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Zhu AH, Nir TM, Javid S, Villalón-Reina JE, Rodrigue AL, Strike LT, de Zubicaray GI, McMahon KL, Wright MJ, Medland SE, Blangero J, Glahn DC, Kochunov P, Williamson DE, Håberg AK, Thompson PM, Jahanshad N. Lifespan reference curves for harmonizing multi-site regional brain white matter metrics from diffusion MRI. Sci Data 2025; 12:748. [PMID: 40328780 PMCID: PMC12056076 DOI: 10.1038/s41597-025-05028-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 04/17/2025] [Indexed: 05/08/2025] Open
Abstract
Age-related white matter (WM) microstructure maturation and decline occur throughout the human lifespan, complementing the process of gray matter development and degeneration. Here, we create normative lifespan reference curves for global and regional WM microstructure by harmonizing diffusion MRI (dMRI)-derived data from ten public datasets (N = 40,898 subjects; age: 3-95 years; 47.6% male). We tested three harmonization methods on regional diffusion tensor imaging (DTI) based fractional anisotropy (FA), a metric of WM microstructure, extracted using the ENIGMA-DTI pipeline. ComBat-GAM harmonization provided multi-study trajectories most consistent with known WM maturation peaks. Lifespan FA reference curves were validated with test-retest data and used to assess the effect of the ApoE4 risk factor for dementia in WM across the lifespan. We found significant associations between ApoE4 and FA in WM regions associated with neurodegenerative disease even in healthy individuals across the lifespan, with regional age-by-genotype interactions. Our lifespan reference curves and tools to harmonize new dMRI data to the curves are publicly available as eHarmonize ( https://github.com/ahzhu/eharmonize ).
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Affiliation(s)
- Alyssa H Zhu
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
- Department of Biomedical Engineering, USC Viterbi School of Engineering, Los Angeles, CA, USA
| | - Talia M Nir
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Shayan Javid
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
- Department of Biomedical Engineering, USC Viterbi School of Engineering, Los Angeles, CA, USA
| | - Julio E Villalón-Reina
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Amanda L Rodrigue
- Department of Psychiatry and Behavioral Science, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Lachlan T Strike
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Queensland University of Technology, Brisbane, QLD, Australia
| | | | - Katie L McMahon
- Queensland University of Technology, Brisbane, QLD, Australia
| | - Margaret J Wright
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
- Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Queensland University of Technology, Brisbane, QLD, Australia
- School of Psychology, University of Queensland, Brisbane, QLD, Australia
| | - John Blangero
- Department of Human Genetics, University of Texas Rio Grande Valley, Brownsville, TX, USA
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - David C Glahn
- Department of Psychiatry and Behavioral Science, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA
| | - Peter Kochunov
- Faillace Department of Psychiatry and Behavioral Sciences at McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Douglas E Williamson
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
- Research, Durham VA Health Care System, Durham, NC, USA
| | - Asta K Håberg
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Department of MiDtT National Research Center, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Paul M Thompson
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
- Department of Biomedical Engineering, USC Viterbi School of Engineering, Los Angeles, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA.
- Department of Biomedical Engineering, USC Viterbi School of Engineering, Los Angeles, CA, USA.
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Xu X, Sun C, Yu H, Yan G, Zhu Q, Kong X, Pan Y, Xu H, Zheng T, Zhou C, Wang Y, Xiao J, Chen R, Li M, Zhang S, Hu H, Zou Y, Wang J, Wang G, Wu D. Site effects in multisite fetal brain MRI: morphological insights into early brain development. Eur Radiol 2025; 35:1830-1842. [PMID: 39299951 DOI: 10.1007/s00330-024-11084-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 06/06/2024] [Accepted: 08/26/2024] [Indexed: 09/22/2024]
Abstract
OBJECTIVE To evaluate multisite effects on fetal brain MRI. Specifically, to identify crucial acquisition factors affecting fetal brain structural measurements and developmental patterns, while assessing the effectiveness of existing harmonization methods in mitigating site effects. MATERIALS AND METHODS Between May 2017 and March 2022, T2-weighted fast spin-echo sequences in-utero MRI were performed on healthy fetuses from retrospectively recruited pregnant volunteers on four different scanners at four sites. A generalized additive model (GAM) was used to quantitatively assess site effects, including field strength (FS), manufacturer (M), in-plane resolution (R), and slice thickness (ST), on subcortical volume and cortical morphological measurements, including cortical thickness, curvature, and sulcal depth. Growth models were selected to elucidate the developmental trajectories of these morphological measurements. Welch's test was performed to evaluate the influence of site effects on developmental trajectories. The comBat-GAM harmonization method was applied to mitigate site-related biases. RESULTS The final analytic sample consisted of 340 MRI scans from 218 fetuses (mean GA, 30.1 weeks ± 4.4 [range, 21.7-40 weeks]). GAM results showed that lower FS and lower spatial resolution led to overestimations in selected brain regions of subcortical volumes and cortical morphological measurements. Only the peak cortical thickness in developmental trajectories was significantly influenced by the effects of FS and R. Notably, ComBat-GAM harmonization effectively removed site effects while preserving developmental patterns. CONCLUSION Our findings pinpointed the key acquisition factors in in-utero fetal brain MRI and underscored the necessity of data harmonization when pooling multisite data for fetal brain morphology investigations. KEY POINTS Question How do specific site MRI acquisition factors affect fetal brain imaging? Finding Lower FS and spatial resolution overestimated subcortical volumes and cortical measurements. Cortical thickness in developmental trajectories was influenced by FS and in-plane resolution. Clinical relevance This study provides important guidelines for the fetal MRI community when scanning fetal brains and underscores the necessity of data harmonization of cross-center fetal studies.
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Affiliation(s)
- Xinyi Xu
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Cong Sun
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Hong Yu
- Dalian Municipal Women and Children's Medical Center (Group), Dalian, China
| | - Guohui Yan
- Department of Radiology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Qingqing Zhu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xianglei Kong
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yibin Pan
- Department of Obstetrics and Gynecology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Reproductive Dysfunction Management of Zhejiang Province, Zhejiang Provincial Clinical Research Center for Obstetrics and Gynecology, Hangzhou, China
| | - Haoan Xu
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Tianshu Zheng
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Chi Zhou
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Yutian Wang
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Jiaxin Xiao
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
- School of Biomedical Engineering & Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Ruike Chen
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Mingyang Li
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Songying Zhang
- Department of Obstetrics and Gynecology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Reproductive Dysfunction Management of Zhejiang Province, Zhejiang Provincial Clinical Research Center for Obstetrics and Gynecology, Hangzhou, China
| | - Hongjie Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Yu Zou
- Department of Radiology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Jingshi Wang
- Dalian Municipal Women and Children's Medical Center (Group), Dalian, China.
| | - Guangbin Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
| | - Dan Wu
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.
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Lu T, Zhang Y, Lyzinski V, Bi C, Kochunov P, Hong E, Chen S. Evaluating the effects of high-throughput structural neuroimaging predictors on whole-brain functional connectome outcomes via network-based matrix-on-vector regression. Biometrics 2025; 81:ujaf027. [PMID: 40116280 PMCID: PMC11926586 DOI: 10.1093/biomtc/ujaf027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 12/04/2024] [Accepted: 02/22/2025] [Indexed: 03/23/2025]
Abstract
The joint analysis of multimodal neuroimaging data is vital in brain research, revealing complex interactions between brain structures and functions. Our study is motivated by the analysis of a vast dataset of brain functional connectivity (FC) and multimodal structural imaging (SI) features from the UK Biobank. Specifically, we aim to investigate the effects of SI features, such as white matter microstructure integrity (WMMI) and cortical thickness, on the whole-brain functional connectome network. This analysis is inherently challenging due to the extensive structural-functional associations and the intricate network patterns present in multimodal high-dimensional neuroimaging data. To bridge methodological gaps, we developed a novel multi-level sub-graph extraction method (dense bipartite with nested unipartite graph) within a matrix(network)-on-vector regression model. This method identifies subsets of spatially specific SI features that intensely and systematically influence FC sub-networks, while effectively suppressing false positives in large-scale datasets. Applying our method to a multimodal neuroimaging dataset of 4242 participants ffrom the UK Biobank, we evaluated the effects of whole-brain WMMI and cortical thickness on resting-state FC. Our findings indicate that the WMMI in corticospinal tracts and inferior cerebellar peduncle significantly affect functional connections of sensorimotor, salience, and executive sub-networks, with an average correlation of 0.81 ($p < 0.001$).
