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Linguiti S, Vogel JW, Sydnor VJ, Pines A, Wellman N, Basbaum A, Eickhoff CR, Eickhoff SB, Edwards RR, Larsen B, McKinstry-Wu A, Scott JC, Roalf DR, Sharma V, Strain EC, Corder G, Dworkin RH, Satterthwaite TD. Functional imaging studies of acute administration of classic psychedelics, ketamine, and MDMA: Methodological limitations and convergent results. Neurosci Biobehav Rev 2023; 154:105421. [PMID: 37802267 DOI: 10.1016/j.neubiorev.2023.105421] [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: 05/04/2023] [Revised: 09/13/2023] [Accepted: 10/02/2023] [Indexed: 10/08/2023]
Abstract
Functional magnetic resonance imaging (fMRI) is increasingly used to non-invasively study the acute impact of psychedelics on the human brain. While fMRI is a promising tool for measuring brain function in response to psychedelics, it also has known methodological challenges. We conducted a systematic review of fMRI studies examining acute responses to experimentally administered psychedelics in order to identify convergent findings and characterize heterogeneity in the literature. We reviewed 91 full-text papers; these studies were notable for substantial heterogeneity in design, task, dosage, drug timing, and statistical approach. Data recycling was common, with 51 unique samples across 91 studies. Fifty-seven studies (54%) did not meet contemporary standards for Type I error correction or control of motion artifact. Psilocybin and LSD were consistently reported to moderate the connectivity architecture of the sensorimotor-association cortical axis. Studies also consistently reported that ketamine administration increased activation in the dorsomedial prefrontal cortex. Moving forward, use of best practices such as pre-registration, standardized image processing and statistical testing, and data sharing will be important in this rapidly developing field.
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Affiliation(s)
- Sophia Linguiti
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA, United States; Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Jacob W Vogel
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA, United States; Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States; Department of Clinical Sciences, Malmö, SciLifeLab, Lund University, Lund, Sweden
| | - Valerie J Sydnor
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA, United States; Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Adam Pines
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA, United States; Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States; Department of Psychiatry, Stanford University, Stanford, CA, United States
| | - Nick Wellman
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA, United States; Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Allan Basbaum
- Department of Anatomy, University of California, San Francisco, United States
| | - Claudia R Eickhoff
- Institute of Neuroscience and Medicine, (INM-1, INM-7), Research Centre Jülich, Jülich, Germany; Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine-University, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, (INM-1, INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
| | - Robert R Edwards
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA, United States; Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Andrew McKinstry-Wu
- Department of Anesthesiology and Critical Care, Neuroscience of Unconsciousness and Reanimation Research Alliance (NEURRAL), University of Pennsylvania, Philadelphia, United States
| | - J Cobb Scott
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States; VISN4 Mental Illness Research, Education, and Clinical Center at the Corporal Michael J. Crescenz VA (Veterans Affairs) Medical Center, Philadelphia, PA, United States
| | - David R Roalf
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Vaishnavi Sharma
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA, United States; Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Eric C Strain
- Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine, 5510 Nathan Shock Drive, Baltimore, MD, United States
| | - Gregory Corder
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Robert H Dworkin
- Department of Anesthesiology and Perioperative Medicine, University of Rochester School of Medicine and Dentistry, Rochester, NY, United States
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA, United States; Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States.
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2
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Uddin LQ, Betzel RF, Cohen JR, Damoiseaux JS, De Brigard F, Eickhoff SB, Fornito A, Gratton C, Gordon EM, Laird AR, Larson-Prior L, McIntosh AR, Nickerson LD, Pessoa L, Pinho AL, Poldrack RA, Razi A, Sadaghiani S, Shine JM, Yendiki A, Yeo BTT, Spreng RN. Controversies and progress on standardization of large-scale brain network nomenclature. Netw Neurosci 2023; 7:864-905. [PMID: 37781138 PMCID: PMC10473266 DOI: 10.1162/netn_a_00323] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 05/10/2023] [Indexed: 10/03/2023] Open
Abstract
Progress in scientific disciplines is accompanied by standardization of terminology. Network neuroscience, at the level of macroscale organization of the brain, is beginning to confront the challenges associated with developing a taxonomy of its fundamental explanatory constructs. The Workgroup for HArmonized Taxonomy of NETworks (WHATNET) was formed in 2020 as an Organization for Human Brain Mapping (OHBM)-endorsed best practices committee to provide recommendations on points of consensus, identify open questions, and highlight areas of ongoing debate in the service of moving the field toward standardized reporting of network neuroscience results. The committee conducted a survey to catalog current practices in large-scale brain network nomenclature. A few well-known network names (e.g., default mode network) dominated responses to the survey, and a number of illuminating points of disagreement emerged. We summarize survey results and provide initial considerations and recommendations from the workgroup. This perspective piece includes a selective review of challenges to this enterprise, including (1) network scale, resolution, and hierarchies; (2) interindividual variability of networks; (3) dynamics and nonstationarity of networks; (4) consideration of network affiliations of subcortical structures; and (5) consideration of multimodal information. We close with minimal reporting guidelines for the cognitive and network neuroscience communities to adopt.
