated with higher PM2.5 (b=-0.24, 95%CI=[-0.29, -0.19]) and NO2 exposures (b=-0.09, 95%CI=[-0.14, -0.04]). Higher exposure to PM2.5 but not NO2 was also associated with greater ERC atrophy. Exposures were not associated with amygdala or hippocampal atrophy. Conclusion In summary, higher late-life PM2.5 and NO2 exposures were associated with greater MTL atrophy over time in cognitively unimpaired older women. The PHG and ERC - the MTL cortical subregions where AD neuropathologies likely begin, may be preferentially vulnerable to air pollution neurotoxicity.
Collapse
Affiliation(s)
- Xinhui Wang
- Department of Neurology, University of Southern California, Los Angeles, California
| | - Lauren E Salminen
- Department of Neurology, University of Southern California, Los Angeles, California
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Andrew J Petkus
- Department of Neurology, University of Southern California, Los Angeles, California
| | - Ira Driscoll
- Department of Medicine, University of Wisconsin-Madison, Madison, Wisconsin
| | - Joshua Millstein
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California
| | - Daniel P Beavers
- Departments of Statistical Sciences, Wake Forest University, Winston-Salem, North Carolina
| | - Mark A Espeland
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina
- Department of Biostatistics and Data Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pennsylvania
| | - Meredith N Braskie
- Department of Neurology, University of Southern California, Los Angeles, California
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Paul M Thompson
- Department of Neurology, University of Southern California, Los Angeles, California
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Margaret Gatz
- Center for Economic and Social Research, University of Southern California, Los Angeles, California
| | - Helena C Chui
- Department of Neurology, University of Southern California, Los Angeles, California
| | - Susan M Resnick
- The Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland
| | - Joel D Kaufman
- Departments of Environmental & Occupational Health Sciences, Medicine (General Internal Medicine), and Epidemiology, University of Washington, Seattle, Washington
| | - Stephen R Rapp
- Departments of Psychiatry and Behavioral Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina
- Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Sally Shumaker
- Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Mark Brown
- Department of Biostatistics and Data Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Diana Younan
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California
| | - Jiu-Chiuan Chen
- Department of Neurology, University of Southern California, Los Angeles, California
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California
| |
Collapse
|
13
|
Cordon J, Duggan MR, Gomez GT, Pucha K, Peng Z, Dark HE, Davatzikos C, Erus G, Lewis A, Moghekar A, Candia J, Ferrucci L, Kapogiannis D, Walker KA. Identification of Clinically Relevant Brain Endothelial Cell Biomarkers in Plasma. Stroke 2023; 54:2853-2863. [PMID: 37814955 PMCID: PMC10608795 DOI: 10.1161/strokeaha.123.043908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 09/12/2023] [Indexed: 10/11/2023]
Abstract
BACKGROUND Proteins expressed by brain endothelial cells (BECs), the primary cell type of the blood-brain barrier, may serve as sensitive plasma biomarkers for neurological and neurovascular conditions, including cerebral small vessel disease. METHODS Using data from the BLSA (Baltimore Longitudinal Study of Aging; n=886; 2009-2020), BEC-enriched proteins were identified among 7268 plasma proteins (measured with SomaScanv4.1) using an automated annotation algorithm that filtered endothelial cell transcripts followed by cross-referencing with BEC-specific transcripts reported in single-cell RNA-sequencing studies. To identify BEC-enriched proteins in plasma most relevant to the maintenance of neurological and neurovascular health, we selected proteins significantly associated with 3T magnetic resonance imaging-defined white matter lesion volumes. We then examined how these candidate BEC biomarkers related to white matter lesion volumes, cerebral microhemorrhages, and lacunar infarcts in the ARIC study (Atherosclerosis Risk in Communities; US multisite; 1990-2017). Finally, we determined whether these candidate BEC biomarkers, when measured during midlife, were related to dementia risk over a 25-year follow-up period. RESULTS Of the 28 proteins identified as BEC-enriched, 4 were significantly associated with white matter lesion volumes (CDH5 [cadherin 5], CD93 [cluster of differentiation 93], ICAM2 [intracellular adhesion molecule 2], GP1BB [glycoprotein 1b platelet subunit beta]), while another approached significance (RSPO3 [R-Spondin 3]). A composite score based on 3 of these BEC proteins accounted for 11% of variation in white matter lesion volumes in BLSA participants. We replicated the associations between the BEC composite score, CDH5, and RSPO3 with white matter lesion volumes in ARIC, and further demonstrated that the BEC composite score and RSPO3 were associated with the presence of ≥1 cerebral microhemorrhages. We also showed that the BEC composite score, CDH5, and RSPO3 were associated with 25-year dementia risk. CONCLUSIONS In addition to identifying BEC proteins in plasma that relate to cerebral small vessel disease and dementia risk, we developed a composite score of plasma BEC proteins that may be used to estimate blood-brain barrier integrity and risk for adverse neurovascular outcomes.
Collapse
Affiliation(s)
- Jenifer Cordon
- Multimodal Imaging of Neurodegenerative Disease (MIND) Unit, NIA
| | | | | | | | - Zhongsheng Peng
- Multimodal Imaging of Neurodegenerative Disease (MIND) Unit, NIA
| | - Heather E. Dark
- Multimodal Imaging of Neurodegenerative Disease (MIND) Unit, NIA
| | | | | | | | | | | | | | | | - Keenan A. Walker
- Multimodal Imaging of Neurodegenerative Disease (MIND) Unit, NIA
| |
Collapse
|
14
|
Bao J, Wen J, Wen Z, Yang S, Cui Y, Yang Z, Erus G, Saykin AJ, Long Q, Davatzikos C, Shen L. Brain-wide genome-wide colocalization study for integrating genetics, transcriptomics and brain morphometry in Alzheimer's disease. Neuroimage 2023; 280:120346. [PMID: 37634885 PMCID: PMC10552907 DOI: 10.1016/j.neuroimage.2023.120346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 06/19/2023] [Accepted: 08/22/2023] [Indexed: 08/29/2023] Open
Abstract
Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. However, the AD mechanism has not yet been fully elucidated to date, hindering the development of effective therapies. In our work, we perform a brain imaging genomics study to link genetics, single-cell gene expression data, tissue-specific gene expression data, brain imaging-derived volumetric endophenotypes, and disease diagnosis to discover potential underlying neurobiological pathways for AD. To do so, we perform brain-wide genome-wide colocalization analyses to integrate multidimensional imaging genomic biobank data. Specifically, we use (1) the individual-level imputed genotyping data and magnetic resonance imaging (MRI) data from the UK Biobank, (2) the summary statistics of the genome-wide association study (GWAS) from multiple European ancestry cohorts, and (3) the tissue-specific cis-expression quantitative trait loci (cis-eQTL) summary statistics from the GTEx project. We apply a Bayes factor colocalization framework and mediation analysis to these multi-modal imaging genomic data. As a result, we derive the brain regional level GWAS summary statistics for 145 brain regions with 482,831 single nucleotide polymorphisms (SNPs) followed by posthoc functional annotations. Our analysis yields the discovery of a potential AD causal pathway from a systems biology perspective: the SNP chr10:124165615:G>A (rs6585827) mutation upregulates the expression of BTBD16 gene in oligodendrocytes, a specialized glial cells, in the brain cortex, leading to a reduced risk of volumetric loss in the entorhinal cortex, resulting in the protective effect on AD. We substantiate our findings with multiple evidence from existing imaging, genetic and genomic studies in AD literature. Our study connects genetics, molecular and cellular signatures, regional brain morphologic endophenotypes, and AD diagnosis, providing new insights into the mechanistic understanding of the disease. Our findings can provide valuable guidance for subsequent therapeutic target identification and drug discovery in AD.
Collapse
Affiliation(s)
- Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Junhao Wen
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Laboratory of AI and Biomedical Science, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, CA 90292, USA
| | - Zixuan Wen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Shu Yang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Yuhan Cui
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Zhijian Yang
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Qi Long
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.
| |
Collapse
|
15
|
Wen J, Zhao B, Yang Z, Erus G, Skampardoni I, Mamourian E, Cui Y, Hwang G, Bao J, Boquet-Pujadas A, Zhou Z, Veturi Y, Ritchie MD, Shou H, Thompson PM, Shen L, Toga AW, Davatzikos C. The Genetic Architecture of Multimodal Human Brain Age. bioRxiv 2023:2023.04.13.536818. [PMID: 37333190 PMCID: PMC10274645 DOI: 10.1101/2023.04.13.536818] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
The complex biological mechanisms underlying human brain aging remain incompletely understood, involving multiple body organs and chronic diseases. In this study, we used multimodal magnetic resonance imaging and artificial intelligence to examine the genetic architecture of the brain age gap (BAG) derived from gray matter volume (GM-BAG, N=31,557 European ancestry), white matter microstructure (WM-BAG, N=31,674), and functional connectivity (FC-BAG, N=32,017). We identified sixteen genomic loci that reached genome-wide significance (P-value<5×10-8). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG showed the highest heritability enrichment for genetic variants in conserved regions, whereas WM-BAG exhibited the highest heritability enrichment in the 5' untranslated regions; oligodendrocytes and astrocytes, but not neurons, showed significant heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several exposure variables on brain aging, such as type 2 diabetes on GM-BAG (odds ratio=1.05 [1.01, 1.09], P-value=1.96×10-2) and AD on WM-BAG (odds ratio=1.04 [1.02, 1.05], P-value=7.18×10-5). Overall, our results provide valuable insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at the MEDICINE knowledge portal: https://labs.loni.usc.edu/medicine.
Collapse
Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Ioanna Skampardoni
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Elizabeth Mamourian
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Gyujoon Hwang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | | | - Zhen Zhou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Yogasudha Veturi
- Department of Biobehavioral Health and Statistics, Penn State University, University Park, PA, USA
| | - Marylyn D Ritchie
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Haochang Shou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, California
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | - Arthur W Toga
- Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| |
Collapse
|
16
|
Tu D, Goyal MS, Dworkin JD, Kampondeni S, Vidal L, Biondo-Savin E, Juvvadi S, Raghavan P, Nicholas J, Chetcuti K, Clark K, Robert-Fitzgerald T, Satterthwaite TD, Yushkevich P, Davatzikos C, Erus G, Tustison NJ, Postels DG, Taylor TE, Small DS, Shinohara RT. Automated analysis of low-field brain MRI in cerebral malaria. Biometrics 2023; 79:2417-2429. [PMID: 35731973 PMCID: PMC10267853 DOI: 10.1111/biom.13708] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Accepted: 06/08/2022] [Indexed: 11/26/2022]
Abstract
A central challenge of medical imaging studies is to extract biomarkers that characterize disease pathology or outcomes. Modern automated approaches have found tremendous success in high-resolution, high-quality magnetic resonance images. These methods, however, may not translate to low-resolution images acquired on magnetic resonance imaging (MRI) scanners with lower magnetic field strength. In low-resource settings where low-field scanners are more common and there is a shortage of radiologists to manually interpret MRI scans, it is critical to develop automated methods that can augment or replace manual interpretation, while accommodating reduced image quality. We present a fully automated framework for translating radiological diagnostic criteria into image-based biomarkers, inspired by a project in which children with cerebral malaria (CM) were imaged using low-field 0.35 Tesla MRI. We integrate multiatlas label fusion, which leverages high-resolution images from another sample as prior spatial information, with parametric Gaussian hidden Markov models based on image intensities, to create a robust method for determining ventricular cerebrospinal fluid volume. We also propose normalized image intensity and texture measurements to determine the loss of gray-to-white matter tissue differentiation and sulcal effacement. These integrated biomarkers have excellent classification performance for determining severe brain swelling due to CM.
Collapse
Affiliation(s)
- Danni Tu
- The Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania
| | - Manu S. Goyal
- Mallinckrodt Institute of Radiology, Washington University in St. Louis
| | | | | | - Lorenna Vidal
- Department of Radiology, Children’s Hospital of Philadelphia
| | | | | | - Prashant Raghavan
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine
| | - Jennifer Nicholas
- University Hospitals Cleveland Medical Center, Department of Radiology, Case Western Reserve University
| | - Karen Chetcuti
- Department of Paediatrics and Child Health, Kamuzu University of Health Sciences
| | - Kelly Clark
- The Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania
| | - Timothy Robert-Fitzgerald
- The Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania
| | | | | | | | - Guray Erus
- Center for Biomedical Image Computing and Analysis (CBICA), Department of Radiology, University of Pennsylvania
| | | | - Douglas G. Postels
- Division of Neurology, George Washington University/Children’s National Medical Center
| | - Terrie E. Taylor
- Blantyre Malaria Project, Kamuzu University of Health Sciences
- College of Osteopathic Medicine, Michigan State University
| | | | - Russell T. Shinohara
- The Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania
- Center for Biomedical Image Computing and Analysis (CBICA), Department of Radiology, University of Pennsylvania
| |
Collapse
|
17
|
Beydoun MA, Noren Hooten N, Beydoun HA, Weiss J, Maldonado AI, Katzel LI, Davatzikos C, Gullapalli RP, Seliger SL, Erus G, Evans MK, Zonderman AB, Waldstein SR. Plasma neurofilament light and brain volumetric outcomes among middle-aged urban adults. Neurobiol Aging 2023; 129:28-40. [PMID: 37257406 PMCID: PMC10524231 DOI: 10.1016/j.neurobiolaging.2023.04.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/05/2023] [Accepted: 04/30/2023] [Indexed: 06/02/2023]
Abstract
Elevated plasma neurofilament light chain (NfL) is associated with dementia though underlying mechanisms remain unknown. We examined cross-sectional relationships of time-dependent plasma NfL with selected brain structural magnetic resonance imaging (sMRI) prognostic markers of dementia. The sample was drawn from the Healthy Aging in Neighborhoods of Diversity Across the Life Span (HANDLS) study, selecting participants with complete v1 (2004-2009) and v2 (2009-2013) plasma NfL exposure and ancillary sMRI data at vscan (2011-2015, n = 179, mean v1 to vscan time: 5.4 years). Multivariable-adjusted linear regression models were conducted, overall, by sex, and race, correcting for multiple testing with q-values. NfL(v1) was associated with larger WMLV (both Loge transformed), after 5-6 years' follow-up, overall (β = +2.131 ± 0.660, b = +0.29, p = 0.001, and q = 0.0029) and among females. NfLv2 was linked to a 125 mm3 lower left hippocampal volume (p = 0.004 and q = 0.015) in reduced models, mainly among males, as was observed for annualized longitudinal change in NfL (δNfLbayes). Among African American adults, NfLv1 was inversely related to total, gray and white matter volumes. Plasma NfL may reflect future brain pathologies in middle-aged adults.