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Affiliation(s)
- Tong Lu
- Department of Mathematics, University of Maryland, College Park, MD 20742, United States
| | - Yuan Zhang
- Department of Statistics, The Ohio State University, Columbus, OH 43210, United States
| | - Vince Lyzinski
- Department of Mathematics, University of Maryland, College Park, MD 20742, United States
| | - Chuan Bi
- Maryland Psychiatric Research Center, School of Medicine, Department of Psychiatry, University of Maryland, Catonsville, MD 21228, United States
| | - Peter Kochunov
- Department of Psychiatry and Behavioral Science, University of Texas Health Science Center, Houston, TX 77021, United States
| | - Elliot Hong
- Department of Psychiatry and Behavioral Science, University of Texas Health Science Center, Houston, TX 77021, United States
| | - Shuo Chen
- Maryland Psychiatric Research Center, School of Medicine, Department of Psychiatry, University of Maryland, Catonsville, MD 21228, United States
- Division of Biostatistics and Bioinformatics, School of Medicine, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, MD 21201, United States
- University of Maryland Institute for Health Computing, Bethesda, MD 20852, United States
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4
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Quidé Y, Jahanshad N, Andoh J, Antoniou G, Apkarian AV, Ashar YK, Badran BW, Baird CL, Baxter L, Bell TR, Blanco-Hinojo L, Borckardt J, Cheung CL, Ciampi de Andrade D, Couto BA, Cox SR, Cruz-Almeida Y, Dannlowski U, De Martino E, de Tommaso M, Deus J, Domin M, Egorova-Brumley N, Elliott J, Fanton S, Fauchon C, Flor H, Franz CE, Gatt JM, Gerdhem P, Gilman JM, Gollub RL, Govind V, Graven-Nielsen T, Håkansson G, Hales T, Haswell C, Heukamp NJ, Hu L, Huang L, Hussain A, Jensen K, Kircher T, Kremen WS, Leehr EJ, Lindquist M, Loggia ML, Lotze M, Martucci KT, Meeker TJ, Meinert S, Millard SK, Morey RA, Murillo C, Nees F, Nenadic I, Park HR, Peng X, Ploner M, Pujol J, Robayo LE, Salan T, Seminowicz DA, Serian A, Slater R, Stein F, Stevens J, Strauss S, Sun D, Vachon-Presseau E, Valdes-Hernandez PA, Vanneste S, Vernon M, Verriotis M, Wager TD, Widerstrom-Noga E, Woodbury A, Zeidan F, Bhatt RR, Ching CR, Haddad E, Thomopoulos SI, Thompson PM, Gustin SM. ENIGMA-Chronic Pain: a worldwide initiative to identify brain correlates of chronic pain. Pain 2024; 165:2662-2666. [PMID: 39058957 PMCID: PMC11562752 DOI: 10.1097/j.pain.0000000000003317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Accepted: 05/20/2024] [Indexed: 07/28/2024]
Affiliation(s)
- Yann Quidé
- School of Psychology, The University of New South Wales (UNSW) Sydney, Sydney, NSW, Australia
- Centre for Pain IMPACT, Neuroscience Research Australia, Randwick, NSW, Australia
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Jamila Andoh
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Georgia Antoniou
- Division of Population Health and Genomics, Medical Research Institute, University of Dundee, Dundee, Scotland, United Kingdom
| | - Apkar Vania Apkarian
- Center for Translational Pain Research, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
- Department of Neuroscience, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
- Department of Anesthesiology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Yoni K. Ashar
- Department of General Internal Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Bashar W. Badran
- Department of Psychiatry and Behavioral Sciences, Neuro-X Lab, Medical University of South Carolina, Charleston, SC, United States
| | - C. Lexi Baird
- Duke-UNC Brain Imaging and Analysis Center, Duke University, Durham, NC, United States
- VA Mid-Atlantic MIRECC, Durham VA Medical Center, Durham VA, Durham, NC, United States
| | - Luke Baxter
- Department of Paediatrics, University of Oxford, Oxford, United Kingdom
| | - Tyler R. Bell
- Department of Psychiatry, University of California, San Diego, CA, United States
- Center for Behavior Genetics of Aging, University of California, San Diego, CA, United States
| | - Laura Blanco-Hinojo
- MRI Research Unit, Department of Radiology, Hospital del Mar, Barcelona, Spain
- IsGlobal, Barcelona, Spain
| | - Jeffrey Borckardt
- Department of Psychiatry and Behavioral Sciences, Neuro-X Lab, Medical University of South Carolina, Charleston, SC, United States
- Medical University of South Carolina, Charleston, SC, United States
- Ralph H. Johnson VAMC, Charleston, SC, United States
| | - Chloe L. Cheung
- Neuroscience Graduate Program, Schulich School of Medicine & Dentistry, University of Western Ontario, London, ON, Canada
| | - Daniel Ciampi de Andrade
- Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Bruno A. Couto
- Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Simon R. Cox
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, Scotland, United Kingdom
| | - Yenisel Cruz-Almeida
- Pain Research and Intervention Center of Excellence, University of Florida, Gainesville, FL, United States
- Department of Community Dentistry and Behavioral Sciences, College of Dentistry, University of Florida, Gainesville, FL, United States
- Department of Neuroscience, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Udo Dannlowski
- Institute of Translational Psychiatry, University of Münster, Münster, Germany
| | - Enrico De Martino
- Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Marina de Tommaso
- Neurophysiopathology Unit, DiBrain Department, Bari Aldo Moro University, Bari, Italy
| | - Joan Deus
- MRI Research Unit, Department of Radiology, Hospital del Mar, Barcelona, Spain
- Department of Clinical and Health Psychology, Autonomous University of Barcelona, Barcelona, Spain
| | - Martin Domin
- Functional Imaging Unit, Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Natalia Egorova-Brumley
- Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, VIC, Australia
| | - James Elliott
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
- Northern Sydney Local Health District, Sydney, NSW, Australia
- The Kolling Institute, St Leonards, NSW, Australia
| | - Silvia Fanton
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Camille Fauchon
- Neuro-Dol, Inserm, University Hospital of Clermont-Ferrand, University of Clermont-Auvergne, Clermont-Ferrand, France
- NEUROPAIN Team, CRNL, CNRS, Inserm, University of Saint-Etienne, Saint-Etienne, France
| | - Herta Flor
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Heidelberg University, Mannheim, Germany
| | - Carol E. Franz
- Department of Psychiatry, University of California, San Diego, CA, United States
- Center for Behavior Genetics of Aging, University of California, San Diego, CA, United States
| | - Justine M. Gatt
- School of Psychology, The University of New South Wales (UNSW) Sydney, Sydney, NSW, Australia
- Centre for Wellbeing, Resilience and Recovery, Neuroscience Research Australia, Randwick, NSW, Australia
- Black Dog Institute, Randwick, NSW, Australia
| | - Paul Gerdhem
- Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
- Department of Orthopaedics and Hand Surgery, Uppsala University Hospital, Uppsala, Sweden
| | - Jodi M. Gilman
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Center for Addiction Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Randy L. Gollub
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Varan Govind
- Department of Radiology, University of Miami, Miller School of Medicine, Miami, FL, United States
| | - Thomas Graven-Nielsen
- Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Gustaf Håkansson
- Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden
| | - Tim Hales
- Consortium Against Pain Inequality, University of Dundee, Dundee, Scotland, United Kingdom
| | - Courtney Haswell
- Duke-UNC Brain Imaging and Analysis Center, Duke University, Durham, NC, United States
- VA Mid-Atlantic MIRECC, Durham VA Medical Center, Durham VA, Durham, NC, United States
| | - Nils Jannik Heukamp
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig-Holstein, Kiel University, Kiel, Germany
| | - Li Hu
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Lejian Huang
- Center for Translational Pain Research, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
- Department of Neuroscience, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Ahmed Hussain
- Duke-UNC Brain Imaging and Analysis Center, Duke University, Durham, NC, United States
- VA Mid-Atlantic MIRECC, Durham VA Medical Center, Durham VA, Durham, NC, United States
| | - Karin Jensen
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - William S. Kremen
- Department of Psychiatry, University of California, San Diego, CA, United States
- Center for Behavior Genetics of Aging, University of California, San Diego, CA, United States
| | - Elisabeth J. Leehr
- Institute of Translational Psychiatry, University of Münster, Münster, Germany
| | - Martin Lindquist
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, United States
| | - Marco L. Loggia
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Department of Anesthesia, Clinical Care, and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Martin Lotze
- Functional Imaging Unit, Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Katherine T. Martucci
- Department of Anesthesiology, Center for Translational Pain Medicine, Duke University School of Medicine, Durham, NC, United States
| | - Timothy J. Meeker
- Department of Biology, Morgan State University, Baltimore, MD, United States
| | - Susanne Meinert
- Institute of Translational Psychiatry, University of Münster, Münster, Germany