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Affiliation(s)
- Lucina Q. Uddin
- Department of Psychiatry and Biobehavioral Sciences and Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Richard F. Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Jessica R. Cohen
- Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC, USA
| | - Jessica S. Damoiseaux
- Institute of Gerontology and Department of Psychology, Wayne State University, Detroit, MI, USA
| | | | - Simon B. Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Evan M. Gordon
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, USA
| | - Angela R. Laird
- Department of Physics, Florida International University, Miami, FL, USA
| | - Linda Larson-Prior
- Deptartment of Psychiatry and Department of Neurobiology and Developmental Sciences, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - A. Randal McIntosh
- Institute for Neuroscience and Neurotechnology, Simon Fraser University, Vancouver, BC, Canada
| | | | - Luiz Pessoa
- Department of Psychology, University of Maryland, College Park, MD, USA
| | - Ana Luísa Pinho
- Brain and Mind Institute, Western University, London, Ontario, Canada
| | | | - Adeel Razi
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Sepideh Sadaghiani
- Department of Psychology, University of Illinois, Urbana Champaign, IL, USA
| | - James M. Shine
- Brain and Mind Center, University of Sydney, Sydney, Australia
| | - Anastasia Yendiki
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - B. T. Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
| | - R. Nathan Spreng
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
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3
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Goheen J, Anderson JAE, Zhang J, Northoff G. From Lung to Brain: Respiration Modulates Neural and Mental Activity. Neurosci Bull 2023; 39:1577-1590. [PMID: 37285017 PMCID: PMC10533478 DOI: 10.1007/s12264-023-01070-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 04/10/2023] [Indexed: 06/08/2023] Open
Abstract
Respiration protocols have been developed to manipulate mental states, including their use for therapeutic purposes. In this systematic review, we discuss evidence that respiration may play a fundamental role in coordinating neural activity, behavior, and emotion. The main findings are: (1) respiration affects the neural activity of a wide variety of regions in the brain; (2) respiration modulates different frequency ranges in the brain's dynamics; (3) different respiration protocols (spontaneous, hyperventilation, slow or resonance respiration) yield different neural and mental effects; and (4) the effects of respiration on the brain are related to concurrent modulation of biochemical (oxygen delivery, pH) and physiological (cerebral blood flow, heart rate variability) variables. We conclude that respiration may be an integral rhythm of the brain's neural activity. This provides an intimate connection of respiration with neuro-mental features like emotion. A respiratory-neuro-mental connection holds the promise for a brain-based therapeutic usage of respiration in mental disorders.
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Affiliation(s)
- Josh Goheen
- The Royal Ottawa Mental Health Centre, The University of Ottawa, Ottawa, K1Z 7K4, Canada.
- Department of Cognitive Science, Carleton University, Ottawa, K1S 5B6, Canada.
| | - John A E Anderson
- Department of Cognitive Science, Carleton University, Ottawa, K1S 5B6, Canada
| | - Jianfeng Zhang
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, 518060, China
- School of Psychology, Shenzhen University, Shenzhen, 518060, China
| | - Georg Northoff
- The Royal Ottawa Mental Health Centre, The University of Ottawa, Ottawa, K1Z 7K4, Canada
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4
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Rashid B, Glasser MF, Nichols T, Van Essen D, Juttukonda MR, Schwab NA, Greve DN, Yacoub E, Lovely A, Terpstra M, Harms MP, Bookheimer SY, Ances BM, Salat DH, Arnold SE. Cardiovascular and metabolic health is associated with functional brain connectivity in middle-aged and older adults: Results from the Human Connectome Project-Aging study. Neuroimage 2023; 276:120192. [PMID: 37247763 PMCID: PMC10330931 DOI: 10.1016/j.neuroimage.2023.120192] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 05/15/2023] [Accepted: 05/24/2023] [Indexed: 05/31/2023] Open
Abstract
Several cardiovascular and metabolic indicators, such as cholesterol and blood pressure have been associated with altered neural and cognitive health as well as increased risk of dementia and Alzheimer's disease in later life. In this cross-sectional study, we examined how an aggregate index of cardiovascular and metabolic risk factor measures was associated with correlation-based estimates of resting-state functional connectivity (FC) across a broad adult age-span (36-90+ years) from 930 volunteers in the Human Connectome Project Aging (HCP-A). Increased (i.e., worse) aggregate cardiometabolic scores were associated with reduced FC globally, with especially strong effects in insular, medial frontal, medial parietal, and superior temporal regions. Additionally, at the network-level, FC between core brain networks, such as default-mode and cingulo-opercular, as well as dorsal attention networks, showed strong effects of cardiometabolic risk. These findings highlight the lifespan impact of cardiovascular and metabolic health on whole-brain functional integrity and how these conditions may disrupt higher-order network integrity.