Collapse
Affiliation(s)
- May A Beydoun
- Laboratory of Epidemiology and Population Sciences, NIA/NIH/IRP, Baltimore, MD, USA.
| | - Nicole Noren Hooten
- Laboratory of Epidemiology and Population Sciences, NIA/NIH/IRP, Baltimore, MD, USA
| | - Hind A Beydoun
- Department of Research Programs, Fort Belvoir Community Hospital, Fort Belvoir, VA, USA
| | - Jordan Weiss
- Department of Demography, University of California Berkeley, Berkeley, CA, USA
| | - Ana I Maldonado
- Laboratory of Epidemiology and Population Sciences, NIA/NIH/IRP, Baltimore, MD, USA; Department of Psychology, University of Maryland, Baltimore County, Catonsville, MD, USA
| | - Leslie I Katzel
- Geriatric Research Education and Clinical Center, Baltimore VA Medical Center, Baltimore, MD, USA; Division of Gerontology, Geriatrics and Palliative Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rao P Gullapalli
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Stephen L Seliger
- Division of Nephrology, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michele K Evans
- Laboratory of Epidemiology and Population Sciences, NIA/NIH/IRP, Baltimore, MD, USA
| | - Alan B Zonderman
- Laboratory of Epidemiology and Population Sciences, NIA/NIH/IRP, Baltimore, MD, USA
| | - Shari R Waldstein
- Department of Psychology, University of Maryland, Baltimore County, Catonsville, MD, USA; Geriatric Research Education and Clinical Center, Baltimore VA Medical Center, Baltimore, MD, USA; Division of Gerontology, Geriatrics and Palliative Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| |
Collapse
|
18
|
Tian Q, Mitchell BA, Erus G, Davatzikos C, Moaddel R, Resnick SM, Ferrucci L. Sex differences in plasma lipid profiles of accelerated brain aging. Neurobiol Aging 2023; 129:178-184. [PMID: 37336172 PMCID: PMC10527719 DOI: 10.1016/j.neurobiolaging.2023.05.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 05/18/2023] [Accepted: 05/23/2023] [Indexed: 06/21/2023]
Abstract
Lipids are essential components of brain structure and shown to affect brain function. Previous studies have shown that aging men undergo greater brain atrophy than women, but whether the associations between lipids and brain atrophy differ by sex is unclear. We examined sex differences in the associations between circulating lipids by liquid chromatography-tandem mass spectrometry and the progression of MRI-derived brain atrophy index Spatial Patterns of Atrophy for Recognition of Brain Aging (SPARE-BA) over an average of 4.7 (SD = 2.3) years in 214 men and 261 women aged 60 or older who were initially cognitively normal using multivariable linear regression, adjusted for age, race, education, and baseline SPARE-BA. We found significant sex interactions for beta-oxidation rate, short-chain acylcarnitines, long-chain ceramides, and very long-chain triglycerides. Lower beta-oxidation rate and short-chain acylcarnitines in women and higher long-chain ceramides and very long-chain triglycerides in men were associated with faster increases in SPARE-BA (accelerated brain aging). Circulating lipid profiles of accelerated brain aging are sex-specific and vary by lipid classes and structure. Mechanisms underlying these sex-specific lipid profiles of brain aging warrant further investigation.
Collapse
Affiliation(s)
- Qu Tian
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA.
| | - Brendan A Mitchell
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
| | - Guray Erus
- Radiology Department, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Radiology Department, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ruin Moaddel
- Laboratory of Clinical Investigation, National Institute on Aging, Baltimore, MD, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Luigi Ferrucci
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
| |
Collapse
|
19
|
Wen J, Skampardoni I, Tian YE, Yang Z, Cui Y, Erus G, Hwang G, Varol E, Boquet-Pujadas A, Chand GB, Nasrallah I, Satterthwaite T, Shou H, Shen L, Toga AW, Zaleskey A, Davatzikos C. Neuroimaging-AI Endophenotypes of Brain Diseases in the General Population: Towards a Dimensional System of Vulnerability. medRxiv 2023:2023.08.16.23294179. [PMID: 37662256 PMCID: PMC10473785 DOI: 10.1101/2023.08.16.23294179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Disease heterogeneity poses a significant challenge for precision diagnostics in both clinical and sub-clinical stages. Recent work leveraging artificial intelligence (AI) has offered promise to dissect this heterogeneity by identifying complex intermediate phenotypes - herein called dimensional neuroimaging endophenotypes (DNEs) - which subtype various neurologic and neuropsychiatric diseases. We investigate the presence of nine such DNEs derived from independent yet harmonized studies on Alzheimer's disease (AD1-2)1, autism spectrum disorder (ASD1-3)2, late-life depression (LLD1-2)3, and schizophrenia (SCZ1-2)4, in the general population of 39,178 participants in the UK Biobank study. Phenome-wide associations revealed prominent associations between the nine DNEs and phenotypes related to the brain and other human organ systems. This phenotypic landscape aligns with the SNP-phenotype genome-wide associations, revealing 31 genomic loci associated with the nine DNEs (Bonferroni corrected P-value < 5×10-8/9). The DNEs exhibited significant genetic correlations, colocalization, and causal relationships with multiple human organ systems and chronic diseases. A causal effect (odds ratio=1.25 [1.11, 1.40], P-value=8.72×1-4) was established from AD2, characterized by focal medial temporal lobe atrophy, to AD. The nine DNEs and their polygenic risk scores significantly improved the prediction accuracy for 14 systemic disease categories and mortality. These findings underscore the potential of the nine DNEs to identify individuals at a high risk of developing the four brain diseases during preclinical stages for precision diagnostics. All results are publicly available at: http://labs.loni.usc.edu/medicine/.
Collapse
Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Ioanna Skampardoni
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Ye Ella Tian
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia
| | - Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Gyujoon Hwang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Erdem Varol
- Department of Computer Science and Engineering, New York University, New York, USA
| | | | - Ganesh B. Chand
- Department of Radiology, School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Ilya Nasrallah
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Theodore Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Haochang Shou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | - Arthur W. Toga
- Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Andrew Zaleskey
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| |
Collapse
|
20
|
du Plessis S, Chand GB, Erus G, Phahladira L, Luckhoff HK, Smit R, Asmal L, Wolf DH, Davatzikos C, Emsley R. Two Neuroanatomical Signatures in Schizophrenia: Expression Strengths Over the First 2 Years of Treatment and Their Relationships to Neurodevelopmental Compromise and Antipsychotic Treatment. Schizophr Bull 2023; 49:1067-1077. [PMID: 37043772 PMCID: PMC10318886 DOI: 10.1093/schbul/sbad040] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/14/2023]
Abstract
BACKGROUND AND HYPOTHESIS Two machine learning derived neuroanatomical signatures were recently described. Signature 1 is associated with widespread grey matter volume reductions and signature 2 with larger basal ganglia and internal capsule volumes. We hypothesized that they represent the neurodevelopmental and treatment-responsive components of schizophrenia respectively. STUDY DESIGN We assessed the expression strength trajectories of these signatures and evaluated their relationships with indicators of neurodevelopmental compromise and with antipsychotic treatment effects in 83 previously minimally treated individuals with a first episode of a schizophrenia spectrum disorder who received standardized treatment and underwent comprehensive clinical, cognitive and neuroimaging assessments over 24 months. Ninety-six matched healthy case-controls were included. STUDY RESULTS Linear mixed effect repeated measures models indicated that the patients had stronger expression of signature 1 than controls that remained stable over time and was not related to treatment. Stronger signature 1 expression showed trend associations with lower educational attainment, poorer sensory integration, and worse cognitive performance for working memory, verbal learning and reasoning and problem solving. The most striking finding was that signature 2 expression was similar for patients and controls at baseline but increased significantly with treatment in the patients. Greater increase in signature 2 expression was associated with larger reductions in PANSS total score and increases in BMI and not associated with neurodevelopmental indices. CONCLUSIONS These findings provide supporting evidence for two distinct neuroanatomical signatures representing the neurodevelopmental and treatment-responsive components of schizophrenia.
Collapse
Affiliation(s)
- Stefan du Plessis
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg Campus, Cape Town, South Africa
| | - Ganesh B Chand
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Department of Radiology and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Lebogang Phahladira
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg Campus, Cape Town, South Africa
| | - Hilmar K Luckhoff
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg Campus, Cape Town, South Africa
| | - Retha Smit
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg Campus, Cape Town, South Africa
| | - Laila Asmal
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg Campus, Cape Town, South Africa
| | - Daniel H Wolf
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Robin Emsley
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg Campus, Cape Town, South Africa
| |
Collapse
|
21
|
Serpa M, Doshi J, Joaquim HPG, Vieira ELM, Erus G, Chaim-Avancini TM, Cavallet M, Guglielmi LG, Sallet PC, Talib L, Teixeira AL, van de Bilt MT, McGuire P, Gattaz WF, Davatzikos C, Busatto GF, Zanetti MV. Inflammatory cytokines and white matter microstructure in the acute phase of first-episode psychosis: A longitudinal study. Schizophr Res 2023; 257:5-18. [PMID: 37230043 DOI: 10.1016/j.schres.2023.05.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 01/14/2023] [Accepted: 05/06/2023] [Indexed: 05/27/2023]
Abstract
OBJECTIVES Schizophrenia-related psychosis is associated with abnormalities in white matter (WM) microstructure and structural brain dysconnectivity. However, the pathological process underlying such changes is unknown. We sought to investigate the potential association between peripheral cytokine levels and WM microstructure during the acute phase of first-episode psychosis (FEP) in a cohort of drug-naïve patients. METHODS Twenty-five non-affective FEP patients and 69 healthy controls underwent MRI scanning and blood collection at study entry. After achieving clinical remission, 21 FEP were reassessed; 38 age and biological sex-matched controls also had a second assessment. We measured fractional anisotropy (FA) of selected WM regions-of-interest (ROIs) and plasma levels of four cytokines (IL-6, IL-10, IFN-γ, and TNF-α). RESULTS At baseline (acute psychosis), the FEP group showed reduced FA relative to controls in half the examined ROIs. Within the FEP group, IL-6 levels were negatively correlated with FA values. Longitudinally, patients showed increments of FA in several ROIs affected at baseline, and such changes were associated with reductions in IL-6 levels. CONCLUSIONS A state-dependent process involving an interplay between a pro-inflammatory cytokine and brain WM might be associated with the clinical manifestation of FEP. This association suggests a deleterious effect of IL-6 on WM tracts during the acute phase of psychosis.
Collapse
Affiliation(s)
- Mauricio Serpa
- Laboratory of Psychiatric Neuroimaging (LIM21), Department and Institute of Psychiatry, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, SP, Brazil; Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), Universidade de Sao Paulo, Sao Paulo, SP, Brazil; Laboratory of Neuroscience (LIM27), Department and Institute of Psychiatry, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, SP, Brazil.
| | - Jimit Doshi
- Section of Biomedical Image Analysis (SBIA), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Helena P G Joaquim
- Laboratory of Neuroscience (LIM27), Department and Institute of Psychiatry, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, SP, Brazil
| | - Erica L M Vieira
- Universidade Federal de Minas Gerais, Faculdade de Medicina, Belo Horizonte, MG, Brazil
| | - Guray Erus
- Section of Biomedical Image Analysis (SBIA), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Tiffany M Chaim-Avancini
- Laboratory of Psychiatric Neuroimaging (LIM21), Department and Institute of Psychiatry, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, SP, Brazil; Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), Universidade de Sao Paulo, Sao Paulo, SP, Brazil
| | - Mikael Cavallet
- Laboratory of Psychiatric Neuroimaging (LIM21), Department and Institute of Psychiatry, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, SP, Brazil
| | - Luiza Guilherme Guglielmi
- Laboratory of Immunology, Instituto do Coracao (INCOR), Hospital das Clinicas FMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brazil
| | - Paulo C Sallet
- Laboratory of Neuroscience (LIM27), Department and Institute of Psychiatry, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, SP, Brazil
| | - Leda Talib
- Laboratory of Neuroscience (LIM27), Department and Institute of Psychiatry, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, SP, Brazil
| | - Antonio L Teixeira
- Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA
| | - Martinus T van de Bilt
- Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), Universidade de Sao Paulo, Sao Paulo, SP, Brazil; Laboratory of Neuroscience (LIM27), Department and Institute of Psychiatry, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, SP, Brazil
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; NIHR Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Wagner F Gattaz
- Laboratory of Neuroscience (LIM27), Department and Institute of Psychiatry, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, SP, Brazil
| | - Christos Davatzikos
- Section of Biomedical Image Analysis (SBIA), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Geraldo F Busatto
- Laboratory of Psychiatric Neuroimaging (LIM21), Department and Institute of Psychiatry, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, SP, Brazil; Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), Universidade de Sao Paulo, Sao Paulo, SP, Brazil
| | | |
Collapse
|
22
|
Chao AM, Zhou Y, Erus G, Davatzikos C, Cardel MI, Foster GD, Wadden TA. A randomized controlled trial examining the effects of behavioral weight loss treatment on hippocampal volume and neurocognition. Physiol Behav 2023; 267:114228. [PMID: 37156318 DOI: 10.1016/j.physbeh.2023.114228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 04/20/2023] [Accepted: 05/05/2023] [Indexed: 05/10/2023]
Abstract
BACKGROUND/PURPOSE Obesity in midlife is an established risk factor for dementia. In middle-aged adults, elevated body mass index (BMI) is associated with lower neurocognition and smaller hippocampal volumes. It is unclear whether behavioral weight loss (BWL) can improve neurocognition. The purpose of this study was to evaluate whether BWL, compared to wait list control (WLC), improved hippocampal volume and neurocognition. We also examined if baseline hippocampal volume and neurocognition were associated with weight loss. METHODS We randomly assigned women with obesity (N=61; mean±SD age=41.1±9.9 years; BMI=38.6±6.2 kg/m2; and 50.8% Black) to BWL or WLC. Participants completed assessments at baseline and follow-up including T1-weighted structural magnetic resonance imaging scans and the National Institutes of Health (NIH) Toolbox Cognition Battery. RESULTS The BWL group lost 4.7±4.9% of initial body weight at 16 to 25 weeks, which was significantly more than the WLC group which gained 0.2±3.5% (p<0.001). The BWL and WLC groups did not differ significantly in changes in hippocampal volume or neurocognition (ps>0.05). Baseline hippocampal volume and neurocognition scores were not significantly associated with weight loss (ps>0.05). CONCLUSIONS AND IMPLICATIONS Contrary to our hypothesis, we found no overall benefit of BWL relative to WLC on hippocampal volumes or cognition in young- and middle-aged women. Baseline hippocampal volume and neurocognition were not associated with weight loss.