- Institute for Translational Neuroscience, University of Münster, Münster, Germany
| | - Samantha K. Millard
- Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Rajendra A. Morey
- Duke-UNC Brain Imaging and Analysis Center, Duke University, Durham, NC, United States
- VA Mid-Atlantic MIRECC, Durham VA Medical Center, Durham VA, Durham, NC, United States
| | - Carlos Murillo
- Department of General Internal Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
- Department of Rehabilitation Sciences, Ghent University, Ghent, Belgium
| | - Frauke Nees
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig-Holstein, Kiel University, Kiel, Germany
| | - Igor Nenadic
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Haeme R.P. Park
- School of Psychology, The University of New South Wales (UNSW) Sydney, Sydney, NSW, Australia
- Centre for Wellbeing, Resilience and Recovery, Neuroscience Research Australia, Randwick, NSW, Australia
| | - Xiaolong Peng
- Department of Psychiatry and Behavioral Sciences, Neuro-X Lab, Medical University of South Carolina, Charleston, SC, United States
| | - Markus Ploner
- Department of Neurology, Center for Interdisciplinary Pain Medicine and TUM-Neuroimaging Center, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Jesus Pujol
- MRI Research Unit, Department of Radiology, Hospital del Mar, Barcelona, Spain
| | - Linda E. Robayo
- The Miami Project to Cure Paralysis, Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Teddy Salan
- Department of Radiology, University of Miami, Miller School of Medicine, Miami, FL, United States
| | - David A. Seminowicz
- Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada
| | - Angela Serian
- Department of Neurology, University Hospital Greifswald, Greifswald, Germany
| | - Rebeccah Slater
- Department of Paediatrics, University of Oxford, Oxford, United Kingdom
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Jennifer Stevens
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
- Atlanta Veterans Affairs Healthcare System, Atlanta, GA, United States
| | - Sebastian Strauss
- Department of Neurology, University Hospital Greifswald, Greifswald, Germany
| | - Delin Sun
- Duke-UNC Brain Imaging and Analysis Center, Duke University, Durham, NC, United States
- VA Mid-Atlantic MIRECC, Durham VA Medical Center, Durham VA, Durham, NC, United States
- Department of Psychiatry, School of Medicine, Duke University, Durham, NC, United States
| | - Etienne Vachon-Presseau
- Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, QC, Canada
- Department of Anesthesia, Faculty of Medicine, McGill University, Montreal, QC, Canada
- Alan Edwards Centre for Research on Pain (AECRP), McGill University, Montreal, QC, Canada
| | - Pedro A. Valdes-Hernandez
- Department of Community Dentistry and Behavioral Sciences, College of Dentistry, University of Florida, Gainesville, FL, United States
| | - Sven Vanneste
- School of Psychology, Trinity College Dublin, Dublin, Ireland
- Trinity Institute for Neuroscience, Trinity College Dublin, Dublin, Ireland
- Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Mark Vernon
- Atlanta Veterans Affairs Healthcare System, Atlanta, GA, United States
| | - Madeleine Verriotis
- Developmental Neurosciences Department, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
- Department of Anaesthesia and Pain Medicine, Great Ormond Street Hospital NHS Foundation Trust, London, United Kingdom
| | | | - Eva Widerstrom-Noga
- The Miami Project to Cure Paralysis, Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Anna Woodbury
- Atlanta Veterans Affairs Healthcare System, Atlanta, GA, United States
- Division of Pain Medicine, Department of Anesthesiology, Emory University School of Medicine, Atlanta, GA, United States
| | - Fadel Zeidan
- Center for Pain Medicine, Department of Anesthesiology, University of California San Diego, La Jolla, CA, United States
| | - Ravi R. Bhatt
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Christopher R.K. Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Elizabeth Haddad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Sophia I. Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Sylvia M. Gustin
- School of Psychology, The University of New South Wales (UNSW) Sydney, Sydney, NSW, Australia
- Centre for Pain IMPACT, Neuroscience Research Australia, Randwick, NSW, Australia
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5
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Yang B, Earnest T, Kumar S, Kothapalli D, Benzinger T, Gordon B, Sotiras A. Evaluation of ComBat Harmonization for Reducing Across-Tracer Differences in Regional Amyloid PET Analyses. Hum Brain Mapp 2024; 45:e70068. [PMID: 39540665 PMCID: PMC11561838 DOI: 10.1002/hbm.70068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2024] [Revised: 09/30/2024] [Accepted: 10/22/2024] [Indexed: 11/16/2024] Open
Abstract
Differences in amyloid positron emission tomography (PET) radiotracer pharmacokinetics and binding properties lead to discrepancies in amyloid-β uptake estimates. Harmonization of tracer-specific biases is crucial for optimal performance of downstream tasks. Here, we investigated the efficacy of ComBat, a data-driven harmonization model, for reducing tracer-specific biases in regional amyloid PET measurements from [18F]-florbetapir (FBP) and [11C]-Pittsburgh compound-B (PiB). One hundred thirteen head-to-head FBP-PiB scan pairs, scanned from the same subject within 90 days, were selected from the Open Access Series of Imaging Studies 3 (OASIS-3) dataset. The Centiloid scale, ComBat with no covariates, ComBat with biological covariates, and GAM-ComBat with biological covariates were used to harmonize both global and regional amyloid standardized uptake value ratios (SUVR). Variants of ComBat, including longitudinal ComBat and PEACE, were also tested. Intraclass correlation coefficient (ICC) and mean absolute error (MAE) were computed to measure the absolute agreement between tracers. Additionally, longitudinal amyloid SUVRs from an anti-amyloid drug trial were simulated using linear mixed effects modeling. Differences in rates-of-change between simulated treatment and placebo groups were tested, and change in statistical power/Type-I error after harmonization was quantified. In the head-to-head tracer comparison, ComBat with no covariates was the best at increasing ICC and decreasing MAE of both global summary and regional amyloid PET SUVRs between scan pairs of the same group of subjects. In the clinical trial simulation, harmonization with both Centiloid and ComBat increased statistical power of detecting true rate-of-change differences between groups and decreased false discovery rate in the absence of a treatment effect. The greatest benefit of harmonization was observed when groups exhibited differing FBP-to-PiB proportions. ComBat outperformed the Centiloid scale in harmonizing both global and regional amyloid estimates. Additionally, ComBat improved the detection of rate-of-change differences between clinical trial groups. Our findings suggest that ComBat is a viable alternative to Centiloid for harmonizing regional amyloid PET analyses.
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Affiliation(s)
- Braden Yang
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. LouisSt. LouisMissouriUSA
| | - Tom Earnest
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. LouisSt. LouisMissouriUSA
| | - Sayantan Kumar
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. LouisSt. LouisMissouriUSA
| | - Deydeep Kothapalli
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. LouisSt. LouisMissouriUSA
| | - Tammie Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. LouisSt. LouisMissouriUSA
| | - Brian Gordon
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. LouisSt. LouisMissouriUSA
| | - Aristeidis Sotiras
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. LouisSt. LouisMissouriUSA
- Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine in St. LouisSt. LouisMissouriUSA
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6
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Jahanshad N, Lenzini P, Bijsterbosch J. Current best practices and future opportunities for reproducible findings using large-scale neuroimaging in psychiatry. Neuropsychopharmacology 2024; 50:37-51. [PMID: 39117903 PMCID: PMC11526024 DOI: 10.1038/s41386-024-01938-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 06/05/2024] [Accepted: 07/09/2024] [Indexed: 08/10/2024]
Abstract
Research into the brain basis of psychopathology is challenging due to the heterogeneity of psychiatric disorders, extensive comorbidities, underdiagnosis or overdiagnosis, multifaceted interactions with genetics and life experiences, and the highly multivariate nature of neural correlates. Therefore, increasingly larger datasets that measure more variables in larger cohorts are needed to gain insights. In this review, we present current "best practice" approaches for using existing databases, collecting and sharing new repositories for big data analyses, and future directions for big data in neuroimaging and psychiatry with an emphasis on contributing to collaborative efforts and the challenges of multi-study data analysis.
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Affiliation(s)
- Neda Jahanshad
- Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, 90292, USA.
| | - Petra Lenzini
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, 63110, USA
| | - Janine Bijsterbosch
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, 63110, USA.