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Affiliation(s)
- Barnaly Rashid
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th St., Charlestown, MA 02129, United States; Harvard Medical School, Boston, MA, United States.
| | - Matthew F Glasser
- Washington University School of Medicine, St. Louis, MO, United States
| | | | - David Van Essen
- Washington University School of Medicine, St. Louis, MO, United States
| | - Meher R Juttukonda
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th St., Charlestown, MA 02129, United States; Harvard Medical School, Boston, MA, United States
| | - Nadine A Schwab
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th St., Charlestown, MA 02129, United States; Harvard Medical School, Boston, MA, United States
| | - Douglas N Greve
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th St., Charlestown, MA 02129, United States; Harvard Medical School, Boston, MA, United States
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States
| | - Allison Lovely
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th St., Charlestown, MA 02129, United States; Harvard Medical School, Boston, MA, United States
| | | | - Michael P Harms
- Washington University in St. Louis, St. Louis, MO, United States
| | | | - Beau M Ances
- Washington University School of Medicine, St. Louis, MO, United States; Washington University in St. Louis, St. Louis, MO, United States
| | - David H Salat
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th St., Charlestown, MA 02129, United States; Harvard Medical School, Boston, MA, United States.
| | - Steven E Arnold
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th St., Charlestown, MA 02129, United States; Harvard Medical School, Boston, MA, United States.
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5
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Yao S, Zhu Q, Zhang Q, Cai Y, Liu S, Pang L, Jing Y, Yin X, Cheng H. Managing Cancer and Living Meaningfully (CALM) alleviates chemotherapy related cognitive impairment (CRCI) in breast cancer survivors: A pilot study based on resting-state fMRI. Cancer Med 2023; 12:16231-16242. [PMID: 37409628 PMCID: PMC10469649 DOI: 10.1002/cam4.6285] [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: 10/09/2022] [Revised: 06/12/2023] [Accepted: 06/14/2023] [Indexed: 07/07/2023] Open
Abstract
BACKGROUND Chemotherapy related cognitive impairment (CRCI) is a type of memory and cognitive impairment induced by chemotherapy and has become a growing clinical problem. Breast cancer survivors (BCs) refer to patients from the moment of breast cancer diagnosis to the end of their lives. Managing Cancer and Living Meaningfully (CALM) is a convenient and easy-to-apply psychological intervention that has been proven to improve quality of life and alleviate CRCI in BCs. However, the underlying neurobiological mechanisms remain unclear. Resting-state functional magnetic resonance imaging (rs-fMRI) has become an effective method for understanding the neurobiological mechanisms of brain networks in CRCI. The fractional amplitude of low-frequency fluctuations (fALFF) and ALFF have often been used in analyzing the power and intensity of spontaneous regional resting state neural activity. METHODS The recruited BCs were randomly divided into the CALM group and the care as usual (CAU) group. All BCs were evaluated by the Functional Assessment of Cancer Therapy Cognitive Function (FACT-Cog) before and after CALM or CAU. The rs-fMRI imaging was acquired before and after CALM intervention in CALM group BCs. The BCs were defined as before CALM intervention (BCI) group and after CALM intervention (ACI) group. RESULTS There were 32 BCs in CALM group and 35 BCs in CAU group completed the overall study. There were significant differences between the BCI group and the ACI group in the FACT-Cog-PCI scores. Compared with the BCI group, the ACI group showed lower fALFF signal in the left medial frontal gyrus and right sub-gyral and higher fALFF in the left occipital_sup and middle occipital gyrus. There was a significant positive correlation between hippocampal ALFF value and FACT-Cog-PCI scores. CONCLUSIONS CALM intervention may have an effective function in alleviating CRCI of BCs. The altered local synchronization and regional brain activity may be correlated with the improved cognitive function of BCs who received the CALM intervention. The ALFF value of hippocampus seems to be an important factor in reflect cognitive function in BCs with CRCI and the neural network mechanism of CALM intervention deserves further exploration to promote its application.