Collapse
Affiliation(s)
- Ariana M Chao
- University of Pennsylvania School of Nursing, Department of Biobehavioral Health Sciences, Philadelphia, PA, USA; Perelman School of Medicine at the University of Pennsylvania, Department of Psychiatry, Philadelphia, PA, USA.
| | - Yingjie Zhou
- University of Pennsylvania School of Nursing, Department of Biobehavioral Health Sciences, Philadelphia, PA, USA
| | - Guray Erus
- Perelman School of Medicine at the University of Pennsylvania, Department of Psychiatry, Philadelphia, PA, USA; University of Pennsylvania, Center for Biomedical Image Computing and Analytics, Philadelphia, PA, USA
| | - Christos Davatzikos
- University of Pennsylvania, Center for Biomedical Image Computing and Analytics, Philadelphia, PA, USA
| | - Michelle I Cardel
- WW International, Inc., New York, New York, USA; Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, Florida, USA
| | - Gary D Foster
- Perelman School of Medicine at the University of Pennsylvania, Department of Psychiatry, Philadelphia, PA, USA; WW International, Inc., New York, New York, USA
| | - Thomas A Wadden
- Perelman School of Medicine at the University of Pennsylvania, Department of Psychiatry, Philadelphia, PA, USA
| |
Collapse
|
23
|
Shah C, Srinivasan D, Erus G, Kurella Tamura M, Habes M, Detre JA, Haley WE, Lerner AJ, Wright CB, Wright JT, Oparil S, Kritchevsky SB, Punzi HA, Rastogi A, Malhotra R, Still CH, Williamson JD, Bryan RN, Fan Y, Nasrallah IM. Intensive Blood Pressure Management Preserves Functional Connectivity in Patients with Hypertension from the Systolic Blood Pressure Intervention Randomized Trial. AJNR Am J Neuroradiol 2023; 44:582-588. [PMID: 37105682 PMCID: PMC10171386 DOI: 10.3174/ajnr.a7852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 03/19/2023] [Indexed: 04/29/2023]
Abstract
BACKGROUND AND PURPOSE The Systolic Blood Pressure Intervention (SPRINT) randomized trial demonstrated that intensive blood pressure management resulted in slower progression of cerebral white matter hyperintensities, compared with standard therapy. We assessed longitudinal changes in brain functional connectivity to determine whether intensive treatment results in less decline in functional connectivity and how changes in brain functional connectivity relate to changes in brain structure. MATERIALS AND METHODS Five hundred forty-eight participants completed longitudinal brain MR imaging, including resting-state fMRI, during a median follow-up of 3.84 years. Functional brain networks were identified using independent component analysis, and a mean connectivity score was calculated for each network. Longitudinal changes in mean connectivity score were compared between treatment groups using a 2-sample t test, followed by a voxelwise t test. In the full cohort, adjusted linear regression analysis was performed between changes in the mean connectivity score and changes in structural MR imaging metrics. RESULTS Four hundred six participants had longitudinal imaging that passed quality control. The auditory-salience-language network demonstrated a significantly larger decline in the mean connectivity score in the standard treatment group relative to the intensive treatment group (P = .014), with regions of significant difference between treatment groups in the cingulate and right temporal/insular regions. There was no treatment group difference in other networks. Longitudinal changes in mean connectivity score of the default mode network but not the auditory-salience-language network demonstrated a significant correlation with longitudinal changes in white matter hyperintensities (P = .013). CONCLUSIONS Intensive treatment was associated with preservation of functional connectivity of the auditory-salience-language network, while mean network connectivity in other networks was not significantly different between intensive and standard therapy. A longitudinal increase in the white matter hyperintensity burden is associated with a decline in mean connectivity of the default mode network.
Collapse
Affiliation(s)
- C Shah
- From the Department of Radiology (C.S.), Imaging Institute, Cleveland Clinic, Cleveland, Ohio
| | - D Srinivasan
- Department of Radiology (D.S., G.E., J.A.D., R.N.B., Y.F., I.M.N.), University of Pennsylvania, Philadelphia, Pennsylvania
| | - G Erus
- Department of Radiology (D.S., G.E., J.A.D., R.N.B., Y.F., I.M.N.), University of Pennsylvania, Philadelphia, Pennsylvania
| | - M Kurella Tamura
- Division of Nephrology (M.K.T.), Stanford University, and VA Palo Alto Geriatric Research and Education Clinical Center, Palo Alto, California
| | - M Habes
- Biggs Institute, University of Texas San Antonio (M.H.), San Antonio, Texas
| | - J A Detre
- Department of Radiology (D.S., G.E., J.A.D., R.N.B., Y.F., I.M.N.), University of Pennsylvania, Philadelphia, Pennsylvania
| | - W E Haley
- Department of Nephrology and Hypertension (W.E.H.), Mayo Clinic, Jacksonville, Florida
| | | | - C B Wright
- National Institute of Neurological Disorders and Stroke (C.B.W.), National Institutes of Health, Bethesda, Maryland
| | - J T Wright
- Medicine (J.T.W.), Case Western Reserve University, and University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - S Oparil
- Division of Cardiovascular Disease (S.O.), Department of Medicine, University of Alabama, Birmingham, Alabama
| | - S B Kritchevsky
- Section of Gerontology and Geriatric Medicine, Department of Internal Medicine (S.B.K., J.D.W.), Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - H A Punzi
- Punzi Medical Center (H.A.P.), Carrollton, Texas
| | - A Rastogi
- Division of Nephrology (A.R.), Department of Medicine, University of California Los Angeles, Los Angeles, California
| | - R Malhotra
- Division of Nephrology (R.M.), University of California San Diego, San Diego, California
| | - C H Still
- Frances Payne Bolton School of Nursing (C.H.S.), Case Western Reserve University, Cleveland, Ohio
| | - J D Williamson
- Section of Gerontology and Geriatric Medicine, Department of Internal Medicine (S.B.K., J.D.W.), Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - R N Bryan
- Department of Radiology (D.S., G.E., J.A.D., R.N.B., Y.F., I.M.N.), University of Pennsylvania, Philadelphia, Pennsylvania
| | - Y Fan
- Department of Radiology (D.S., G.E., J.A.D., R.N.B., Y.F., I.M.N.), University of Pennsylvania, Philadelphia, Pennsylvania
| | - I M Nasrallah
- Department of Radiology (D.S., G.E., J.A.D., R.N.B., Y.F., I.M.N.), University of Pennsylvania, Philadelphia, Pennsylvania
| |
Collapse
|
24
|
Dwyer DB, Chand GB, Pigoni A, Khuntia A, Wen J, Antoniades M, Hwang G, Erus G, Doshi J, Srinivasan D, Varol E, Kahn RS, Schnack HG, Meisenzahl E, Wood SJ, Zhuo C, Sotiras A, Shinohara RT, Shou H, Fan Y, Schaulfelberger M, Rosa P, Lalousis PA, Upthegrove R, Kaczkurkin AN, Moore TM, Nelson B, Gur RE, Gur RC, Ritchie MD, Satterthwaite TD, Murray RM, Di Forti M, Ciufolini S, Zanetti MV, Wolf DH, Pantelis C, Crespo-Facorro B, Busatto GF, Davatzikos C, Koutsouleris N, Dazzan P. Psychosis brain subtypes validated in first-episode cohorts and related to illness remission: results from the PHENOM consortium. Mol Psychiatry 2023; 28:2008-2017. [PMID: 37147389 PMCID: PMC10575777 DOI: 10.1038/s41380-023-02069-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 03/15/2023] [Accepted: 04/05/2023] [Indexed: 05/07/2023]
Abstract
Using machine learning, we recently decomposed the neuroanatomical heterogeneity of established schizophrenia to discover two volumetric subgroups-a 'lower brain volume' subgroup (SG1) and an 'higher striatal volume' subgroup (SG2) with otherwise normal brain structure. In this study, we investigated whether the MRI signatures of these subgroups were also already present at the time of the first-episode of psychosis (FEP) and whether they were related to clinical presentation and clinical remission over 1-, 3-, and 5-years. We included 572 FEP and 424 healthy controls (HC) from 4 sites (Sao Paulo, Santander, London, Melbourne) of the PHENOM consortium. Our prior MRI subgrouping models (671 participants; USA, Germany, and China) were applied to both FEP and HC. Participants were assigned into 1 of 4 categories: subgroup 1 (SG1), subgroup 2 (SG2), no subgroup membership ('None'), and mixed SG1 + SG2 subgroups ('Mixed'). Voxel-wise analyses characterized SG1 and SG2 subgroups. Supervised machine learning analyses characterized baseline and remission signatures related to SG1 and SG2 membership. The two dominant patterns of 'lower brain volume' in SG1 and 'higher striatal volume' (with otherwise normal neuromorphology) in SG2 were identified already at the first episode of psychosis. SG1 had a significantly higher proportion of FEP (32%) vs. HC (19%) than SG2 (FEP, 21%; HC, 23%). Clinical multivariate signatures separated the SG1 and SG2 subgroups (balanced accuracy = 64%; p < 0.0001), with SG2 showing higher education but also greater positive psychosis symptoms at first presentation, and an association with symptom remission at 1-year, 5-year, and when timepoints were combined. Neuromorphological subtypes of schizophrenia are already evident at illness onset, separated by distinct clinical presentations, and differentially associated with subsequent remission. These results suggest that the subgroups may be underlying risk phenotypes that could be targeted in future treatment trials and are critical to consider when interpreting neuroimaging literature.
Collapse
Affiliation(s)
- Dominic B Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany.
- Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia.
- Orygen, Melbourne, VIC, Australia.
| | - Ganesh B Chand
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Alessandro Pigoni
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
- Social and Affective Neuroscience Group, MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Adyasha Khuntia
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
- Max-Planck Institute of Psychiatry, Munich, Germany
| | - Junhao Wen
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mathilde Antoniades
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Gyujoon Hwang
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jimit Doshi
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dhivya Srinivasan
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Erdem Varol
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Statistics, Zuckerman Institute, Columbia University, New York, NY, USA
| | - Rene S Kahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hugo G Schnack
- Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands
| | - Eva Meisenzahl
- LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany
| | - Stephen J Wood
- Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia
- Orygen, Melbourne, VIC, Australia
- University of Birmingham, Edgbaston, UK
| | - Chuanjun Zhuo
- Department of Psychiatric-Neuroimaging-Genetics and Co-morbidity Laboratory (PNGC-Lab), Nankai University Affiliated Tianjin Anding Hospital; Department of Psychiatry, Tianjin Medical University, Tianjin, China
| | - Aristeidis Sotiras
- Department of Radiology and Institute for Informatics, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Russell T Shinohara
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Haochang Shou
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Pedro Rosa
- Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil
| | - Paris A Lalousis
- Institute for Mental Health and Centre for Brain Health, University of Birmingham, Birmingham, UK
| | - Rachel Upthegrove
- Institute for Mental Health and Centre for Brain Health, University of Birmingham, Birmingham, UK
- Early Intervention Service, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK
| | | | - Tyler M Moore
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Barnaby Nelson
- Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia
- Orygen, Melbourne, VIC, Australia
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Marylyn D Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D Satterthwaite
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Robin M Murray
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Marta Di Forti
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Simone Ciufolini
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Marcus V Zanetti
- Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil
- Hospital Sírio-Libanês, São Paulo, Brazil
| | - Daniel H Wolf
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
| | - Benedicto Crespo-Facorro
- Mental Health Service, Hospital Universitario Virgen del Rocío, Seville, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III (CIBERSAM), Madrid, Spain
- Instituto de Biomedicina de Sevilla (IBiS), Seville, Spain
- Department of Psychiatry, Universidad de Sevilla, Seville, Spain
| | - Geraldo F Busatto
- Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany.
- Max-Planck Institute of Psychiatry, Munich, Germany.
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK.
| | - Paola Dazzan
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK.
| |
Collapse
|
25
|
Hwang G, Wen J, Sotardi S, Brodkin ES, Chand GB, Dwyer DB, Erus G, Doshi J, Singhal P, Srinivasan D, Varol E, Sotiras A, Dazzan P, Kahn RS, Schnack HG, Zanetti MV, Meisenzahl E, Busatto GF, Crespo-Facorro B, Pantelis C, Wood SJ, Zhuo C, Shinohara RT, Shou H, Fan Y, Di Martino A, Koutsouleris N, Gur RE, Gur RC, Satterthwaite TD, Wolf DH, Davatzikos C. Assessment of Neuroanatomical Endophenotypes of Autism Spectrum Disorder and Association With Characteristics of Individuals With Schizophrenia and the General Population. JAMA Psychiatry 2023; 80:498-507. [PMID: 37017948 PMCID: PMC10157419 DOI: 10.1001/jamapsychiatry.2023.0409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/06/2023]
Abstract
Importance Autism spectrum disorder (ASD) is associated with significant clinical, neuroanatomical, and genetic heterogeneity that limits precision diagnostics and treatment. Objective To assess distinct neuroanatomical dimensions of ASD using novel semisupervised machine learning methods and to test whether the dimensions can serve as endophenotypes also in non-ASD populations. Design, Setting, and Participants This cross-sectional study used imaging data from the publicly available Autism Brain Imaging Data Exchange (ABIDE) repositories as the discovery cohort. The ABIDE sample included individuals diagnosed with ASD aged between 16 and 64 years and age- and sex-match typically developing individuals. Validation cohorts included individuals with schizophrenia from the Psychosis Heterogeneity Evaluated via Dimensional Neuroimaging (PHENOM) consortium and individuals from the UK Biobank to represent the general population. The multisite discovery cohort included 16 internationally distributed imaging sites. Analyses were performed between March 2021 and March 2022. Main Outcomes and Measures The trained semisupervised heterogeneity through discriminative analysis models were tested for reproducibility using extensive cross-validations. It was then applied to individuals from the PHENOM and the UK Biobank. It was hypothesized that neuroanatomical dimensions of ASD would display distinct clinical and genetic profiles and would be prominent also in non-ASD populations. Results Heterogeneity through discriminative analysis models trained on T1-weighted brain magnetic resonance images of 307 individuals with ASD (mean [SD] age, 25.4 [9.8] years; 273 [88.9%] male) and 362 typically developing control individuals (mean [SD] age, 25.8 [8.9] years; 309 [85.4%] male) revealed that a 3-dimensional scheme was optimal to capture the ASD neuroanatomy. The first dimension (A1: aginglike) was associated with smaller brain volume, lower cognitive function, and aging-related genetic variants (FOXO3; Z = 4.65; P = 1.62 × 10-6). The second dimension (A2: schizophrenialike) was characterized by enlarged subcortical volumes, antipsychotic medication use (Cohen d = 0.65; false discovery rate-adjusted P = .048), partially overlapping genetic, neuroanatomical characteristics to schizophrenia (n = 307), and significant genetic heritability estimates in the general population (n = 14 786; mean [SD] h2, 0.71 [0.04]; P < 1 × 10-4). The third dimension (A3: typical ASD) was distinguished by enlarged cortical volumes, high nonverbal cognitive performance, and biological pathways implicating brain development and abnormal apoptosis (mean [SD] β, 0.83 [0.02]; P = 4.22 × 10-6). Conclusions and Relevance This cross-sectional study discovered 3-dimensional endophenotypic representation that may elucidate the heterogeneous neurobiological underpinnings of ASD to support precision diagnostics. The significant correspondence between A2 and schizophrenia indicates a possibility of identifying common biological mechanisms across the 2 mental health diagnoses.