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7
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Yang B, Earnest T, Kumar S, Kothapalli D, Benzinger T, Gordon B, Sotiras A. Evaluation of ComBat harmonization for reducing across-tracer differences in regional amyloid PET analyses. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.14.24308952. [PMID: 38947044 PMCID: PMC11213066 DOI: 10.1101/2024.06.14.24308952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Introduction Differences in amyloid positron emission tomography (PET) radiotracer pharmacokinetics and binding properties lead to discrepancies in amyloid-β uptake estimates. Harmonization of tracer-specific biases is crucial for optimal performance of downstream tasks. Here, we investigated the efficacy of ComBat, a data-driven harmonization model, for reducing tracer-specific biases in regional amyloid PET measurements from [18F]-florbetapir (FBP) and [11C]-Pittsburgh Compound-B (PiB). Methods One-hundred-thirteen head-to-head FBP-PiB scan pairs, scanned from the same subject within ninety days, were selected from the Open Access Series of Imaging Studies 3 (OASIS-3) dataset. The Centiloid scale, ComBat with no covariates, ComBat with biological covariates, and GAM-ComBat with biological covariates were used to harmonize both global and regional amyloid standardized uptake value ratios (SUVR). Variants of ComBat, including longitudinal ComBat and PEACE, were also tested. Intraclass correlation coefficient (ICC) and mean absolute error (MAE) were computed to measure the absolute agreement between tracers. Additionally, longitudinal amyloid SUVRs from an anti-amyloid drug trial were simulated using linear mixed effects modeling. Differences in rates-of-change between simulated treatment and placebo groups were tested, and change in statistical power/Type-I error after harmonization was quantified. Results In the head-to-head tracer comparison, ComBat with no covariates was the best at increasing ICC and decreasing MAE of both global summary and regional amyloid PET SUVRs between scan pairs of the same group of subjects. In the clinical trial simulation, harmonization with both Centiloid and ComBat increased statistical power of detecting true rate-of-change differences between groups and decreased false discovery rate in the absence of a treatment effect. The greatest benefit of harmonization was observed when groups exhibited differing FBP-to-PiB proportions. Conclusion ComBat outperformed the Centiloid scale in harmonizing both global and regional amyloid estimates. Additionally, ComBat improved the detection of rate-of-change differences between clinical trial groups. Our findings suggest that ComBat is a viable alternative to Centiloid for harmonizing regional amyloid PET analyses.
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Affiliation(s)
- Braden Yang
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
| | - Tom Earnest
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
| | - Sayantan Kumar
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
| | - Deydeep Kothapalli
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
| | - Tammie Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
| | - Brian Gordon
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
| | - Aristeidis Sotiras
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
- Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
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8
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Patel J, Schöttner M, Tarun A, Tourbier S, Alemán-Gómez Y, Hagmann P, Bolton TAW. Modeling the impact of MRI acquisition bias on structural connectomes: Harmonizing structural connectomes. Netw Neurosci 2024; 8:623-652. [PMID: 39355442 PMCID: PMC11340995 DOI: 10.1162/netn_a_00368] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 02/26/2024] [Indexed: 10/03/2024] Open
Abstract
One way to increase the statistical power and generalizability of neuroimaging studies is to collect data at multiple sites or merge multiple cohorts. However, this usually comes with site-related biases due to the heterogeneity of scanners and acquisition parameters, negatively impacting sensitivity. Brain structural connectomes are not an exception: Being derived from T1-weighted and diffusion-weighted magnetic resonance images, structural connectivity is impacted by differences in imaging protocol. Beyond minimizing acquisition parameter differences, removing bias with postprocessing is essential. In this work we create, from the exhaustive Human Connectome Project Young Adult dataset, a resampled dataset of different b-values and spatial resolutions, modeling a cohort scanned across multiple sites. After demonstrating the statistical impact of acquisition parameters on connectivity, we propose a linear regression with explicit modeling of b-value and spatial resolution, and validate its performance on separate datasets. We show that b-value and spatial resolution affect connectivity in different ways and that acquisition bias can be reduced using a linear regression informed by the acquisition parameters while retaining interindividual differences and hence boosting fingerprinting performance. We also demonstrate the generative potential of our model, and its generalization capability in an independent dataset reflective of typical acquisition practices in clinical settings.
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Affiliation(s)
- Jagruti Patel
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Mikkel Schöttner
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Anjali Tarun
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Sebastien Tourbier
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Yasser Alemán-Gómez
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Patric Hagmann
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Thomas A W Bolton
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
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9
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Roelofs EF, Bas-Hoogendam JM, Winkler AM, van der Wee NJ, Vermeiren RRM. Longitudinal development of resting-state functional connectivity in adolescents with and without internalizing disorders. NEUROSCIENCE APPLIED 2024; 3:104090. [PMID: 39634556 PMCID: PMC11615185 DOI: 10.1016/j.nsa.2024.104090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/07/2024]
Abstract
Longitudinal studies using resting-state functional magnetic resonance imaging (rs-fMRI) focused on adolescent internalizing psychopathology are scarce and have mostly investigated standardized treatment effects on functional connectivity (FC) of the full amygdala. The role of amygdala subregions and large resting-state networks had yet to be elucidated, and treatment is in practice often personalized. Here, longitudinal FC development of amygdala subregions and whole-brain networks are investigated in a clinically representative sample. Treatment-naïve adolescents with clinical depression and comorbid anxiety who started care-as-usual (n = 23; INT) and healthy controls (n = 24; HC) participated in rs-fMRI scans and questionnaires at baseline (before treatment) and after three months. Changes between and within groups over time in FC of the laterobasal amygdala (LBA), centromedial amygdala (CMA) and whole-brain networks derived from independent component analysis (ICA) were investigated. Groups differed significantly in FC development of the right LBA to the postcentral gyrus and the left LBA to the frontal pole. Within INT, FC to the frontal pole and postcentral gyrus changed over time while changes in FC of the right LBA were also linked to symptom change. No significant interactions were observed when considering FC from CMA bilateral seeds or within ICA-derived networks. Results in this cohort suggest divergent longitudinal development of FC from bilateral LBA subregions in adolescents with internalizing disorders compared to healthy peers, possibly reflecting nonspecific treatment effects. Moreover, associations were found with symptom change. These results highlight the importance of differentiation of amygdala subregions in neuroimaging research in adolescents.
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Affiliation(s)
- Eline F. Roelofs
- LUMC-Curium, Department of Child and Adolescent Psychiatry, Leiden University Medical Center, Leiden, the Netherlands
- Department of Psychiatry, Leiden University Medical Center, Leiden, the Netherlands
- Leiden Institute for Brain and Cognition, Leiden, the Netherlands
| | - Janna Marie Bas-Hoogendam
- Department of Psychiatry, Leiden University Medical Center, Leiden, the Netherlands
- Leiden Institute for Brain and Cognition, Leiden, the Netherlands
- Developmental and Educational Psychology, Institute of Psychology, Leiden University, Leiden, the Netherlands
| | - Anderson M. Winkler
- Section on Development and Affective Neuroscience (SDAN), Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
- Division of Human Genetics, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, United States
| | - Nic J.A. van der Wee
- Department of Psychiatry, Leiden University Medical Center, Leiden, the Netherlands
- Leiden Institute for Brain and Cognition, Leiden, the Netherlands
| | - Robert R.J. M. Vermeiren
- LUMC-Curium, Department of Child and Adolescent Psychiatry, Leiden University Medical Center, Leiden, the Netherlands
- Leiden Institute for Brain and Cognition, Leiden, the Netherlands
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10
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Gardner M, Shinohara RT, Bethlehem RAI, Romero-Garcia R, Warrier V, Dorfschmidt L, Shanmugan S, Thompson P, Seidlitz J, Alexander-Bloch AF, Chen AA. ComBatLS: A location- and scale-preserving method for multi-site image harmonization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.21.599875. [PMID: 39131292 PMCID: PMC11312440 DOI: 10.1101/2024.06.21.599875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Recent work has leveraged massive datasets and advanced harmonization methods to construct normative models of neuroanatomical features and benchmark individuals' morphology. However, current harmonization tools do not preserve the effects of biological covariates including sex and age on features' variances; this failure may induce error in normative scores, particularly when such factors are distributed unequally across sites. Here, we introduce a new extension of the popular ComBat harmonization method, ComBatLS, that preserves biological variance in features' locations and scales. We use UK Biobank data to show that ComBatLS robustly replicates individuals' normative scores better than other ComBat methods when subjects are assigned to sex-imbalanced synthetic "sites". Additionally, we demonstrate that ComBatLS significantly reduces sex biases in normative scores compared to traditional methods. Finally, we show that ComBatLS successfully harmonizes consortium data collected across over 50 studies. R implementation of ComBatLS is available at https://github.com/andy1764/ComBatFamily.
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Affiliation(s)
- Margaret Gardner
- Brain-Gene-Development Lab, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Neuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Center for Biomedical Imaging Computing and Analytics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, USA
| | | | - Rafael Romero-Garcia
- Instituto de Biomedicina de Sevilla (IBiS) HUVR/CSIC/Universidad de Sevilla/CIBERSAM, ISCIII, Dpto. de Fisiología Médica y Biofísica, Seville, ES
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Varun Warrier
- Department of Psychology, University of Cambridge, Cambridge, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Lena Dorfschmidt
- Brain-Gene-Development Lab, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Sheila Shanmugan
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA
| | - Paul Thompson
- Imaging Genetics Center, Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jakob Seidlitz
- Brain-Gene-Development Lab, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Aaron F Alexander-Bloch
- Brain-Gene-Development Lab, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Andrew A Chen
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA
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11
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Conte S, Zimmerman D, Richards JE. White matter trajectories over the lifespan. PLoS One 2024; 19:e0301520. [PMID: 38758830 PMCID: PMC11101104 DOI: 10.1371/journal.pone.0301520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 03/14/2024] [Indexed: 05/19/2024] Open
Abstract
White matter (WM) changes occur throughout the lifespan at a different rate for each developmental period. We aggregated 10879 structural MRIs and 6186 diffusion-weighted MRIs from participants between 2 weeks to 100 years of age. Age-related changes in gray matter and WM partial volumes and microstructural WM properties, both brain-wide and on 29 reconstructed tracts, were investigated as a function of biological sex and hemisphere, when appropriate. We investigated the curve fit that would best explain age-related differences by fitting linear, cubic, quadratic, and exponential models to macro and microstructural WM properties. Following the first steep increase in WM volume during infancy and childhood, the rate of development slows down in adulthood and decreases with aging. Similarly, microstructural properties of WM, particularly fractional anisotropy (FA) and mean diffusivity (MD), follow independent rates of change across the lifespan. The overall increase in FA and decrease in MD are modulated by demographic factors, such as the participant's age, and show different hemispheric asymmetries in some association tracts reconstructed via probabilistic tractography. All changes in WM macro and microstructure seem to follow nonlinear trajectories, which also differ based on the considered metric. Exponential changes occurred for the WM volume and FA and MD values in the first five years of life. Collectively, these results provide novel insight into how changes in different metrics of WM occur when a lifespan approach is considered.