Collapse
Affiliation(s)
- Gyujoon Hwang
- AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Junhao Wen
- AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Laboratory of AI & Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey
| | - Susan Sotardi
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Edward S Brodkin
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Ganesh B Chand
- AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Department of Radiology, School of Medicine, Washington University in St Louis, St Louis, Missouri
| | - Dominic B Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Guray Erus
- AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Jimit Doshi
- AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Pankhuri Singhal
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Dhivya Srinivasan
- AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Erdem Varol
- AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Department of Statistics, Zuckerman Institute, Columbia University, New York, New York
| | - Aristeidis Sotiras
- AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Department of Radiology, School of Medicine, Washington University in St Louis, St Louis, Missouri
| | - Paola Dazzan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Rene S Kahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Hugo G Schnack
- Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands
| | - Marcus V Zanetti
- Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil
- Hospital Sírio-Libanês, São Paulo, Brazil
| | - Eva Meisenzahl
- LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany
| | - Geraldo F Busatto
- Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil
| | - Benedicto Crespo-Facorro
- University Hospital Virgen del Rocio, Department of Psychiatry, School of Medicine, IBiS-CIBERSAM, University of Sevilla, Seville, Spain
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Victoria, Australia
| | - Stephen J Wood
- Orygen, Melbourne, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
- School of Psychology, University of Birmingham, Edgbaston, UK
| | - Chuanjun Zhuo
- Department of Psychiatric-Neuroimaging-Genetics and Co-morbidity Laboratory, Tianjin Anding Hospital, Tianjin, China
- Department of Psychiatry, Tianjin Medical University, Tianjin, China
| | - Russell T Shinohara
- AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Haochang Shou
- AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Yong Fan
- AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Adriana Di Martino
- Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience at the New York University Child Study Center, New York
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Theodore D Satterthwaite
- AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Daniel H Wolf
- AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Christos Davatzikos
- AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| |
Collapse
|
26
|
Zhou Z, Li H, Srinivasan D, Abdulkadir A, Nasrallah IM, Wen J, Doshi J, Erus G, Mamourian E, Bryan NR, Wolk DA, Beason-Held L, Resnick SM, Satterthwaite TD, Davatzikos C, Shou H, Fan Y. Multiscale functional connectivity patterns of the aging brain learned from harmonized rsfMRI data of the multi-cohort iSTAGING study. Neuroimage 2023; 269:119911. [PMID: 36731813 PMCID: PMC9992322 DOI: 10.1016/j.neuroimage.2023.119911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 01/06/2023] [Accepted: 01/28/2023] [Indexed: 02/03/2023] Open
Abstract
To learn multiscale functional connectivity patterns of the aging brain, we built a brain age prediction model of functional connectivity measures at seven scales on a large fMRI dataset, consisting of resting-state fMRI scans of 4186 individuals with a wide age range (22 to 97 years, with an average of 63) from five cohorts. We computed multiscale functional connectivity measures of individual subjects using a personalized functional network computational method, harmonized the functional connectivity measures of subjects from multiple datasets in order to build a functional brain age model, and finally evaluated how functional brain age gap correlated with cognitive measures of individual subjects. Our study has revealed that functional connectivity measures at multiple scales were more informative than those at any single scale for the brain age prediction, the data harmonization significantly improved the brain age prediction performance, and the data harmonization in the functional connectivity measures' tangent space worked better than in their original space. Moreover, brain age gap scores of individual subjects derived from the brain age prediction model were significantly correlated with clinical and cognitive measures. Overall, these results demonstrated that multiscale functional connectivity patterns learned from a large-scale multi-site rsfMRI dataset were informative for characterizing the aging brain and the derived brain age gap was associated with cognitive and clinical measures.
Collapse
Affiliation(s)
- Zhen Zhou
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| | - Hongming Li
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Dhivya Srinivasan
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ahmed Abdulkadir
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ilya M Nasrallah
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Junhao Wen
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jimit Doshi
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Elizabeth Mamourian
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Nick R Bryan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Diagnostic Medicine, University of Texas at Austin, Austin, TX, 78705, USA
| | - David A Wolk
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Neurology and Penn Memory Center, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Lori Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, 20892, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, 20892, USA
| | - Theodore D Satterthwaite
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn Statistic in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Psychiatry, Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Psychiatry, Brain Behavior Laboratory and Penn-CHOP Lifespan Brain Institute, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Haochang Shou
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn Statistic in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| |
Collapse
|
27
|
Cooper CP, Shafer AT, Armstrong NM, An Y, Erus G, Davatzikos C, Ferrucci L, Rapp PR, Resnick SM. Associations of baseline and longitudinal change in cerebellum volume with age-related changes in verbal learning and memory. Neuroimage 2023; 272:120048. [PMID: 36958620 DOI: 10.1016/j.neuroimage.2023.120048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 03/17/2023] [Accepted: 03/20/2023] [Indexed: 03/25/2023] Open
Abstract
The cerebellum is involved in higher-order cognitive functions, e.g., learning and memory, and is susceptible to age-related atrophy. Yet, the cerebellum's role in age-related cognitive decline remains largely unknown. We investigated cross-sectional and longitudinal associations between cerebellar volume and verbal learning and memory. Linear mixed effects models and partial correlations were used to examine the relationship between changes in cerebellum volumes (total cerebellum, cerebellum white matter [WM], cerebellum hemisphere gray matter [GM], and cerebellum vermis subregions) and changes in verbal learning and memory performance among 549 Baltimore Longitudinal Study of Aging participants (2,292 visits). All models were adjusted by baseline demographic characteristics (age, sex, race, education), and APOE e4 carrier status. In examining associations between change with change, we tested an additional model that included either hippocampal (HC), cuneus, or postcentral gyrus (PoCG) volumes to assess whether cerebellar volumes were uniquely associated with verbal learning and memory. Cross-sectionally, the association of baseline cerebellum GM and WM with baseline verbal learning and memory was age-dependent, with the oldest individuals showing the strongest association between volume and performance. Baseline volume was not significantly associated with change in learning and memory. However, analysis of associations between change in volumes and changes in verbal learning and memory showed that greater declines in verbal memory were associated with greater volume loss in cerebellum white matter, and preserved GM volume in cerebellum vermis lobules VI-VII. The association between decline in verbal memory and decline in cerebellar WM volume remained after adjustment for HC, cuneus, and PoCG volume. Our findings highlight that associations between cerebellum volume and verbal learning and memory are age-dependent and regionally specific.
Collapse
Affiliation(s)
- C'iana P Cooper
- Neurocognitive Aging Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland
| | - Andrea T Shafer
- Brain Aging and Behavior Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland
| | - Nicole M Armstrong
- Brain Aging and Behavior Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland; Department of Psychiatry and Human Behavior, Warren Alpert Medical School, Brown University, Providence, Rhode Island
| | - Yang An
- Brain Aging and Behavior Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland
| | - Guray Erus
- Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Christos Davatzikos
- Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Luigi Ferrucci
- Translational Gerontology Branch, Longitudinal Studies Section, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Peter R Rapp
- Neurocognitive Aging Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland
| | - Susan M Resnick
- Brain Aging and Behavior Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland.
| |
Collapse
|
28
|
Jirsaraie RJ, Kaufmann T, Bashyam V, Erus G, Luby JL, Westlye LT, Davatzikos C, Barch DM, Sotiras A. Benchmarking the generalizability of brain age models: Challenges posed by scanner variance and prediction bias. Hum Brain Mapp 2023; 44:1118-1128. [PMID: 36346213 PMCID: PMC9875922 DOI: 10.1002/hbm.26144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 10/01/2022] [Accepted: 10/24/2022] [Indexed: 11/11/2022] Open
Abstract
Machine learning has been increasingly applied to neuroimaging data to predict age, deriving a personalized biomarker with potential clinical applications. The scientific and clinical value of these models depends on their applicability to independently acquired scans from diverse sources. Accordingly, we evaluated the generalizability of two brain age models that were trained across the lifespan by applying them to three distinct early-life samples with participants aged 8-22 years. These models were chosen based on the size and diversity of their training data, but they also differed greatly in their processing methods and predictive algorithms. Specifically, one brain age model was built by applying gradient tree boosting (GTB) to extracted features of cortical thickness, surface area, and brain volume. The other model applied a 2D convolutional neural network (DBN) to minimally preprocessed slices of T1-weighted scans. Additional model variants were created to understand how generalizability changed when each model was trained with data that became more similar to the test samples in terms of age and acquisition protocols. Our results illustrated numerous trade-offs. The GTB predictions were relatively more accurate overall and yielded more reliable predictions when applied to lower quality scans. In contrast, the DBN displayed the most utility in detecting associations between brain age gaps and cognitive functioning. Broadly speaking, the largest limitations affecting generalizability were acquisition protocol differences and biased brain age estimates. If such confounds could eventually be removed without post-hoc corrections, brain age predictions may have greater utility as personalized biomarkers of healthy aging.
Collapse
Affiliation(s)
- Robert J Jirsaraie
- Division of Computational and Data Sciences, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Tobias Kaufmann
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany.,Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Vishnu Bashyam
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Joan L Luby
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Lars T Westlye
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Psychology, University of Oslo, Oslo, Norway.,KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Deanna M Barch
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Aristeidis Sotiras
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| |
Collapse
|
29
|
Yang Z, Wen J, Abdulkadir A, Cui Y, Erus G, Mamourian E, Melhem R, Srinivasan D, Govindarajan ST, Chen J, Habes M, Masters CL, Maruff P, Fripp J, Ferrucci L, Albert MS, Johnson SC, Morris JC, LaMontagne P, Marcus DS, Benzinger TLS, Wolk DA, Shen L, Bao J, Resnick SM, Shou H, Nasrallah IM, Davatzikos C. Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering. ArXiv 2023:arXiv:2301.10772v1. [PMID: 36748000 PMCID: PMC9900969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and SNP data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-driven neuroimaging phenotypes.
Collapse
|
30
|
Fu CHY, Erus G, Fan Y, Antoniades M, Arnone D, Arnott SR, Chen T, Choi KS, Fatt CC, Frey BN, Frokjaer VG, Ganz M, Garcia J, Godlewska BR, Hassel S, Ho K, McIntosh AM, Qin K, Rotzinger S, Sacchet MD, Savitz J, Shou H, Singh A, Stolicyn A, Strigo I, Strother SC, Tosun D, Victor TA, Wei D, Wise T, Woodham RD, Zahn R, Anderson IM, Deakin JFW, Dunlop BW, Elliott R, Gong Q, Gotlib IH, Harmer CJ, Kennedy SH, Knudsen GM, Mayberg HS, Paulus MP, Qiu J, Trivedi MH, Whalley HC, Yan CG, Young AH, Davatzikos C. AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale. BMC Psychiatry 2023; 23:59. [PMID: 36690972 PMCID: PMC9869598 DOI: 10.1186/s12888-022-04509-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 12/29/2022] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. METHODS We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. RESULTS We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. CONCLUSION We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.
Collapse
Affiliation(s)
- Cynthia H Y Fu
- Department of Psychological Sciences, University of East London, London, UK.
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK.
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Mathilde Antoniades
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Danilo Arnone
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
- Department of Psychiatry and Behavioral Science, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | | | - Taolin Chen
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Ki Sueng Choi
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Cherise Chin Fatt
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, USA
| | - Benicio N Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Canada
- Mood Disorders Treatment and Research Centre and Women's Health Concerns Clinic, St Joseph's Healthcare Hamilton, Hamilton, Canada
| | - Vibe G Frokjaer
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Psychiatry, Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Melanie Ganz
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Jose Garcia
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Beata R Godlewska
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK
| | - Stefanie Hassel
- Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, Canada
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Keith Ho
- Department of Psychiatry, University Health Network, Toronto, Canada
| | - Andrew M McIntosh
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Kun Qin
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Susan Rotzinger
- Department of Psychiatry, University Health Network, Toronto, Canada
- Centre for Depression and Suicide Studies, Unity Health Toronto, Toronto, Canada
| | - Matthew D Sacchet
- Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | | | - Haochang Shou
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) Center, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA
| | - Ashish Singh
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Aleks Stolicyn
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Irina Strigo
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, USA
| | - Stephen C Strother
- Rotman Research Institute, Baycrest Centre, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, USA
| | | | - Dongtao Wei
- School of Psychology, Southwest University, Chongqing, China
| | - Toby Wise
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Rachel D Woodham
- Department of Psychological Sciences, University of East London, London, UK
| | - Roland Zahn
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Ian M Anderson
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - J F William Deakin
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - Boadie W Dunlop
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, USA
| | - Rebecca Elliott
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - Qiyong Gong
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Ian H Gotlib
- Department of Psychology, Stanford University, Stanford, USA
| | | | - Sidney H Kennedy
- Department of Psychiatry, University Health Network, Toronto, Canada
- Centre for Depression and Suicide Studies, Unity Health Toronto, Toronto, Canada
- Unity Health Toronto, Toronto, Canada
| | - Gitte M Knudsen
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Helen S Mayberg
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, USA
| | | | - Jiang Qiu
- School of Psychology, Southwest University, Chongqing, China
| | - Madhukar H Trivedi
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, USA
| | - Heather C Whalley
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
| | - Allan H Young
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
- South London and Maudsley NHS Foundation Trust, Bethlem Royal Hospital, London, UK
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| |
Collapse
|
31
|
Beydoun MA, Noren Hooten N, Weiss J, Maldonado AI, Beydoun HA, Katzel LI, Davatzikos C, Gullapalli RP, Seliger SL, Erus G, Evans MK, Zonderman AB, Waldstein SR. Plasma neurofilament light as blood marker for poor brain white matter integrity among middle-aged urban adults. Neurobiol Aging 2023; 121:52-63. [PMID: 36371816 PMCID: PMC9733693 DOI: 10.1016/j.neurobiolaging.2022.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 10/05/2022] [Accepted: 10/10/2022] [Indexed: 11/06/2022]
Abstract
Plasma neurofilament light chain (NfL)'s link to dementia may be mediated through white matter integrity (WMI). In this study, we examined plasma NfL's relationships with diffusion tensor magnetic resonance imaging markers: global and cortical white matter fractional anisotropy (FA) and trace (TR). Plasma NfL measurements at 2 times (v1: 2004-2009 and v2: 2009-2013) and ancillary dMRI (vscan: 2011-2015) were considered (n = 163, mean time v1 to vscan = 5.4 years and v2 to vscan: 1.1 years). Multivariable-adjusted regression models, correcting for multiple-testing revealed that, overall, higher NfLv1 was associated with greater global TR (β ± SE: +0.0000560 ± 0.0000186, b = 0.27, p = 0.003, q = 0.012), left frontal WM TR (β ± SE: + 0.0000706 ± 0.0000201, b ± 0.30, p = 0.001, q = 0.0093) and right frontal WM TR (β ± SE: + 0.0000767 ± 0.000021, b ± 0.31, p < 0.001, q = 0.0093). These associations were mainly among males and White adults. Among African American adults only, NfLv2 was associated with greater left temporal lobe TR. "Tracking high" in NfL was associated with reduced left frontal FA (Model 2, body mass index-adjusted: β ± SE:-0.01084 ± 0.00408, p = 0.009). Plasma NfL is a promising biomarker predicting future brain white matter integrity (WMI) in middle-aged adults.
Collapse
Affiliation(s)
- May A Beydoun
- Laboratory of Epidemiology and Population Sciences, Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
| | - Nicole Noren Hooten
- Laboratory of Epidemiology and Population Sciences, Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Jordan Weiss
- Stanford Center on Longevity, Stanford University, Stanford, CA USA
| | - Ana I Maldonado
- Laboratory of Epidemiology and Population Sciences, Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA; Department of Psychology, University of Maryland, Catonsville, MD, USA
| | - Hind A Beydoun
- Department of Research Programs, Fort Belvoir Community Hospital, Fort Belvoir, VA, USA
| | - Leslie I Katzel
- Division of Gerontology, Geriatrics, and Palliative Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA; Division of Gerontology & Geriatric Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rao P Gullapalli
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Stephen L Seliger
- Division of Nephrology, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michele K Evans
- Laboratory of Epidemiology and Population Sciences, Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Alan B Zonderman
- Laboratory of Epidemiology and Population Sciences, Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Shari R Waldstein
- Department of Psychology, University of Maryland, Catonsville, MD, USA; Division of Gerontology, Geriatrics, and Palliative Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA; Division of Gerontology & Geriatric Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| |
Collapse
|
32
|
Dintica CS, Habes M, Erus G, Simone T, Schreiner P, Yaffe K. Long-term depressive symptoms and midlife brain age. J Affect Disord 2023; 320:436-441. [PMID: 36202300 PMCID: PMC10115134 DOI: 10.1016/j.jad.2022.09.164] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 09/26/2022] [Accepted: 09/30/2022] [Indexed: 11/05/2022]
Abstract
BACKGROUND Evidence suggests that depression may be a risk factor for dementia in older adults, but the link between depressive symptoms and brain health earlier in life is less understood. Our aim was to investigate the association between long-term depressive symptoms in young to mid-adulthood and a measure of brain age derived from structural MRI. METHODS From the Coronary Artery Risk Development in Young Adults study, we identified 649 participants (age 23-36 at baseline) with brain MRI and cognitive testing. Long-term depressive symptoms were measured with the Center for Epidemiological Studies Depression scale (CESD) six times across 25 years and analyzed as time-weighted averages (TWA). Brain age was derived using previously validated high dimensional neuroimaging pattern analysis, quantifying individual differences in age-related atrophy. Elevated depressive symptoms were defined as CES-D ≥16. Linear regression was used to test the association between TWA depressive symptoms, brain aging, and cognition. RESULTS Each standard deviation (5-points) increment in TWA depression symptoms over 25 years was associated with one-year greater brain age (β: 1.14, 95 % confidence interval [CI]: 0.57 to 1.71). Participants with elevated TWA depressive symptoms had on average a 3-year greater brain age (β: 2.75, 95 % CI: 0.43 to 5.08). Moreover, elevated depressive symptoms were associated with higher odds of poor cognitive function in midlife (OR: 3.30, 95 % CI: 1.37 to 7.97). LIMITATIONS Brain age was assessed at one time, limiting our ability to evaluate the temporality of depressive symptoms and brain aging. CONCLUSIONS Elevated depressive symptoms in early adulthood may have implications for brain health as early as in midlife.
Collapse
Affiliation(s)
| | - Mohamad Habes
- University of Pennsylvania, Philadelphia, PA, USA; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC), Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), San Antonio, TX, USA.
| | - Guray Erus
- University of Pennsylvania, Philadelphia, PA, USA.
| | - Tamar Simone
- Northern California Institute for Research and Education, San Francisco, CA, USA.
| | - Pamela Schreiner
- Division of Epidemiology & Community Health, University of Minnesota, Minneapolis, MN, USA.
| | - Kristine Yaffe
- University of California, San Francisco, California, CA, USA; VA Medical Center, San Francisco, CA, USA.
| |
Collapse
|
33
|
Tian Q, Mitchell B, Erus G, Davatzikos C, Resnick S, Ferrucci L. METABOLOMIC SIGNATURES OF BRAIN ATROPHY PATTERNS IN AGING AND ALZHEIMER'S DISEASE. Innov Aging 2022. [PMCID: PMC9765778 DOI: 10.1093/geroni/igac059.1233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Can plasma metabolomics reveal mechanisms of brain aging? We investigated metabolomic signatures of brain atrophy patterns related to cognitive decline and Alzheimer’s disease(AD) risk. Relationships between metabolomics(Biocrates-p500) and annual rates of change in two neuroimaging-based brain atrophy patterns(SPARE-BA indexing brain aging, SPARE-AD indexing AD-related atrophy) were examined using multivariable linear regression in 477 Baltimore Longitudinal Study of Aging participants aged 60+, adjusted for demographic variables and BMI. Higher concentrations of sarcosine, triglycerides, diglycerides, and ceramides and lower concentrations from phosphatidylcholines and cholesteryl ester were associated with faster rates of SPARE-BA increase longitudinally. Higher concentrations of diglyceride and alpha-amino-butyric acid and lower concentrations of tryptophan, hippuric acid, cholesteryl ester, phosphatidylcholine and sphingomyelin were associated with faster rates of SPARE-AD. Metabolites have differential associations with age-related and AD-related brain atrophy patterns, which may provide new insights into preventive and therapeutic interventions. Future studies should examine whether metabolite changes precede brain atrophy patterns.
Collapse
Affiliation(s)
- Qu Tian
- National Institutes on Aging, Baltimore, Maryland, United States
| | | | - Guray Erus
- University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | | | - Susan Resnick
- National Institute on Aging, Baltimore, Maryland, United States
| | - Luigi Ferrucci
- National Institute on Aging, Baltimore, Maryland, United States
| |
Collapse
|
34
|
de Resende EDPF, Xia F, Sidney S, Launer LJ, Schreiner PJ, Erus G, Bryan N, Yaffe K. Higher literacy is associated with better white matter integrity and cognition in middle age. Alzheimers Dement (Amst) 2022; 14:e12363. [PMID: 36514538 PMCID: PMC9732896 DOI: 10.1002/dad2.12363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 08/29/2022] [Accepted: 09/16/2022] [Indexed: 12/13/2022]
Abstract
Introduction Literacy can be a better measure of quality of education. Its association with brain health in midlife has not been thoroughly investigated. Methods We studied, cross-sectionally, 616 middle-aged adults (mean age of 55.1 ± 3.6 years, 53% female and 38% Black) from the Coronary Artery Risk Development in Young Adults (CARDIA) study. We correlated literacy with cognitive tests, gray matter volumes, and fractional anisotropy (FA) values (indirect measures of white matter integrity) using linear regression. Results The higher-literacy group (n = 499) performed better than the low-literacy group (n = 117) on all cognitive tests. There was no association between literacy and gray matter volumes. The higher-literacy group had greater total-brain FA and higher temporal, parietal, and occipital FA values after multivariable adjustments. Discussion Higher literacy is associated with higher white matter integrity as well as with better cognitive performance in middle-aged adults. These results highlight the importance of focusing on midlife interventions to improve literacy skills.
Collapse
Affiliation(s)
| | - Feng Xia
- Northern California Institute for ResearchSan FranciscoCaliforniaUSA
| | - Stephen Sidney
- Kaiser Permanente Division of ResearchOaklandCaliforniaUSA
| | | | - Pamela J. Schreiner
- Division of Epidemiology and Community HealthUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Guray Erus
- Department of RadiologyPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Nick Bryan
- Department of RadiologyPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Kristine Yaffe
- Global Brain Health InstituteSan Francisco and DublinUSA and Ireland
- Northern California Institute for ResearchSan FranciscoCaliforniaUSA
- Departments of PsychiatryNeurology, and Epidemiology and BiostatisticsUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| |
Collapse
|
35
|
Moonen JE, Nasrallah IM, Detre JA, Dolui S, Erus G, Davatzikos C, Meirelles O, Bryan NR, Launer LJ. Race, sex, and mid-life changes in brain health: Cardia MRI substudy. Alzheimers Dement 2022; 18:2428-2437. [PMID: 35142033 PMCID: PMC9360196 DOI: 10.1002/alz.12560] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 09/20/2021] [Accepted: 12/03/2021] [Indexed: 01/31/2023]
Abstract
OBJECTIVE To examine longitudinal race and sex differences in mid-life brain health and to evaluate whether cardiovascular health (CVH) or apolipoprotein E (APOE) ε4 explain differences. METHODS The study included 478 Black and White participants (mean age: 50 years). Total (TBV), gray (GMV), white (WMV), and white matter hyperintensity (WMH) volumes and GM-cerebral blood flow (CBF) were acquired with 3T-magnetic resonance imaging at baseline and 5-year follow-up. Analyses were based on general linear models. RESULTS There were race x sex interactions for GMV (P-interaction = .004) and CBF (P-interaction = .01) such that men showed more decline than women, and this was most evident in Blacks. Blacks compared to Whites had a significantly greater increase in WMH (P = .002). All sex-race differences in change were marginally attenuated by CVH and APOE ε4. CONCLUSION Race-sex differences in brain health emerge by mid-life. Identifying new environmental factors beyond CVH is needed to develop early interventions to maintain brain health.
Collapse
Affiliation(s)
- Justine E Moonen
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institute of Health, LEPS/IRP/NIA/NIH, 251 Bayview Blvd, Suite 100, Baltimore, MD 21224, USA, Tel: 410-558-8292
| | - Ilya M Nasrallah
- Department of Radiology, University of Pennsylvania Health System, Philadelphia, PA 19104, US
| | - John A Detre
- Department of Radiology, University of Pennsylvania Health System, Philadelphia, PA 19104, US
| | - Sudipto Dolui
- Department of Radiology, University of Pennsylvania Health System, Philadelphia, PA 19104, US
| | - Guray Erus
- Department of Radiology, University of Pennsylvania Health System, Philadelphia, PA 19104, US
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School for advanced Medicine, 3400 Civic Center Boulevard Atrium, Ground Floor, Philadelphia, PA 19104, US
| | - Osorio Meirelles
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institute of Health, LEPS/IRP/NIA/NIH, 251 Bayview Blvd, Suite 100, Baltimore, MD 21224, USA, Tel: 410-558-8292
| | - Nick R Bryan
- Department of Radiology, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, Austin, US
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institute of Health, LEPS/IRP/NIA/NIH, 251 Bayview Blvd, Suite 100, Baltimore, MD 21224, USA, Tel: 410-558-8292
| |
Collapse
|
36
|
Dintica CS, Habes M, Simone T, Schreiner P, Erus G, Yaffe K. Depression in young adulthood to midlife and brain aging in midlife: A 30‐year follow‐up of The CARDIA Study. Alzheimers Dement 2022. [DOI: 10.1002/alz.064749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
| | - Mohamad Habes
- Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC), Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Sciences Center San Antonio TX USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Philadelphia PA USA
| | - Tamar Simone
- NCIRE‐The Veterans Health Research Institute San Francisco CA USA
- University of California, San Francsico San Francisco CA USA
| | | | - Guray Erus
- University of Pennsylvania Philadelphia PA USA
| | - Kristine Yaffe
- University of California, San Francisco San Francisco CA USA
| |
Collapse
|
37
|
Peng Z, Duggan MR, Dark HE, Daya GN, An Y, Davatzikos C, Erus G, Lewis A, Moghekar AR, Walker KA. Association of liver disease with brain volume loss, cognitive decline, and plasma neurodegenerative disease biomarkers. Neurobiol Aging 2022; 120:34-42. [PMID: 36115133 PMCID: PMC9685609 DOI: 10.1016/j.neurobiolaging.2022.08.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 08/09/2022] [Accepted: 08/12/2022] [Indexed: 11/29/2022]
Abstract
Although liver dysfunction has been implicated in Alzheimer's disease (AD), it remains unknown how liver disease may influence the trajectory of brain and cognitive changes in older adults. We related self-reported liver disease to longitudinal measures of brain structure and cognition, as well as baseline measures of plasma AD/neurodegeneration biomarkers in the Baltimore Longitudinal Study of Aging. Liver disease was identified using ICD-9 classification codes. Brain volume and cognition were assessed serially using 3T-MRI and a cognitive battery. 1008, 2157, and 780 participants were included in the MRI, cognitive, and plasma biomarker analysis, respectively. After adjustment for confounders, liver disease was associated with accelerated decline in total brain and white matter volume, but not total gray matter or AD signature region volume. Although liver disease showed no relationship with domain-specific cognitive decline or plasma biomarkers, participants with a history of hepatitis demonstrated accelerated decline in verbal fluency and elevated neurofilament light. Results suggest all-cause liver disease may accelerate brain volume loss but does not appear to promote AD-specific neurocognitive changes.
Collapse
Affiliation(s)
- Zhongsheng Peng
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Michael R Duggan
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Heather E Dark
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Gulzar N Daya
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Christos Davatzikos
- Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Guray Erus
- Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Alexandria Lewis
- Deparment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Abhay R Moghekar
- Deparment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Keenan A Walker
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA.