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Affiliation(s)
- Stefania Conte
- Department of Psychology, State University of New York at Binghamton, Vestal, NY, United States of America
| | - Dabriel Zimmerman
- Department of Biomedical Engineering, Boston University, Boston, MA, United States of America
| | - John E. Richards
- Department of Psychology, University of South Carolina, Columbia, SC, United States of America
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12
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Zhu AH, Nir TM, Javid S, Villalon-Reina JE, Rodrigue AL, Strike LT, de Zubicaray GI, McMahon KL, Wright MJ, Medland SE, Blangero J, Glahn DC, Kochunov P, Håberg AK, Thompson PM, Jahanshad N. Lifespan reference curves for harmonizing multi-site regional brain white matter metrics from diffusion MRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.22.581646. [PMID: 38463962 PMCID: PMC10925090 DOI: 10.1101/2024.02.22.581646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Age-related white matter (WM) microstructure maturation and decline occur throughout the human lifespan, complementing the process of gray matter development and degeneration. Here, we create normative lifespan reference curves for global and regional WM microstructure by harmonizing diffusion MRI (dMRI)-derived data from ten public datasets (N = 40,898 subjects; age: 3-95 years; 47.6% male). We tested three harmonization methods on regional diffusion tensor imaging (DTI) based fractional anisotropy (FA), a metric of WM microstructure, extracted using the ENIGMA-DTI pipeline. ComBat-GAM harmonization provided multi-study trajectories most consistent with known WM maturation peaks. Lifespan FA reference curves were validated with test-retest data and used to assess the effect of the ApoE4 risk factor for dementia in WM across the lifespan. We found significant associations between ApoE4 and FA in WM regions associated with neurodegenerative disease even in healthy individuals across the lifespan, with regional age-by-genotype interactions. Our lifespan reference curves and tools to harmonize new dMRI data to the curves are publicly available as eHarmonize (https://github.com/ahzhu/eharmonize).
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Affiliation(s)
- Alyssa H Zhu
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
- Department of Biomedical Engineering, USC Viterbi School of Engineering, Los Angeles, CA, USA
| | - Talia M Nir
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Shayan Javid
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
- Department of Biomedical Engineering, USC Viterbi School of Engineering, Los Angeles, CA, USA
| | - Julio E Villalon-Reina
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Amanda L Rodrigue
- Department of Psychiatry and Behavioral Science, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Lachlan T Strike
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Queensland University of Technology, Brisbane, QLD, Australia
| | | | - Katie L McMahon
- Queensland University of Technology, Brisbane, QLD, Australia
| | - Margaret J Wright
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
- Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Queensland University of Technology, Brisbane, QLD, Australia
- School of Psychology, `, Brisbane, QLD, Australia
| | - John Blangero
- Department of Human Genetics, University of Texas Rio Grande Valley, Brownsville, TX, USA
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - David C Glahn
- Department of Psychiatry and Behavioral Science, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA
| | - Peter Kochunov
- Faillace Department of Psychiatry and Behavioral Sciences at McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Asta K Håberg
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Department of MiDtT National Research Center, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Paul M Thompson
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
- Department of Biomedical Engineering, USC Viterbi School of Engineering, Los Angeles, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
- Department of Biomedical Engineering, USC Viterbi School of Engineering, Los Angeles, CA, USA
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13
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Cheng C, Messerschmidt L, Bravo I, Waldbauer M, Bhavikatti R, Schenk C, Grujic V, Model T, Kubinec R, Barceló J. A General Primer for Data Harmonization. Sci Data 2024; 11:152. [PMID: 38297013 PMCID: PMC10831085 DOI: 10.1038/s41597-024-02956-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 01/11/2024] [Indexed: 02/02/2024] Open
Affiliation(s)
- Cindy Cheng
- Hochschule für Politik, Technical University of Munich, Richard-Wagner Str. 1, Munich, 80333, Bavaria, Germany.
| | - Luca Messerschmidt
- Hochschule für Politik, Technical University of Munich, Richard-Wagner Str. 1, Munich, 80333, Bavaria, Germany
| | - Isaac Bravo
- Hochschule für Politik, Technical University of Munich, Richard-Wagner Str. 1, Munich, 80333, Bavaria, Germany
| | - Marco Waldbauer
- Hochschule für Politik, Technical University of Munich, Richard-Wagner Str. 1, Munich, 80333, Bavaria, Germany
| | | | - Caress Schenk
- School of Humanities and Social Sciences, Nazarbayev University, Kabanbay Batry Ave., 53, Astana, 010000, Kazakhstan
| | - Vanja Grujic
- Faculty of Law, University of Brasilia, Campus Universitário Darcy Ribeiro Asa Norte, Brasília, 10587, Brazil
| | - Tim Model
- Delve, 2225 3rd St, San Francisco, 94107, California, USA
| | - Robert Kubinec
- Division of Social Science, New York University Abu Dhabi, Social Science Building (A5), Abu Dhabi, 129188, United Arab Emirates
| | - Joan Barceló
- Division of Social Science, New York University Abu Dhabi, Social Science Building (A5), Abu Dhabi, 129188, United Arab Emirates
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14
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Suarez-Jimenez B, Lazarov A, Zhu X, Zilcha-Mano S, Kim Y, Marino CE, Rjabtsenkov P, Bavdekar SY, Pine DS, Bar-Haim Y, Larson CL, Huggins AA, Terri deRoon-Cassini, Tomas C, Fitzgerald J, Kennis M, Varkevisser T, Geuze E, Quidé Y, El Hage W, Wang X, O’Leary EN, Cotton AS, Xie H, Shih C, Disner SG, Davenport ND, Sponheim SR, Koch SB, Frijling JL, Nawijn L, van Zuiden M, Olff M, Veltman DJ, Gordon EM, May G, Nelson SM, Jia-Richards M, Neria Y, Morey RA. Intrusive Traumatic Re-Experiencing Domain: Functional Connectivity Feature Classification by the ENIGMA PTSD Consortium. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:299-307. [PMID: 38298781 PMCID: PMC10829610 DOI: 10.1016/j.bpsgos.2023.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 04/12/2023] [Accepted: 05/22/2023] [Indexed: 02/02/2024] Open
Abstract
Background Intrusive traumatic re-experiencing domain (ITRED) was recently introduced as a novel perspective on posttraumatic psychopathology, proposing to focus research of posttraumatic stress disorder (PTSD) on the unique symptoms of intrusive and involuntary re-experiencing of the trauma, namely, intrusive memories, nightmares, and flashbacks. The aim of the present study was to explore ITRED from a neural network connectivity perspective. Methods Data were collected from 9 sites taking part in the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) PTSD Consortium (n= 584) and included itemized PTSD symptom scores and resting-state functional connectivity (rsFC) data. We assessed the utility of rsFC in classifying PTSD, ITRED-only (no PTSD diagnosis), and trauma-exposed (TE)-only (no PTSD or ITRED) groups using a machine learning approach, examining well-known networks implicated in PTSD. A random forest classification model was built on a training set using cross-validation, and the averaged cross-validation model performance for classification was evaluated using the area under the curve. The model was tested using a fully independent portion of the data (test dataset), and the test area under the curve was evaluated. Results rsFC signatures differentiated TE-only participants from PTSD and ITRED-only participants at about 60% accuracy. Conversely, rsFC signatures did not differentiate PTSD from ITRED-only individuals (45% accuracy). Common features differentiating TE-only participants from PTSD and ITRED-only participants mainly involved default mode network-related pathways. Some unique features, such as connectivity within the frontoparietal network, differentiated TE-only participants from one group (PTSD or ITRED-only) but to a lesser extent from the other group. Conclusions Neural network connectivity supports ITRED as a novel neurobiologically based approach to classifying posttrauma psychopathology.
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Affiliation(s)
- Benjamin Suarez-Jimenez
- Del Monte Institute for Neuroscience, Department of Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester, New York
| | - Amit Lazarov
- Department of Clinical Psychology, School of Psychological Sciences, Tel-Aviv University, Tel-Aviv, Israel
- Department of Psychiatry, Columbia University Irving Medical Center and New York State Psychiatric Institute, New York, New York
| | - Xi Zhu
- Department of Psychiatry, Columbia University Irving Medical Center and New York State Psychiatric Institute, New York, New York
| | - Sigal Zilcha-Mano
- Department of Psychology, University of Haifa, Mount Carmel, Haifa, Israel
| | - Yoojean Kim
- Department of Psychiatry, New York State Psychiatric Institute, New York, New York
| | - Claire E. Marino
- Del Monte Institute for Neuroscience, Department of Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester, New York
| | - Pavel Rjabtsenkov
- Del Monte Institute for Neuroscience, Department of Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester, New York
| | - Shreya Y. Bavdekar
- Del Monte Institute for Neuroscience, Department of Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester, New York
| | - Daniel S. Pine
- Section on Developmental Affective Neuroscience, National Institute of Mental Health, Bethesda, Maryland
| | - Yair Bar-Haim
- Department of Clinical Psychology, School of Psychological Sciences, Tel-Aviv University, Tel-Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | | | | | | | | | | | - Mitzy Kennis
- Brain Research and Innovation Centre, Ministry of Defence, Utrecht, the Netherlands
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Tim Varkevisser
- Brain Research and Innovation Centre, Ministry of Defence, Utrecht, the Netherlands
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Elbert Geuze
- Brain Research and Innovation Centre, Ministry of Defence, Utrecht, the Netherlands
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Yann Quidé
- School of Psychology, University of New South Wales Sydney, Sydney, New South Wales, Australia
- Neuroscience Research Australia, Randwick, New South Wales, Australia
| | - Wissam El Hage
- Unité Mixte de Recherche 1253, Institut National de la Santé et de la Recherche Médicale, Université de Tours, Tours, France
- Centre d'investigation Clinique 1415, Institut National de la Santé et de la Recherche Médicale, Centre Hospitalier Régional Universitaire de Tours, Tours, France
| | - Xin Wang
- University of Toledo, Toledo, Ohio
| | | | | | - Hong Xie
- University of Toledo, Toledo, Ohio
| | | | - Seth G. Disner
- Minneapolis VA Health Care System, Minneapolis, Minnesota
| | | | | | - Saskia B.J. Koch
- Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, the Netherlands
| | - Jessie L. Frijling
- Department of Psychiatry, Amsterdam UMC location University of Amsterdam, Amsterdam, the Netherlands
| | - Laura Nawijn
- Department of Psychiatry, Amsterdam UMC location University of Amsterdam, Amsterdam, the Netherlands
- Department of Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Mirjam van Zuiden
- Department of Psychiatry, Amsterdam UMC location University of Amsterdam, Amsterdam, the Netherlands
| | - Miranda Olff
- Department of Psychiatry, Amsterdam UMC location University of Amsterdam, Amsterdam, the Netherlands
- ARQ National Psychotrauma Centre, Diemen, the Netherlands
| | - Dick J. Veltman
- Department of Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Evan M. Gordon
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, Missouri
| | - Geoffery May
- VISN 17 Center of Excellence for Research on Returning War Veterans, U.S. Department of Veterans Affairs, Waco, Texas
| | - Steven M. Nelson
- Department of Pediatrics, University of Minnesota, Minneapolis, Minnesota
| | | | - Yuval Neria
- Department of Psychiatry, Columbia University Irving Medical Center and New York State Psychiatric Institute, New York, New York
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15
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Ge R, Ching CRK, Bassett AS, Kushan L, Antshel KM, van Amelsvoort T, Bakker G, Butcher NJ, Campbell LE, Chow EWC, Craig M, Crossley NA, Cunningham A, Daly E, Doherty JL, Durdle CA, Emanuel BS, Fiksinski A, Forsyth JK, Fremont W, Goodrich‐Hunsaker NJ, Gudbrandsen M, Gur RE, Jalbrzikowski M, Kates WR, Lin A, Linden DEJ, McCabe KL, McDonald‐McGinn D, Moss H, Murphy DG, Murphy KC, Owen MJ, Villalon‐Reina JE, Repetto GM, Roalf DR, Ruparel K, Schmitt JE, Schuite‐Koops S, Angkustsiri K, Sun D, Vajdi A, van den Bree M, Vorstman J, Thompson PM, Vila‐Rodriguez F, Bearden CE. Source-based morphometry reveals structural brain pattern abnormalities in 22q11.2 deletion syndrome. Hum Brain Mapp 2024; 45:e26553. [PMID: 38224541 PMCID: PMC10785196 DOI: 10.1002/hbm.26553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 11/12/2023] [Accepted: 11/19/2023] [Indexed: 01/17/2024] Open
Abstract
22q11.2 deletion syndrome (22q11DS) is the most frequently occurring microdeletion in humans. It is associated with a significant impact on brain structure, including prominent reductions in gray matter volume (GMV), and neuropsychiatric manifestations, including cognitive impairment and psychosis. It is unclear whether GMV alterations in 22q11DS occur according to distinct structural patterns. Then, 783 participants (470 with 22q11DS: 51% females, mean age [SD] 18.2 [9.2]; and 313 typically developing [TD] controls: 46% females, mean age 18.0 [8.6]) from 13 datasets were included in the present study. We segmented structural T1-weighted brain MRI scans and extracted GMV images, which were then utilized in a novel source-based morphometry (SBM) pipeline (SS-Detect) to generate structural brain patterns (SBPs) that capture co-varying GMV. We investigated the impact of the 22q11.2 deletion, deletion size, intelligence quotient, and psychosis on the SBPs. Seventeen GMV-SBPs were derived, which provided spatial patterns of GMV covariance associated with a quantitative metric (i.e., loading score) for analysis. Patterns of topographically widespread differences in GMV covariance, including the cerebellum, discriminated individuals with 22q11DS from healthy controls. The spatial extents of the SBPs that revealed disparities between individuals with 22q11DS and controls were consistent with the findings of the univariate voxel-based morphometry analysis. Larger deletion size was associated with significantly lower GMV in frontal and occipital SBPs; however, history of psychosis did not show a strong relationship with these covariance patterns. 22q11DS is associated with distinct structural abnormalities captured by topographical GMV covariance patterns that include the cerebellum. Findings indicate that structural anomalies in 22q11DS manifest in a nonrandom manner and in distinct covarying anatomical patterns, rather than a diffuse global process. These SBP abnormalities converge with previously reported cortical surface area abnormalities, suggesting disturbances of early neurodevelopment as the most likely underlying mechanism.