| |
Collapse
|
38
|
Govindarajan ST, Mamourian E, Erus G, Abdulkadir A, Melhem R, Doshi J, Pomponio R, Tosun D, Bilgel M, An Y, Sotiras A, Marcus DS, LaMontagne PJ, Espeland MA, Masters CL, Maruff P, Launer LJ, Fripp J, Johnson SC, Morris JC, Albert MS, Bryan RN, Resnick SM, Habes M, Shou H, Wolk DA, Nasrallah IM, Davatzikos C. Machine‐learning based MRI neuro‐anatomical signatures associated with cardiovascular and metabolic risk factors. Alzheimers Dement 2022. [DOI: 10.1002/alz.061530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
| | - Elizabeth Mamourian
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Philadelphia PA USA
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Philadelphia PA USA
| | - Ahmed Abdulkadir
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Philadelphia PA USA
| | - Randa Melhem
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Philadelphia PA USA
| | - Jimit Doshi
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Philadelphia PA USA
| | - Raymond Pomponio
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Philadelphia PA USA
| | - Duygu Tosun
- University of California, San Francisco San Francisco CA USA
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program Baltimore MD USA
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program Baltimore MD USA
| | | | - Daniel S. Marcus
- Washington University in St. Louis School of Medicine St. Louis MO USA
| | | | | | - Colin L. Masters
- Florey Institute of Neuroscience and Mental Health Parkville VIC Australia
| | - Paul Maruff
- The Florey Institute of Neuroscience and Mental Health Melbourne VIC Australia
| | - Lenore J. Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging Baltimore MD USA
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Australian E‐Health Research Centre Brisbane QLD Australia
| | - Sterling C. Johnson
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health Madison WI USA
| | - John C. Morris
- Knight Alzheimer Disease Research Center St. Louis MO USA
| | - Marilyn S. Albert
- Department of Neurology, Division of Cognitive Neuroscience, John’s Hopkins University School of Medicine Baltimore MD USA
| | | | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program Baltimore MD USA
| | - Mohamad Habes
- Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC), Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Sciences Center San Antonio TX USA
| | - Haochang Shou
- Penn Statistics in Imaging and Visualization Center, University of Pennsylvania Philadelphia PA USA
| | - David A. Wolk
- Department of Neurology, University of Pennsylvania School of Medicine Philadelphia PA USA
| | - Ilya M. Nasrallah
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Philadelphia PA USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Philadelphia PA USA
| |
Collapse
|
39
|
Wen J, Cui Y, Yang Z, Bao J, Chen J, Erus G, Abdulkadir A, Mamourian E, Singh A, Yang S, Fan Y, Saykin AJ, Thompson PM, Jun GR, Ritchie MD, Shen L, Wolk DA, Shou H, Nasrallah IM, Davatzikos C. Genetic heterogeneity of four MCI/AD neuroanatomical dimensions discovered via deep learning. Alzheimers Dement 2022. [DOI: 10.1002/alz.065223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Junhao Wen
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Philadelphia PA USA
- University of Pennsylvania Philadelphia PA USA
| | | | - Zhijian Yang
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Philadelphia PA USA
| | | | - Jiong Chen
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Philadelphia PA USA
- University of Pennsylvania Philadelphia PA USA
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Philadelphia PA USA
- University of Pennsylvania Philadelphia PA USA
- Department of Radiology, University of Pennsylvania Philadelphia PA USA
| | - Ahmed Abdulkadir
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Philadelphia PA USA
- University of Bern Bern Switzerland
| | - Elizabeth Mamourian
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Philadelphia PA USA
| | | | - Shu Yang
- University of Pennsylvania Philadelphia PA USA
| | - Yong Fan
- University of Pennsylvania Philadelphia PA USA
| | | | - Paul M Thompson
- University of Southern California Marina del Rey CA USA
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California Marina del Rey CA USA
| | - Gyungah R Jun
- Boston University School of Public Health Boston MA USA
- Boston University School of Medicine Boston MA USA
- Eisai Andover Innovative Medicines (AiM) Institute Andover MA USA
| | | | - Li Shen
- University of Pennsylvania Philadelphia PA USA
- University of Pennsylvania Perelman School of Medicine Philadelphia PA USA
- Indiana University School of Informatics and Computing Indianapolis IN USA
- Indiana University School of Medicine Indianapolis IN USA
| | - David A. Wolk
- University of Pennsylvania Philadelphia PA USA
- Department of Neurology, University of Pennsylvania School of Medicine Philadelphia PA USA
- Penn Alzheimer’s Disease Research Center, University of Pennsylvania Philadelphia PA USA
- Department of Pathology and Laboratory Medicine, Alzheimer’s Disease Center, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
| | - Haochang Shou
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Philadelphia PA USA
- Penn Statistics in Imaging and Visualization Center, University of Pennsylvania Philadelphia PA USA
| | - Ilya M. Nasrallah
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Philadelphia PA USA
- University of Pennsylvania Philadelphia PA USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
| | - Christos Davatzikos
- University of Pennsylvania Philadelphia PA USA
- Department of Radiology, University of Pennsylvania Philadelphia PA USA
| |
Collapse
|
40
|
Duggan MR, Peng Z, An Y, Kitner‐Triolo M, Shafer AT, Davatzikos C, Erus G, Karikkineth A, Lewis A, Moghekar A, Walker KA. Herpes Viruses in the Baltimore Longitudinal Study of Aging:Associations with Brain Volumes, Cognitive Performance and Plasma Biomarkers. Alzheimers Dement 2022. [DOI: 10.1002/alz.063612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Michael R Duggan
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program Baltimore MD USA
| | - Zhongsheng Peng
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program Baltimore MD USA
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program Baltimore MD USA
| | - Melissa Kitner‐Triolo
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program Baltimore MD USA
| | - Andrea T Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program Baltimore MD USA
| | | | - Guray Erus
- Department of Radiology, University of Pennsylvania Philadelphia PA USA
| | - Ajoy Karikkineth
- Clinical Research Core, National Institute on Aging Baltimore MD USA
| | - Alexandria Lewis
- Department of Neurology, Johns Hopkins University School of Medicine Baltimore MD USA
| | - Abhay Moghekar
- Johns Hopkins University School of Medicine Baltimore MD USA
| | - Keenan A Walker
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program Baltimore MD USA
| |
Collapse
|
41
|
Shaked D, Katzel LI, Davatzikos C, Gullapalli RP, Seliger SL, Erus G, Evans MK, Zonderman AB, Waldstein SR. White matter integrity as a mediator between socioeconomic status and executive function. Front Hum Neurosci 2022; 16:1021857. [PMID: 36466616 PMCID: PMC9716285 DOI: 10.3389/fnhum.2022.1021857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 11/04/2022] [Indexed: 11/03/2023] Open
Abstract
Introduction Lower socioeconomic status (SES) is associated with poorer executive function, but the neural mechanisms of this association remain unclear. As healthy brain communication is essential to our cognitive abilities, white matter integrity may be key to understanding socioeconomic disparities. Methods Participants were 201 African American and White adults (ages 33-72) from the Healthy Aging in Neighborhoods of Diversity across the Life Span (HANDLS) SCAN study. Diffusion tensor imaging was used to estimate regional fractional anisotropy as a measure of white matter integrity. Adjusting for age, analyses examined if integrity of the anterior limb of the internal capsule (ALIC), external capsule (EC), superior longitudinal fasciculus (SLF), and cingulum mediated SES-executive function relations. Results Lower SES was related to poorer cognitive performance and white matter integrity. Lower Trails B performance was related to poorer integrity of the ALIC, EC, and SLF, and lower Stroop performance was associated with poorer integrity of the ALIC and EC. ALIC mediated the SES-Trails B relation, and EC mediated the SES-Trails B and SES-Stroop relations. Sensitivity analyses revealed that (1) adjustment for race rendered the EC mediations non-significant, (2) when using poverty status and continuous education as predictors, results were largely the same, (3) at least some of the study's findings may generalize to processing speed, (4) mediations are not age-dependent in our sample, and (5) more research is needed to understand the role of cardiovascular risk factors in these models. Discussion Findings demonstrate that poorer white matter integrity helps explain SES disparities in executive function and highlight the need for further clarification of the biopsychosocial mechanisms of the SES-cognition association.
Collapse
Affiliation(s)
- Danielle Shaked
- Department of Psychology, University of Maryland, Baltimore County, Baltimore, MD, United States
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging Intramural Research Program, Baltimore, MD, United States
- Department of Psychology, VA Boston Health Care System, Boston, MA, United States
| | - Leslie I. Katzel
- Division of Gerontology, Geriatrics, and Palliative Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
- Geriatric Research Education and Clinical Center, Baltimore VA Medical Center, Baltimore, MD, United States
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Rao P. Gullapalli
- Department of Diagnostic Radiology, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Stephen L. Seliger
- Geriatric Research Education and Clinical Center, Baltimore VA Medical Center, Baltimore, MD, United States
- Division of Nephrology, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Michele K. Evans
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging Intramural Research Program, Baltimore, MD, United States
| | - Alan B. Zonderman
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging Intramural Research Program, Baltimore, MD, United States
| | - Shari R. Waldstein
- Department of Psychology, University of Maryland, Baltimore County, Baltimore, MD, United States
- Division of Gerontology, Geriatrics, and Palliative Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
- Geriatric Research Education and Clinical Center, Baltimore VA Medical Center, Baltimore, MD, United States
| |
Collapse
|
42
|
Duggan MR, Peng Z, An Y, Kitner Triolo MH, Shafer AT, Davatzikos C, Erus G, Karikkineth A, Lewis A, Moghekar A, Walker KA. Herpes Viruses in the Baltimore Longitudinal Study of Aging: Associations With Brain Volumes, Cognitive Performance, and Plasma Biomarkers. Neurology 2022; 99:e2014-e2024. [PMID: 35985823 PMCID: PMC9651463 DOI: 10.1212/wnl.0000000000201036] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 06/15/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Although an infectious etiology of Alzheimer disease (AD) has received renewed attention with a particular focus on herpes viruses, the longitudinal effects of symptomatic herpes virus (sHHV) infection on brain structure and cognition remain poorly understood, as does the effect of sHHV on AD/neurodegeneration biomarkers. METHODS We used a longitudinal, community-based cohort to characterize the association of sHHV diagnoses with changes in 3 T MRI brain volume and cognitive performance. In addition, we related sHHV to cross-sectional differences in plasma biomarkers of AD (β-amyloid [Aβ]42/40), astrogliosis (glial fibrillary acidic protein [GFAP]), and neurodegeneration (neurofilament light [NfL]). Baltimore Longitudinal Study of Aging participants were recruited from the community and assessed with serial brain MRIs and cognitive examinations over an average of 3.4 (SD = 3.2) and 8.6 (SD = 7.7) years, respectively. sHHV classification used International Classification of Diseases, Ninth Revision codes documented at comprehensive health and functional screening evaluations at each study visit. Linear mixed-effects and multivariable linear regression models were used in analyses. RESULTS A total of 1,009 participants were included in the primary MRI analysis, 98% of whom were cognitively normal at baseline MRI (mean age = 65.7 years; 54.8% female). Having a sHHV diagnosis (N = 119) was associated with longitudinal reductions in white matter volume (annual additional rate of change -0.34 cm3/y; p = 0.035), particularly in the temporal lobe. However, there was no association between sHHV and changes in total brain, total gray matter, or AD signature region volumes. Among the 119 participants with sHHV, exposure to antiviral treatment attenuated declines in occipital white matter (p = 0.04). Although the sHHV group had higher cognitive scores at baseline, sHHV diagnosis was associated with accelerated longitudinal declines in attention (annual additional rate of change -0.01 Z-score/year; p = 0.008). In addition, sHHV diagnosis was associated with elevated plasma GFAP, but not related to Aβ42/40 and NfL levels. DISCUSSION These findings suggest an association of sHHV infection with white matter volume loss, attentional decline, and astrogliosis. Although the findings link sHHV to several neurocognitive features, the results do not support an association between sHHV and AD-specific disease processes.
Collapse
Affiliation(s)
- Michael R Duggan
- From the Laboratory of Behavioral Neuroscience (M.R.D., Z.P., Y.A., M.H.K.T., A.T.S., K.A.W.), National Institute on Aging, Baltimore, MD; Section of Biomedical Image Analysis (C.D., G.E.), Department of Radiology, University of Pennsylvania, Philadelphia; Clinical Research Core (A.K.), National Institute on Aging; and Department of Neurology (A.L., A.M.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Zhongsheng Peng
- From the Laboratory of Behavioral Neuroscience (M.R.D., Z.P., Y.A., M.H.K.T., A.T.S., K.A.W.), National Institute on Aging, Baltimore, MD; Section of Biomedical Image Analysis (C.D., G.E.), Department of Radiology, University of Pennsylvania, Philadelphia; Clinical Research Core (A.K.), National Institute on Aging; and Department of Neurology (A.L., A.M.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Yang An
- From the Laboratory of Behavioral Neuroscience (M.R.D., Z.P., Y.A., M.H.K.T., A.T.S., K.A.W.), National Institute on Aging, Baltimore, MD; Section of Biomedical Image Analysis (C.D., G.E.), Department of Radiology, University of Pennsylvania, Philadelphia; Clinical Research Core (A.K.), National Institute on Aging; and Department of Neurology (A.L., A.M.), Johns Hopkins University School of Medicine, Baltimore, MD.
| | - Melissa H Kitner Triolo
- From the Laboratory of Behavioral Neuroscience (M.R.D., Z.P., Y.A., M.H.K.T., A.T.S., K.A.W.), National Institute on Aging, Baltimore, MD; Section of Biomedical Image Analysis (C.D., G.E.), Department of Radiology, University of Pennsylvania, Philadelphia; Clinical Research Core (A.K.), National Institute on Aging; and Department of Neurology (A.L., A.M.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Andrea T Shafer
- From the Laboratory of Behavioral Neuroscience (M.R.D., Z.P., Y.A., M.H.K.T., A.T.S., K.A.W.), National Institute on Aging, Baltimore, MD; Section of Biomedical Image Analysis (C.D., G.E.), Department of Radiology, University of Pennsylvania, Philadelphia; Clinical Research Core (A.K.), National Institute on Aging; and Department of Neurology (A.L., A.M.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Christos Davatzikos
- From the Laboratory of Behavioral Neuroscience (M.R.D., Z.P., Y.A., M.H.K.T., A.T.S., K.A.W.), National Institute on Aging, Baltimore, MD; Section of Biomedical Image Analysis (C.D., G.E.), Department of Radiology, University of Pennsylvania, Philadelphia; Clinical Research Core (A.K.), National Institute on Aging; and Department of Neurology (A.L., A.M.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Guray Erus
- From the Laboratory of Behavioral Neuroscience (M.R.D., Z.P., Y.A., M.H.K.T., A.T.S., K.A.W.), National Institute on Aging, Baltimore, MD; Section of Biomedical Image Analysis (C.D., G.E.), Department of Radiology, University of Pennsylvania, Philadelphia; Clinical Research Core (A.K.), National Institute on Aging; and Department of Neurology (A.L., A.M.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Ajoy Karikkineth
- From the Laboratory of Behavioral Neuroscience (M.R.D., Z.P., Y.A., M.H.K.T., A.T.S., K.A.W.), National Institute on Aging, Baltimore, MD; Section of Biomedical Image Analysis (C.D., G.E.), Department of Radiology, University of Pennsylvania, Philadelphia; Clinical Research Core (A.K.), National Institute on Aging; and Department of Neurology (A.L., A.M.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Alexandria Lewis
- From the Laboratory of Behavioral Neuroscience (M.R.D., Z.P., Y.A., M.H.K.T., A.T.S., K.A.W.), National Institute on Aging, Baltimore, MD; Section of Biomedical Image Analysis (C.D., G.E.), Department of Radiology, University of Pennsylvania, Philadelphia; Clinical Research Core (A.K.), National Institute on Aging; and Department of Neurology (A.L., A.M.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Abhay Moghekar
- From the Laboratory of Behavioral Neuroscience (M.R.D., Z.P., Y.A., M.H.K.T., A.T.S., K.A.W.), National Institute on Aging, Baltimore, MD; Section of Biomedical Image Analysis (C.D., G.E.), Department of Radiology, University of Pennsylvania, Philadelphia; Clinical Research Core (A.K.), National Institute on Aging; and Department of Neurology (A.L., A.M.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Keenan A Walker
- From the Laboratory of Behavioral Neuroscience (M.R.D., Z.P., Y.A., M.H.K.T., A.T.S., K.A.W.), National Institute on Aging, Baltimore, MD; Section of Biomedical Image Analysis (C.D., G.E.), Department of Radiology, University of Pennsylvania, Philadelphia; Clinical Research Core (A.K.), National Institute on Aging; and Department of Neurology (A.L., A.M.), Johns Hopkins University School of Medicine, Baltimore, MD.
| |
Collapse
|
43
|
Chand GB, Singhal P, Dwyer DB, Wen J, Erus G, Doshi J, Srinivasan D, Mamourian E, Varol E, Sotiras A, Hwang G, Dazzan P, Kahn RS, Schnack HG, Zanetti MV, Meisenzahl E, Busatto GF, Crespo-Facorro B, Pantelis C, Wood SJ, Zhuo C, Shinohara RT, Shou H, Fan Y, Koutsouleris N, Kaczkurkin AN, Moore TM, Verma A, Calkins ME, Gur RE, Gur RC, Ritchie MD, Satterthwaite TD, Wolf DH, Davatzikos C. Schizophrenia Imaging Signatures and Their Associations With Cognition, Psychopathology, and Genetics in the General Population. Am J Psychiatry 2022; 179:650-660. [PMID: 35410495 PMCID: PMC9444886 DOI: 10.1176/appi.ajp.21070686] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
OBJECTIVE The prevalence and significance of schizophrenia-related phenotypes at the population level is debated in the literature. Here, the authors assessed whether two recently reported neuroanatomical signatures of schizophrenia-signature 1, with widespread reduction of gray matter volume, and signature 2, with increased striatal volume-could be replicated in an independent schizophrenia sample, and investigated whether expression of these signatures can be detected at the population level and how they relate to cognition, psychosis spectrum symptoms, and schizophrenia genetic risk. METHODS This cross-sectional study used an independent schizophrenia-control sample (N=347; ages 16-57 years) for replication of imaging signatures, and then examined two independent population-level data sets: typically developing youths and youths with psychosis spectrum symptoms in the Philadelphia Neurodevelopmental Cohort (N=359; ages 16-23 years) and adults in the UK Biobank study (N=836; ages 44-50 years). The authors quantified signature expression using support-vector machine learning and compared cognition, psychopathology, and polygenic risk between signatures. RESULTS Two neuroanatomical signatures of schizophrenia were replicated. Signature 1 but not signature 2 was significantly more common in youths with psychosis spectrum symptoms than in typically developing youths, whereas signature 2 frequency was similar in the two groups. In both youths and adults, signature 1 was associated with worse cognitive performance than signature 2. Compared with adults with neither signature, adults expressing signature 1 had elevated schizophrenia polygenic risk scores, but this was not seen for signature 2. CONCLUSIONS The authors successfully replicated two neuroanatomical signatures of schizophrenia and describe their prevalence in population-based samples of youths and adults. They further demonstrated distinct relationships of these signatures with psychosis symptoms, cognition, and genetic risk, potentially reflecting underlying neurobiological vulnerability.