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Affiliation(s)
- Ruiyang Ge
- Department of PsychiatryUniversity of British ColumbiaVancouverBritish ColumbiaCanada
- Djavad Mowafaghian Centre for Brain HealthUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | | | - Anne S. Bassett
- Clinical Genetics Research ProgramCentre for Addiction and Mental HealthTorontoOntarioCanada
- The Dalglish Family 22q Clinic, Department of Psychiatry and Division of Cardiology, Department of Medicine, and Toronto General Hospital Research InstituteUniversity Health NetworkTorontoOntarioCanada
- Campbell Family Mental Health Research InstituteCentre for Addiction and Mental HealthTorontoOntarioCanada
- Department of PsychiatryUniversity of TorontoTorontoOntarioCanada
| | - Leila Kushan
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human BehaviorUniversity of California, Los AngelesLos AngelesCaliforniaUSA
| | | | | | - Geor Bakker
- Department of Psychiatry and NeuropsychologyMaastricht UniversityMaastrichtNetherlands
| | - Nancy J. Butcher
- Department of PsychiatryUniversity of TorontoTorontoOntarioCanada
- Child Health Evaluative SciencesThe Hospital for Sick ChildrenTorontoOntarioCanada
| | | | - Eva W. C. Chow
- Clinical Genetics Research ProgramCentre for Addiction and Mental HealthTorontoOntarioCanada
- Department of PsychiatryUniversity of TorontoTorontoOntarioCanada
| | - Michael Craig
- Sackler Institute for Translational Neurodevelopment and Department of Forensic and Neurodevelopmental Sciences, King's College LondonInstitute of Psychiatry, Psychology and NeuroscienceLondonUK
- National Autism UnitBethlem Royal HospitalBeckenhamUK
| | - Nicolas A. Crossley
- Department of PsychiatryPontificia Universidad Catolica de ChileSantiagoChile
| | - Adam Cunningham
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical NeurosciencesCardiff UniversityCardiffUK
| | - Eileen Daly
- Sackler Institute for Translational Neurodevelopment and Department of Forensic and Neurodevelopmental Sciences, King's College LondonInstitute of Psychiatry, Psychology and NeuroscienceLondonUK
| | - Joanne L. Doherty
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical NeurosciencesCardiff UniversityCardiffUK
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUK
| | - Courtney A. Durdle
- Department of PediatricsUC Davis MIND InstituteDavisCaliforniaUSA
- Department of Psychological and Brain SciencesUC Santa BarbaraSanta BarbaraCaliforniaUSA
| | - Beverly S. Emanuel
- Division of Human GeneticsThe Children's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
- Department of Pediatrics, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Ania Fiksinski
- Department of Psychology and Department of Pediatrics, Wilhelmina Children's HospitalUniversity Medical Center UtrechtUtrechtNetherlands
- Department of Psychiatry and Neuropsychology, Division of Mental Health, MHeNSMaastricht UniversityMaastrichtNetherlands
| | - Jennifer K. Forsyth
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human BehaviorUniversity of California, Los AngelesLos AngelesCaliforniaUSA
- Department of PsychologyUniversity of WashingtonSeattleWashingtonUSA
| | - Wanda Fremont
- Department of Psychiatry and Behavioral Sciences State University of New YorkUpstate Medical University SyracuseNew YorkUSA
| | - Naomi J. Goodrich‐Hunsaker
- Department of PediatricsUC Davis MIND InstituteDavisCaliforniaUSA
- Department of NeurologyUniversity of UtahSalt Lake CityUtahUSA
| | - Maria Gudbrandsen
- Sackler Institute for Translational Neurodevelopment and Department of Forensic and Neurodevelopmental Sciences, King's College LondonInstitute of Psychiatry, Psychology and NeuroscienceLondonUK
- Centre for Research in Psychological Wellbeing (CREW), School of PsychologyUniversity of RoehamptonLondonUK
| | - Raquel E. Gur
- Department of Psychiatry, Perelman School of MedicineUniversity of Pennsylvania and Children's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
| | - Maria Jalbrzikowski
- Department of PsychiatryHarvard Medical SchoolBostonMassachusettsUSA
- Department of Psychiatry and Behavioral SciencesBoston Children's HospitalBostonMassachusettsUSA
| | - Wendy R. Kates
- Department of Psychiatry and Behavioral Sciences State University of New YorkUpstate Medical University SyracuseNew YorkUSA
| | - Amy Lin
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human BehaviorUniversity of California, Los AngelesLos AngelesCaliforniaUSA
- Graduate Interdepartmental Program in NeuroscienceUCLA School of MedicineLos AngelesCaliforniaUSA
| | - David E. J. Linden
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical NeurosciencesCardiff UniversityCardiffUK
| | - Kathryn L. McCabe
- School of PsychologyUniversity of NewcastleCallaghanAustralia
- Department of PediatricsUC Davis MIND InstituteDavisCaliforniaUSA
| | - Donna McDonald‐McGinn
- Department of Pediatrics, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- 22q and You Center, Clinical Genetics Center, and Division of Human GeneticsThe Children's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
- Department of Human Biology and Medical GeneticsSapienza UniversityRomeItaly
| | - Hayley Moss
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical NeurosciencesCardiff UniversityCardiffUK
| | - Declan G. Murphy
- Sackler Institute for Translational Neurodevelopment and Department of Forensic and Neurodevelopmental Sciences, King's College LondonInstitute of Psychiatry, Psychology and NeuroscienceLondonUK
- Behavioural Genetics Clinic, Adult Autism Service, Behavioural and Developmental Psychiatry Clinical Academic GroupSouth London and Maudsley Foundation NHS TrustLondonUK
| | - Kieran C. Murphy
- Department of PsychiatryRoyal College of Surgeons in IrelandDublinIreland
| | - Michael J. Owen
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical NeurosciencesCardiff UniversityCardiffUK
| | | | - Gabriela M. Repetto
- Centro de Genetica y Genomica, Facultad de MedicinaClinica Alemana Universidad del DesarrolloSantiagoChile
| | - David R. Roalf
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Kosha Ruparel
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - J. Eric Schmitt
- Department of Radiology and PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Sanne Schuite‐Koops
- Department of PsychiatryUniversity Medical Center Groningen, Rijksuniversiteit GroningenGroningenNetherlands
| | | | - Daqiang Sun
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human BehaviorUniversity of California, Los AngelesLos AngelesCaliforniaUSA
| | - Ariana Vajdi
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human BehaviorUniversity of California, Los AngelesLos AngelesCaliforniaUSA
- Kaiser Permanente Bernard J. Tyson School of Medicine PasadenaCaliforniaUSA
| | - Marianne van den Bree
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical NeurosciencesCardiff UniversityCardiffUK
| | - Jacob Vorstman
- Department of PsychiatryUniversity of TorontoTorontoOntarioCanada
- Program in Genetics and Genome Biology, Research Institute, and Department of PsychiatryThe Hospital for Sick ChildrenTorontoOntarioCanada
| | - Paul M. Thompson
- Departments of Neurology, Psychiatry, Radiology, Engineering, Pediatrics and OphthalmologyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Fidel Vila‐Rodriguez
- Department of PsychiatryUniversity of British ColumbiaVancouverBritish ColumbiaCanada
- Djavad Mowafaghian Centre for Brain HealthUniversity of British ColumbiaVancouverBritish ColumbiaCanada
- School of Biomedical Engineering University of British Columbia VancouverBritish ColumbiaCanada
| | - Carrie E. Bearden
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human BehaviorUniversity of California, Los AngelesLos AngelesCaliforniaUSA
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16
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Shiohama T, Maikusa N, Kawaguchi M, Natsume J, Hirano Y, Saito K, Takanashi JI, Levman J, Takahashi E, Matsumoto K, Yokota H, Hattori S, Tsujimura K, Sawada D, Uchida T, Takatani T, Fujii K, Naganawa S, Sato N, Hamada H. A Brain Morphometry Study with Across-Site Harmonization Using a ComBat-Generalized Additive Model in Children and Adolescents. Diagnostics (Basel) 2023; 13:2774. [PMID: 37685313 PMCID: PMC10487204 DOI: 10.3390/diagnostics13172774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 08/23/2023] [Accepted: 08/25/2023] [Indexed: 09/10/2023] Open
Abstract
Regional anatomical structures of the brain are intimately connected to functions corresponding to specific regions and the temporospatial pattern of genetic expression and their functions from the fetal period to old age. Therefore, quantitative brain morphometry has often been employed in neuroscience investigations, while controlling for the scanner effect of the scanner is a critical issue for ensuring accuracy in brain morphometric studies of rare orphan diseases due to the lack of normal reference values available for multicenter studies. This study aimed to provide across-site normal reference values of global and regional brain volumes for each sex and age group in children and adolescents. We collected magnetic resonance imaging (MRI) examinations of 846 neurotypical participants aged 6.0-17.9 years (339 male and 507 female participants) from 5 institutions comprising healthy volunteers or neurotypical patients without neurological disorders, neuropsychological disorders, or epilepsy. Regional-based analysis using the CIVET 2.1.0. pipeline provided regional brain volumes, and the measurements were across-site combined using ComBat-GAM harmonization. The normal reference values of global and regional brain volumes and lateral indices in our study could be helpful for evaluating the characteristics of the brain morphology of each individual in a clinical setting and investigating the brain morphology of ultra-rare diseases.
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Affiliation(s)
- Tadashi Shiohama
- Department of Pediatrics, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chuo-ku, Chiba-shi 260-8677, Chiba, Japan
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA
| | - Norihide Maikusa
- Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo, Tokyo 108-8639, Japan
- Department of Radiology, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
| | - Masahiro Kawaguchi
- Department of Pediatrics, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya 466-8550, Aichi, Japan; (M.K.)
| | - Jun Natsume
- Department of Pediatrics, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya 466-8550, Aichi, Japan; (M.K.)
- Department of Developmental Disability Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya 466-8550, Aichi, Japan
| | - Yoshiyuki Hirano
- Research Center for Child Mental Development, Chiba University, Inohana 1-8-1, Chuo-ku, Chiba-shi 260-8677, Chiba, Japan
- United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University and University of Fukui, Suita 565-0871, Osaka, Japan
| | - Keito Saito
- Department of Pediatrics and Pediatric Neurology, Tokyo Women’s Medical University Yachiyo Medical Center, 477-96 Owadashinden, Yachiyo-shi 276-8524, Chiba, Japan
| | - Jun-ichi Takanashi
- Department of Pediatrics and Pediatric Neurology, Tokyo Women’s Medical University Yachiyo Medical Center, 477-96 Owadashinden, Yachiyo-shi 276-8524, Chiba, Japan
| | - Jacob Levman
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA
- Department of Mathematics, Statistics and Computer Science, St. Francis Xavier University, 5005 Chapel Square, Antigonish, NS B2G 2W5, Canada
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 149 13th Street, Charlestown, MA 02129, USA
- Nova Scotia Health Authority—Research, Innovation and Discovery Center for Clinical Research, 5790 University Avenue, Halifax, NS B3H 1V7, Canada
| | - Emi Takahashi
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 149 13th Street, Charlestown, MA 02129, USA
| | - Koji Matsumoto
- Department of Radiology, Chiba University Hospital, Inohana 1-8-1, Chuo-ku, Chiba-shi 260-8677, Chiba, Japan
| | - Hajime Yokota
- Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chuo-ku, Chiba-shi 260-8677, Chiba, Japan
| | - Shinya Hattori
- Department of Radiology, Chiba University Hospital, Inohana 1-8-1, Chuo-ku, Chiba-shi 260-8677, Chiba, Japan
| | - Keita Tsujimura
- Group of Brain Function and Development, Neuroscience Institute of the Graduate School of Science, Nagoya University, Nagoya 466-8550, Aichi, Japan
- Research Unit for Developmental Disorders, Institute for Advanced Research, Nagoya University, Nagoya 466-8550, Aichi, Japan
| | - Daisuke Sawada
- Department of Pediatrics, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chuo-ku, Chiba-shi 260-8677, Chiba, Japan
| | - Tomoko Uchida
- Department of Pediatrics, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chuo-ku, Chiba-shi 260-8677, Chiba, Japan
| | - Tomozumi Takatani
- Department of Pediatrics, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chuo-ku, Chiba-shi 260-8677, Chiba, Japan
| | - Katsunori Fujii
- Department of Pediatrics, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chuo-ku, Chiba-shi 260-8677, Chiba, Japan
- Department of Pediatrics, International University of Welfare and Health School of Medicine, Narita 286-8520, Chiba, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya 466-8550, Aichi, Japan
| | - Noriko Sato
- Department of Radiology, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
| | - Hiromichi Hamada
- Department of Pediatrics, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chuo-ku, Chiba-shi 260-8677, Chiba, Japan
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17
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Hu F, Chen AA, Horng H, Bashyam V, Davatzikos C, Alexander-Bloch A, Li M, Shou H, Satterthwaite TD, Yu M, Shinohara RT. Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization. Neuroimage 2023; 274:120125. [PMID: 37084926 PMCID: PMC10257347 DOI: 10.1016/j.neuroimage.2023.120125] [Citation(s) in RCA: 56] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 04/12/2023] [Accepted: 04/19/2023] [Indexed: 04/23/2023] Open
Abstract
Magnetic resonance imaging and computed tomography from multiple batches (e.g. sites, scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to obtain new insights into the human brain. However, significant confounding due to batch-related technical variation, called batch effects, is present in this data; direct application of downstream analyses to the data may lead to biased results. Image harmonization methods seek to remove these batch effects and enable increased generalizability and reproducibility of downstream results. In this review, we describe and categorize current approaches in statistical and deep learning harmonization methods. We also describe current evaluation metrics used to assess harmonization methods and provide a standardized framework to evaluate newly-proposed methods for effective harmonization and preservation of biological information. Finally, we provide recommendations to end-users to advocate for more effective use of current methods and to methodologists to direct future efforts and accelerate development of the field.
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Affiliation(s)
- Fengling Hu
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States.
| | - Andrew A Chen
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States
| | - Hannah Horng
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States
| | - Vishnu Bashyam
- Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Aaron Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Penn-CHOP Lifespan Brain Institute, United States; Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, United States
| | - Mingyao Li
- Statistical Center for Single-Cell and Spatial Genomics, Perelman School of Medicine, University of Pennsylvania, United States
| | - Haochang Shou
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States; Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Penn-CHOP Lifespan Brain Institute, United States; The Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States
| | - Meichen Yu
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, United States
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States; Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
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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: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [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.
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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.
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Bayer JMM, Thompson PM, Ching CRK, Liu M, Chen A, Panzenhagen AC, Jahanshad N, Marquand A, Schmaal L, Sämann PG. Site effects how-to and when: An overview of retrospective techniques to accommodate site effects in multi-site neuroimaging analyses. Front Neurol 2022; 13:923988. [PMID: 36388214 PMCID: PMC9661923 DOI: 10.3389/fneur.2022.923988] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 08/12/2022] [Indexed: 09/12/2023] Open
Abstract
Site differences, or systematic differences in feature distributions across multiple data-acquisition sites, are a known source of heterogeneity that may adversely affect large-scale meta- and mega-analyses of independently collected neuroimaging data. They influence nearly all multi-site imaging modalities and biomarkers, and methods to compensate for them can improve reliability and generalizability in the analysis of genetics, omics, and clinical data. The origins of statistical site effects are complex and involve both technical differences (scanner vendor, head coil, acquisition parameters, imaging processing) and differences in sample characteristics (inclusion/exclusion criteria, sample size, ancestry) between sites. In an age of expanding international consortium research, there is a growing need to disentangle technical site effects from sample characteristics of interest. Numerous statistical and machine learning methods have been developed to control for, model, or attenuate site effects - yet to date, no comprehensive review has discussed the benefits and drawbacks of each for different use cases. Here, we provide an overview of the different existing statistical and machine learning methods developed to remove unwanted site effects from independently collected neuroimaging samples. We focus on linear mixed effect models, the ComBat technique and its variants, adjustments based on image quality metrics, normative modeling, and deep learning approaches such as generative adversarial networks. For each method, we outline the statistical foundation and summarize strengths and weaknesses, including their assumptions and conditions of use. We provide information on software availability and comment on the ease of use and the applicability of these methods to different types of data. We discuss validation and comparative reports, mention caveats and provide guidance on when to use each method, depending on context and specific research questions.
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Affiliation(s)
- Johanna M. M. Bayer
- Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Christopher R. K. Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Mengting Liu
- School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China
| | - Andrew Chen
- Department of Biostatistics, Epidemiology, and Informatics, Penn Statistics in Imaging and Visualization Center, University of Pennsylvania, Philadelphia, PA, United States
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, United States
| | - Alana C. Panzenhagen
- Programa de Pós-graduação em Ciências Biológicas: Bioquímica, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- Department of Translational Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Neda Jahanshad
- Laboratory of Brain eScience, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, CA, United States
| | - Andre Marquand
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behavior, Radboudumc, Nijmegen, Netherlands
| | - Lianne Schmaal
- Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia
- Orygen, Parkville, VIC, Australia
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Shao T, Wang W, Hei G, Yang Y, Long Y, Wang X, Xiao J, Huang Y, Song X, Xu X, Gao S, Huang J, Wang Y, Zhao J, Wu R. Identifying and revealing different brain neural activities of cognitive subtypes in early course schizophrenia. Front Mol Neurosci 2022; 15:983995. [PMID: 36267704 PMCID: PMC9577612 DOI: 10.3389/fnmol.2022.983995] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 09/07/2022] [Indexed: 01/10/2023] Open
Abstract
Background Cognitive subtypes of schizophrenia may exhibit different neurobiological characteristics. This study aimed to reveal the underlying neurobiological features between cognitive subtypes in the early course of schizophrenia (ECS). According to prior studies, we hypothesized to identify 2–4 distinct cognitive subtypes. We further hypothesized that the subtype with relatively poorer cognitive function might have lower brain spontaneous neural activity than the subtype with relatively better cognitive function. Method Cognitive function was assessed by the MATRICS Consensus Cognitive Battery (MCCB). Resting-state functional magnetic resonance imaging scanning was conducted for each individual. There were 155 ECS individuals and 97 healthy controls (HCs) included in the subsequent analysis. Latent profile analysis (LPA) was used to identify the cognitive subtypes in ECS individuals, and amplitude of low-frequency fluctuations (ALFFs) was used to measure brain spontaneous neural activity in ECS individuals and HCs. Results LPA identified two cognitive subtypes in ECS individuals, containing a severely impaired subtype (SI, n = 63) and a moderately impaired subtype (MI, n = 92). Compared to HCs, ECS individuals exhibited significantly increased ALFF in the left caudate and bilateral thalamus and decreased ALFF in the bilateral medial prefrontal cortex and bilateral posterior cingulate cortex/precuneus (PCC/PCu). In ECS cognitive subtypes, SI showed significantly higher ALFF in the left precentral gyrus (PreCG) and lower ALFF in the left PCC/PCu than MI. Furthermore, ALFFs of left PreCG were negatively correlated with several MCCB cognitive domains in ECS individuals, while ALFF of left PCC/PCu presented opposite correlations. Conclusion Our findings suggest that differences in the brain spontaneous neural activity of PreCG and PCC/PCu might be the potential neurobiological features of the cognitive subtypes in ECS, which may deepen our understanding of the role of PreCG and PCC/PCu in the pathogenesis of cognitive impairment in schizophrenia.
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Affiliation(s)
- Tiannan Shao
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Weiyan Wang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Gangrui Hei
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ye Yang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Yujun Long
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Xiaoyi Wang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Jingmei Xiao
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Yuyan Huang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Xueqin Song
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xijia Xu
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Shuzhan Gao
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Jing Huang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Ying Wang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Jingping Zhao
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Renrong Wu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
- *Correspondence: Renrong Wu
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