Collapse
Affiliation(s)
- Ganesh B Chand
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Pankhuri Singhal
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Dominic B Dwyer
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Junhao Wen
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Jimit Doshi
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Dhivya Srinivasan
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Elizabeth Mamourian
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Erdem Varol
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Aristeidis Sotiras
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Gyujoon Hwang
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Paola Dazzan
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Rene S Kahn
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Hugo G Schnack
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Marcus V Zanetti
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Eva Meisenzahl
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Geraldo F Busatto
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Benedicto Crespo-Facorro
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Christos Pantelis
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Stephen J Wood
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Chuanjun Zhuo
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Russell T Shinohara
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Haochang Shou
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Nikolaos Koutsouleris
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Antonia N Kaczkurkin
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Tyler M Moore
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Anurag Verma
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Monica E Calkins
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Raquel E Gur
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Ruben C Gur
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Marylyn D Ritchie
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Theodore D Satterthwaite
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Daniel H Wolf
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| |
Collapse
|
44
|
Bouhrara M, Triebswetter C, Kiely M, Bilgel M, Dolui S, Erus G, Meirelles O, Bryan NR, Detre JA, Launer LJ. Association of Cerebral Blood Flow With Longitudinal Changes in Cerebral Microstructural Integrity in the Coronary Artery Risk Development in Young Adults (CARDIA) Study. JAMA Netw Open 2022; 5:e2231189. [PMID: 36094503 PMCID: PMC9468885 DOI: 10.1001/jamanetworkopen.2022.31189] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
IMPORTANCE Decreased cerebral tissue integrity and cerebral blood flow (CBF) are features of neurodegenerative diseases. Brain tissue maintenance is an energy-demanding process, making it particularly sensitive to hypoperfusion. However, little is known about the association between blood flow and brain microstructural integrity, including in normative aging. OBJECTIVE To assess associations between CBF and changes in cerebral tissue integrity in white matter and gray matter brain regions. DESIGN, SETTING, AND PARTICIPANTS In this longitudinal cohort study, magnetic resonance imaging was performed on 732 healthy adults from the Coronary Artery Risk Development in Young Adults (CARDIA) study, a prospective longitudinal study (baseline age of 18-30 years) that examined participants up to 8 times during 30 years (1985-1986 to 2015-2016). Cerebral blood flow was measured at baseline (year 25 of the CARDIA study), and changes in diffusion tensor indices of fractional anisotropy (FA) and mean diffusivity (MD), measures of microstructural tissue integrity, were measured at both baseline and after approximately 5 years of follow-up (year 30). Analyses were conducted from November 5, 2020, to January 29, 2022. MAIN OUTCOMES AND MEASURES Automated algorithms and linear mixed-effects statistical models were used to evaluate the associations between CBF at baseline and changes in FA or MD. RESULTS After exclusion of participants with missing or low-quality data, 654 at baseline (342 women; mean [SD] age, 50.3 [3.5] years) and 433 at follow-up (230 women; mean [SD] age, 55.1 [3.5] years) were scanned for CBF or FA and MD imaging. In the baseline cohort, 247 participants were Black (37.8%) and 394 were White (60.2%); in the follow-up cohort, 156 were Black (36.0%) and 277 were White (64.0%). Cross-sectionally, FA and MD were associated with CBF in most regions evaluated, with lower CBF values associated with lower FA or higher MD values, including the frontal white matter lobes (for CBF and MD: mean [SE] β = -1.4 [0.5] × 10-6; for CBF and FA: mean [SE] β = 2.9 [1.0] × 10-4) and the parietal white matter lobes (for CBF and MD: mean [SE] β = -2.4 [0.6] × 10-6; for CBF and FA: mean [SE] β = 4.4 [1.1] × 10-4). Lower CBF values at baseline were also significantly associated with steeper regional decreases in FA or increases in MD in most brain regions investigated, including the frontal (for CBF and MD: mean [SE] β = -1.1 [0.6] × 10-6; for CBF and FA: mean [SE] β = 2.9 [1.0] × 10-4) and parietal lobes (for CBF and MD: mean [SE] β = -1.5 [0.7] × 10-6; for CBF and FA: mean [SE] β = 4.4 [1.1] × 10-4). CONCLUSIONS AND RELEVANCE Results of this longitudinal cohort study of the association between CBF and diffusion tensor imaging metrics suggest that blood flow may be significantly associated with brain tissue microstructure. This work may lay the foundation for investigations to clarify the nature of early brain damage in neurodegeneration. Such studies may lead to new neuroimaging biomarkers of brain microstructure and function for disease progression.
Collapse
Affiliation(s)
- Mustapha Bouhrara
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Curtis Triebswetter
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Matthew Kiely
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Sudipto Dolui
- Department of Radiology, University of Pennsylvania, Philadelphia
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Osorio Meirelles
- Laboratory of Epidemiology and Population Science, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Nick R. Bryan
- Department of Diagnostic Medicine, University of Texas, Austin
| | - John A. Detre
- Department of Radiology, University of Pennsylvania, Philadelphia
- Department of Neurology, University of Pennsylvania, Philadelphia
| | - Lenore J. Launer
- Laboratory of Epidemiology and Population Science, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| |
Collapse
|
45
|
Jacobson AM, Braffett BH, Erus G, Ryan CM, Biessels GJ, Luchsinger JA, Bebu I, Gubitosi-Klug RA, Desiderio L, Lorenzi GM, Trapani VR, Lachin JM, Bryan RN, Habes M, Nasrallah IM. Brain Structure Among Middle-aged and Older Adults With Long-standing Type 1 Diabetes in the DCCT/EDIC Study. Diabetes Care 2022; 45:1779-1787. [PMID: 35699949 PMCID: PMC9346989 DOI: 10.2337/dc21-2438] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 04/17/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Individuals with type 1 diabetes mellitus (T1DM) are living to ages when neuropathological changes are increasingly evident. We hypothesized that middle-aged and older adults with long-standing T1DM will show abnormal brain structure in comparison with control subjects without diabetes. RESEARCH DESIGN AND METHODS MRI was used to compare brain structure among 416 T1DM participants in the Epidemiology of Diabetes Interventions and Complications (EDIC) study with that of 99 demographically similar control subjects without diabetes at 26 U.S. and Canadian sites. Assessments included total brain (TBV) (primary outcome), gray matter (GMV), white matter (WMV), ventricle, and white matter hyperintensity (WMH) volumes and total white matter mean fractional anisotropy (FA). Biomedical assessments included HbA1c and lipid levels, blood pressure, and cognitive assessments of memory and psychomotor and mental efficiency (PME). Among EDIC participants, HbA1c, severe hypoglycemia history, and vascular complications were measured longitudinally. RESULTS Mean age of EDIC participants and control subjects was 60 years. T1DM participants showed significantly smaller TBV (least squares mean ± SE 1,206 ± 1.7 vs. 1,229 ± 3.5 cm3, P < 0.0001), GMV, and WMV and greater ventricle and WMH volumes but no differences in total white matter mean FA versus control subjects. Structural MRI measures in T1DM were equivalent to those of control subjects who were 4-9 years older. Lower PME scores were associated with altered brain structure on all MRI measures in T1DM participants. CONCLUSIONS Middle-aged and older adults with T1DM showed brain volume loss and increased vascular injury in comparison with control subjects without diabetes, equivalent to 4-9 years of brain aging.
Collapse
Affiliation(s)
- Alan M Jacobson
- NYU Long Island School of Medicine, NYU Langone Hospital-Long Island, Mineola
| | - Barbara H Braffett
- The Biostatistics Center, The George Washington University, Rockville, MD
| | - Guray Erus
- Department of Radiology, University of Pennsylvania, Philadelphia, PA
| | | | - Geert J Biessels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Netherlands
| | | | - Ionut Bebu
- The Biostatistics Center, The George Washington University, Rockville, MD
| | - Rose A Gubitosi-Klug
- Case Western Reserve University School of Medicine, Rainbow Babies & Children's Hospital, Cleveland, OH
| | - Lisa Desiderio
- Department of Radiology, University of Pennsylvania, Philadelphia, PA
| | | | | | - John M Lachin
- Department of Radiology, University of Pennsylvania, Philadelphia, PA
| | | | - Mohamad Habes
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX
| | - Ilya M Nasrallah
- Department of Radiology, University of Pennsylvania, Philadelphia, PA
| | | |
Collapse
|
46
|
Dintica CS, Habes M, Erus G, Vittinghoff E, Davatzikos C, Nasrallah IM, Launer LJ, Sidney S, Yaffe K. Elevated blood pressure is associated with advanced brain aging in mid-life: A 30-year follow-up of The CARDIA Study. Alzheimers Dement 2022; 19:10.1002/alz.12725. [PMID: 35779250 PMCID: PMC9806185 DOI: 10.1002/alz.12725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 05/24/2022] [Accepted: 05/25/2022] [Indexed: 01/04/2023]
Abstract
BACKGROUND High blood pressure (BP) is a risk factor for late-life brain health; however, the association of elevated BP with brain health in mid-life is unclear. METHODS We identified 661 participants from the Coronary Artery Risk Development in Young Adults Study (age 18-30 at baseline) with 30 years of follow-up and brain magnetic resonance imaging at year 30. Cumulative exposure of BP was estimated by time-weighted averages (TWA). Ideal cardiovascular health was defined as systolic BP < 120 mm Hg, diastolic BP < 80 mm Hg. Brain age was calculated using previously validated high dimensional machine learning pattern analyses. RESULTS Every 5 mmHg increment in TWA systolic BP was associated with approximately 1-year greater brain age (95% confidence interval [CI]: 0.50-1.36) Participants with TWA systolic or diastolic BP over the recommended guidelines for ideal cardiovascular health, had on average 3-year greater brain age (95% CI: 1.00-4.67; 95% CI: 1.45-5.13, respectively). CONCLUSION Elevated BP from early to mid adulthood, even below clinical cut-offs, is associated with advanced brain aging in mid-life.
Collapse
Affiliation(s)
| | - Mohamad Habes
- University of Pennsylvania, Philadelphia, PA
- Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC), Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), San Antonio, TX, USA
| | - Guray Erus
- University of Pennsylvania, Philadelphia, PA
| | | | | | | | | | | | | |
Collapse
|
47
|
Hwang G, Abdulkadir A, Erus G, Habes M, Pomponio R, Shou H, Doshi J, Mamourian E, Rashid T, Bilgel M, Fan Y, Sotiras A, Srinivasan D, Morris JC, Albert MS, Bryan NR, Resnick SM, Nasrallah IM, Davatzikos C, Wolk DA. Disentangling Alzheimer’s disease neurodegeneration from typical brain ageing using machine learning. Brain Commun 2022; 4:fcac117. [PMID: 35611306 PMCID: PMC9123890 DOI: 10.1093/braincomms/fcac117] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 02/17/2022] [Accepted: 05/04/2022] [Indexed: 11/17/2022] Open
Abstract
Neuroimaging biomarkers that distinguish between changes due to typical brain ageing and Alzheimer’s disease are valuable for determining how much each contributes to cognitive decline. Supervised machine learning models can derive multivariate patterns of brain change related to the two processes, including the Spatial Patterns of Atrophy for Recognition of Alzheimer’s Disease (SPARE-AD) and of Brain Aging (SPARE-BA) scores investigated herein. However, the substantial overlap between brain regions affected in the two processes confounds measuring them independently. We present a methodology, and associated results, towards disentangling the two. T1-weighted MRI scans of 4054 participants (48–95 years) with Alzheimer’s disease, mild cognitive impairment (MCI), or cognitively normal (CN) diagnoses from the Imaging-based coordinate SysTem for AGIng and NeurodeGenerative diseases (iSTAGING) consortium were analysed. Multiple sets of SPARE scores were investigated, in order to probe imaging signatures of certain clinically or molecularly defined sub-cohorts. First, a subset of clinical Alzheimer’s disease patients (n = 718) and age- and sex-matched CN adults (n = 718) were selected based purely on clinical diagnoses to train SPARE-BA1 (regression of age using CN individuals) and SPARE-AD1 (classification of CN versus Alzheimer’s disease) models. Second, analogous groups were selected based on clinical and molecular markers to train SPARE-BA2 and SPARE-AD2 models: amyloid-positive Alzheimer’s disease continuum group (n = 718; consisting of amyloid-positive Alzheimer’s disease, amyloid-positive MCI, amyloid- and tau-positive CN individuals) and amyloid-negative CN group (n = 718). Finally, the combined group of the Alzheimer’s disease continuum and amyloid-negative CN individuals was used to train SPARE-BA3 model, with the intention to estimate brain age regardless of Alzheimer’s disease-related brain changes. The disentangled SPARE models, SPARE-AD2 and SPARE-BA3, derived brain patterns that were more specific to the two types of brain changes. The correlation between the SPARE-BA Gap (SPARE-BA minus chronological age) and SPARE-AD was significantly reduced after the decoupling (r = 0.56–0.06). The correlation of disentangled SPARE-AD was non-inferior to amyloid- and tau-related measurements and to the number of APOE ε4 alleles but was lower to Alzheimer’s disease-related psychometric test scores, suggesting the contribution of advanced brain ageing to the latter. The disentangled SPARE-BA was consistently less correlated with Alzheimer’s disease-related clinical, molecular and genetic variables. By employing conservative molecular diagnoses and introducing Alzheimer’s disease continuum cases to the SPARE-BA model training, we achieved more dissociable neuroanatomical biomarkers of typical brain ageing and Alzheimer’s disease.
Collapse
Affiliation(s)
- Gyujoon Hwang
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Ahmed Abdulkadir
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Mohamad Habes
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, USA
| | - Raymond Pomponio
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Haochang Shou
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Jimit Doshi
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Elizabeth Mamourian
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Tanweer Rashid
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Aristeidis Sotiras
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
- Department of Radiology, Washington University in St. Louis, St. Louis, USA
| | - Dhivya Srinivasan
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - John C. Morris
- Department of Neurology, Washington University in St. Louis, St. Louis, USA
| | - Marilyn S. Albert
- Department of Neurology, Johns Hopkins University School of Medicine, USA
| | - Nick R. Bryan
- Department of Diagnostic Medicine, University of Texas, Austin; Austin, USA
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, USA
| | - Ilya M. Nasrallah
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - David A. Wolk
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
- Department of Neurology and Penn Memory Center, University of Pennsylvania, Philadelphia, USA
| | | |
Collapse
|
48
|
Wen J, Fu CHY, Tosun D, Veturi Y, Yang Z, Abdulkadir A, Mamourian E, Srinivasan D, Skampardoni I, Singh A, Nawani H, Bao J, Erus G, Shou H, Habes M, Doshi J, Varol E, Mackin RS, Sotiras A, Fan Y, Saykin AJ, Sheline YI, Shen L, Ritchie MD, Wolk DA, Albert M, Resnick SM, Davatzikos C. Characterizing Heterogeneity in Neuroimaging, Cognition, Clinical Symptoms, and Genetics Among Patients With Late-Life Depression. JAMA Psychiatry 2022; 79:464-474. [PMID: 35262657 PMCID: PMC8908227 DOI: 10.1001/jamapsychiatry.2022.0020] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 12/19/2021] [Indexed: 12/14/2022]
Abstract
Importance Late-life depression (LLD) is characterized by considerable heterogeneity in clinical manifestation. Unraveling such heterogeneity might aid in elucidating etiological mechanisms and support precision and individualized medicine. Objective To cross-sectionally and longitudinally delineate disease-related heterogeneity in LLD associated with neuroanatomy, cognitive functioning, clinical symptoms, and genetic profiles. Design, Setting, and Participants The Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) study is an international multicenter consortium investigating brain aging in pooled and harmonized data from 13 studies with more than 35 000 participants, including a subset of individuals with major depressive disorder. Multimodal data from a multicenter sample (N = 996), including neuroimaging, neurocognitive assessments, and genetics, were analyzed in this study. A semisupervised clustering method (heterogeneity through discriminative analysis) was applied to regional gray matter (GM) brain volumes to derive dimensional representations. Data were collected from July 2017 to July 2020 and analyzed from July 2020 to December 2021. Main Outcomes and Measures Two dimensions were identified to delineate LLD-associated heterogeneity in voxelwise GM maps, white matter (WM) fractional anisotropy, neurocognitive functioning, clinical phenotype, and genetics. Results A total of 501 participants with LLD (mean [SD] age, 67.39 [5.56] years; 332 women) and 495 healthy control individuals (mean [SD] age, 66.53 [5.16] years; 333 women) were included. Patients in dimension 1 demonstrated relatively preserved brain anatomy without WM disruptions relative to healthy control individuals. In contrast, patients in dimension 2 showed widespread brain atrophy and WM integrity disruptions, along with cognitive impairment and higher depression severity. Moreover, 1 de novo independent genetic variant (rs13120336; chromosome: 4, 186387714; minor allele, G) was significantly associated with dimension 1 (odds ratio, 2.35; SE, 0.15; P = 3.14 ×108) but not with dimension 2. The 2 dimensions demonstrated significant single-nucleotide variant-based heritability of 18% to 27% within the general population (N = 12 518 in UK Biobank). In a subset of individuals having longitudinal measurements, those in dimension 2 experienced a more rapid longitudinal change in GM and brain age (Cohen f2 = 0.03; P = .02) and were more likely to progress to Alzheimer disease (Cohen f2 = 0.03; P = .03) compared with those in dimension 1 (N = 1431 participants and 7224 scans from the Alzheimer's Disease Neuroimaging Initiative [ADNI], Baltimore Longitudinal Study of Aging [BLSA], and Biomarkers for Older Controls at Risk for Dementia [BIOCARD] data sets). Conclusions and Relevance This study characterized heterogeneity in LLD into 2 dimensions with distinct neuroanatomical, cognitive, clinical, and genetic profiles. This dimensional approach provides a potential mechanism for investigating the heterogeneity of LLD and the relevance of the latent dimensions to possible disease mechanisms, clinical outcomes, and responses to interventions.
Collapse
Affiliation(s)
- Junhao Wen
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Cynthia H. Y. Fu
- University of East London, School of Psychology, London, United Kingdom
- Centre for Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California, San Francisco
| | - Yogasudha Veturi
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Zhijian Yang
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Ahmed Abdulkadir
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Elizabeth Mamourian
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Dhivya Srinivasan
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Ioanna Skampardoni
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Ashish Singh
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Hema Nawani
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Haochang Shou
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Mohamad Habes
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio
| | - Jimit Doshi
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Erdem Varol
- Department of Statistics, Center for Theoretical Neuroscience, Zuckerman Institute, Columbia University, New York, New York
| | - R. Scott Mackin
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco
| | - Aristeidis Sotiras
- Department of Radiology and Institute for Informatics, Washington University School of Medicine, St Louis, Missouri
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Andrew J. Saykin
- Radiology and Imaging Sciences, Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana Alzheimer’s Disease Research Center and the Melvin and Bren Simon Cancer Center, Indiana University School of Medicine, Indianapolis
| | - Yvette I. Sheline
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Marylyn D. Ritchie
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - David A. Wolk
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Department of Neurology and Penn Memory Center, University of Pennsylvania, Philadelphia
| | - Marilyn Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| |
Collapse
|
49
|
Austin TR, Nasrallah IM, Erus G, Desiderio LM, Chen LY, Greenland P, Harding BN, Hughes TM, Jensen PN, Longstreth WT, Post WS, Shea SJ, Sitlani CM, Davatzikos C, Habes M, Nick Bryan R, Heckbert SR. Association of Brain Volumes and White Matter Injury With Race, Ethnicity, and Cardiovascular Risk Factors: The Multi-Ethnic Study of Atherosclerosis. J Am Heart Assoc 2022; 11:e023159. [PMID: 35352569 PMCID: PMC9075451 DOI: 10.1161/jaha.121.023159] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background Cardiovascular risk factors are associated with cognitive decline and dementia. Magnetic resonance imaging provides sensitive measurement of brain morphology and vascular brain injury. However, associations of risk factors with brain magnetic resonance imaging findings have largely been studied in White participants. We investigated associations of race, ethnicity, and cardiovascular risk factors with brain morphology and white matter (WM) injury in a diverse population. Methods and Results In the Multi-Ethnic Study of Atherosclerosis, measures were made in 2018 to 2019 of total brain volume, gray matter and WM volume, and WM injury, including WM hyperintensity volume and WM fractional anisotropy. We assessed cross-sectional associations of race and ethnicity and of cardiovascular risk factors with magnetic resonance imaging measures. Magnetic resonance imaging data were complete in 1036 participants; 25% Black, 15% Chinese-American, 19% Hispanic, and 41% White. Mean (SD) age was 72 (8) years and 53% were women. Although WM injury was greater in Black than in White participants in a minimally adjusted model, additional adjustment for cardiovascular risk factors and socioeconomic status each attenuated this association, rendering it nonsignificant. Overall, greater average WM hyperintensity volume was associated with older age and current smoking (69% greater vs never smoking); lower fractional anisotropy was additionally associated with higher diastolic blood pressure, use of antihypertensive medication, and diabetes. Conclusions We found no statistically significant difference in measures of WM injury by race and ethnicity after adjustment for cardiovascular risk factors and socioeconomic status. In all racial and ethnic groups, older age, current smoking, hypertension, and diabetes were strongly associated with WM injury.
Collapse
Affiliation(s)
- Thomas R Austin
- Department of Epidemiology University of Washington Seattle WA
| | - Ilya M Nasrallah
- Department of Radiology University of Pennsylvania Philadelphia PA
| | - Guray Erus
- Department of Radiology University of Pennsylvania Philadelphia PA
| | - Lisa M Desiderio
- Department of Radiology University of Pennsylvania Philadelphia PA
| | - Lin Y Chen
- Cardiovascular Division University of Minnesota Minneapolis MN
| | - Philip Greenland
- Department of Preventative Medicine and Department of MedicineFeinberg School of Medicine Chicago IL
| | | | - Timothy M Hughes
- Department of Internal Medicine Wake Forest School of Medicine Winston-Salem NC
| | - Paul N Jensen
- Department of Medicine University of Washington Seattle WA
| | - W T Longstreth
- Department of Epidemiology University of Washington Seattle WA.,Department of Neurology University of Washington Seattle WA
| | - Wendy S Post
- Division of Cardiology Department of Medicine Johns Hopkins University Baltimore Maryland
| | - Steven J Shea
- Departments of Medicine and Epidemiology Columbia University New York NY
| | | | | | - Mohamad Habes
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases University of Texas Health Science Center San Antonio TX
| | - R Nick Bryan
- Department of Diagnostic Medicine University of Texas at Austin Austin TX
| | | |
Collapse
|
50
|
Bashyam VM, Doshi J, Erus G, Srinivasan D, Abdulkadir A, Habes M, Fan Y, Masters CL, Maruff P, Zhuo C, Völzke H, Johnson SC, Fripp J, Koutsouleris N, Satterthwaite TD, Wolf DH, Gur RE, Gur RC, Morris JC, Albert MS, Grabe HJ, Resnick SM, Bryan RN, Wittfeld K, Bülow R, Wolk DA, Shou H, Nasrallah IM, Davatzikos C, Davatzikos C. Deep Generative Medical Image Harmonization for Improving Cross-Site Generalization in Deep Learning Predictors. J Magn Reson Imaging 2022; 55:908-916. [PMID: 34564904 PMCID: PMC8844038 DOI: 10.1002/jmri.27908] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 08/22/2021] [Accepted: 08/23/2021] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND In the medical imaging domain, deep learning-based methods have yet to see widespread clinical adoption, in part due to limited generalization performance across different imaging devices and acquisition protocols. The deviation between estimated brain age and biological age is an established biomarker of brain health and such models may benefit from increased cross-site generalizability. PURPOSE To develop and evaluate a deep learning-based image harmonization method to improve cross-site generalizability of deep learning age prediction. STUDY TYPE Retrospective. POPULATION Eight thousand eight hundred and seventy-six subjects from six sites. Harmonization models were trained using all subjects. Age prediction models were trained using 2739 subjects from a single site and tested using the remaining 6137 subjects from various other sites. FIELD STRENGTH/SEQUENCE Brain imaging with magnetization prepared rapid acquisition with gradient echo or spoiled gradient echo sequences at 1.5 T and 3 T. ASSESSMENT StarGAN v2, was used to perform a canonical mapping from diverse datasets to a reference domain to reduce site-based variation while preserving semantic information. Generalization performance of deep learning age prediction was evaluated using harmonized, histogram matched, and unharmonized data. STATISTICAL TESTS Mean absolute error (MAE) and Pearson correlation between estimated age and biological age quantified the performance of the age prediction model. RESULTS Our results indicated a substantial improvement in age prediction in out-of-sample data, with the overall MAE improving from 15.81 (±0.21) years to 11.86 (±0.11) with histogram matching to 7.21 (±0.22) years with generative adversarial network (GAN)-based harmonization. In the multisite case, across the 5 out-of-sample sites, MAE improved from 9.78 (±6.69) years to 7.74 (±3.03) years with histogram normalization to 5.32 (±4.07) years with GAN-based harmonization. DATA CONCLUSION While further research is needed, GAN-based medical image harmonization appears to be a promising tool for improving cross-site deep learning generalization. LEVEL OF EVIDENCE 4 TECHNICAL EFFICACY: Stage 1.
Collapse
Affiliation(s)
- Vishnu M. Bashyam
- Artificial Intelligence in Biomedical Imaging Lab, University of Pennsylvania, Philadelphia, PA, USA,Corresponding authors: Vishnu Bashyam and Christos Davatzikos, ; , 3700 Hamilton Walk, 7th Floor, Artificial Intelligence in Biomedical Imaging Lab, University of Pennsylvania, Philadelphia, PA 19104; https://www.med.upenn.edu/cbica/
| | - Jimit Doshi
- Artificial Intelligence in Biomedical Imaging Lab, University of Pennsylvania, Philadelphia, PA, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Lab, University of Pennsylvania, Philadelphia, PA, USA
| | - Dhivya Srinivasan
- Artificial Intelligence in Biomedical Imaging Lab, University of Pennsylvania, Philadelphia, PA, USA
| | - Ahmed Abdulkadir
- Artificial Intelligence in Biomedical Imaging Lab, University of Pennsylvania, Philadelphia, PA, USA
| | - Mohamad Habes
- Biggs Alzheimer’s Institute, University of Texas San Antonio Health Science Center, USA
| | - Yong Fan
- Artificial Intelligence in Biomedical Imaging Lab, University of Pennsylvania, Philadelphia, PA, USA
| | - Colin L. Masters
- Florey Institute of Neuroscience and Mental Health, University of Melbourne
| | - Paul Maruff
- Florey Institute of Neuroscience and Mental Health, University of Melbourne
| | - Chuanjun Zhuo
- Tianjin Mental Health Center, Nankai University Affiliated Tianjin Anding Hospital, Tianjin, China,Department of Psychiatry, Tianjin Medical University, Tianjin, China
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Germany,German Centre for Cardiovascular Research, Partner Site Greifswald, Germany
| | - Sterling C. Johnson
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO
| | | | - Theodore D. Satterthwaite
- Artificial Intelligence in Biomedical Imaging Lab, University of Pennsylvania, Philadelphia, PA, USA,Department of Psychiatry, University of Pennsylvania
| | | | - Raquel E. Gur
- Department of Psychiatry, University of Pennsylvania,Department of Radiology, University of Pennsylvania
| | - Ruben C. Gur
- Department of Psychiatry, University of Pennsylvania,Department of Radiology, University of Pennsylvania
| | - John C. Morris
- Department of Neurology, Washington University in St. Louis
| | - Marilyn S. Albert
- Department of Neurology, Johns Hopkins University School of Medicine
| | - Hans J. Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany,German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Germany
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging
| | - R. Nick Bryan
- Department of Diagnostic Medicine, University of Texas at Austin
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany,German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Germany
| | - Robin Bülow
- Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Germany
| | | | - Haochang Shou
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania
| | | | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Lab, University of Pennsylvania, Philadelphia, PA, USA,Corresponding authors: Vishnu Bashyam and Christos Davatzikos, ; , 3700 Hamilton Walk, 7th Floor, Artificial Intelligence in Biomedical Imaging Lab, University of Pennsylvania, Philadelphia, PA 19104; https://www.med.upenn.edu/cbica/
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Lab, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | |
Collapse
|