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Affiliation(s)
- Senbang Yao
- Department of OncologyThe Second Affiliated Hospital of Anhui Medical UniversityHefeiChina
- Cancer and Cognition LaboratoryAnhui Medical UniversityHefeiChina
| | - Qinqin Zhu
- Department of RadiologyQuzhou People's HospitalQuzhouChina
| | - Qianqian Zhang
- Department of OncologyThe Second Affiliated Hospital of Anhui Medical UniversityHefeiChina
- Cancer and Cognition LaboratoryAnhui Medical UniversityHefeiChina
| | - Yinlian Cai
- Department of OncologyThe Second Affiliated Hospital of Anhui Medical UniversityHefeiChina
- Cancer and Cognition LaboratoryAnhui Medical UniversityHefeiChina
| | - Shaochun Liu
- Department of OncologyThe Second Affiliated Hospital of Anhui Medical UniversityHefeiChina
- Cancer and Cognition LaboratoryAnhui Medical UniversityHefeiChina
| | - Lulian Pang
- Department of OncologyThe Second Affiliated Hospital of Anhui Medical UniversityHefeiChina
- Cancer and Cognition LaboratoryAnhui Medical UniversityHefeiChina
| | - Yanyan Jing
- Department of OncologyThe Second Affiliated Hospital of Anhui Medical UniversityHefeiChina
- Cancer and Cognition LaboratoryAnhui Medical UniversityHefeiChina
| | - Xiangxiang Yin
- Department of OncologyThe Second Affiliated Hospital of Anhui Medical UniversityHefeiChina
- Cancer and Cognition LaboratoryAnhui Medical UniversityHefeiChina
| | - Huaidong Cheng
- Department of OncologyThe Second Affiliated Hospital of Anhui Medical UniversityHefeiChina
- Shenzhen Clinical Medical School of Southern Medical UniversityShenzhenChina
- Department of OncologyShenzhen Hospital of Southern Medical UniversityShenzhenChina
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6
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Hou X, Guo P, Wang P, Liu P, Lin DDM, Fan H, Li Y, Wei Z, Lin Z, Jiang D, Jin J, Kelly C, Pillai JJ, Huang J, Pinho MC, Thomas BP, Welch BG, Park DC, Patel VM, Hillis AE, Lu H. Deep-learning-enabled brain hemodynamic mapping using resting-state fMRI. NPJ Digit Med 2023; 6:116. [PMID: 37344684 PMCID: PMC10284915 DOI: 10.1038/s41746-023-00859-y] [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: 10/10/2022] [Accepted: 06/09/2023] [Indexed: 06/23/2023] Open
Abstract
Cerebrovascular disease is a leading cause of death globally. Prevention and early intervention are known to be the most effective forms of its management. Non-invasive imaging methods hold great promises for early stratification, but at present lack the sensitivity for personalized prognosis. Resting-state functional magnetic resonance imaging (rs-fMRI), a powerful tool previously used for mapping neural activity, is available in most hospitals. Here we show that rs-fMRI can be used to map cerebral hemodynamic function and delineate impairment. By exploiting time variations in breathing pattern during rs-fMRI, deep learning enables reproducible mapping of cerebrovascular reactivity (CVR) and bolus arrival time (BAT) of the human brain using resting-state CO2 fluctuations as a natural "contrast media". The deep-learning network is trained with CVR and BAT maps obtained with a reference method of CO2-inhalation MRI, which includes data from young and older healthy subjects and patients with Moyamoya disease and brain tumors. We demonstrate the performance of deep-learning cerebrovascular mapping in the detection of vascular abnormalities, evaluation of revascularization effects, and vascular alterations in normal aging. In addition, cerebrovascular maps obtained with the proposed method exhibit excellent reproducibility in both healthy volunteers and stroke patients. Deep-learning resting-state vascular imaging has the potential to become a useful tool in clinical cerebrovascular imaging.
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Affiliation(s)
- Xirui Hou
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Pengfei Guo
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Puyang Wang
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Peiying Liu
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Doris D M Lin
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Hongli Fan
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Yang Li
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Zhiliang Wei
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Zixuan Lin
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Dengrong Jiang
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jin Jin
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Catherine Kelly
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jay J Pillai
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Judy Huang
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Marco C Pinho
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Binu P Thomas
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Babu G Welch
- Department of Neurologic Surgery, UT Southwestern Medical Center, Dallas, TX, USA
- Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA
| | - Denise C Park
- Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA
| | - Vishal M Patel
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Argye E Hillis
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Hanzhang Lu
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
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7
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Bukhari H, Su C, Dhamala E, Gu Z, Jamison K, Kuceyeski A. Graph-matching distance between individuals' functional connectomes varies with relatedness, age, and cognitive score. Hum Brain Mapp 2023; 44:3541-3554. [PMID: 37042411 PMCID: PMC10203814 DOI: 10.1002/hbm.26296] [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: 10/28/2022] [Revised: 02/10/2023] [Accepted: 03/20/2023] [Indexed: 04/13/2023] Open
Abstract
Functional connectomes (FCs), represented by networks or graphs that summarize coactivation patterns between pairs of brain regions, have been related at a population level to age, sex, cognitive/behavioral scores, life experience, genetics, and disease/disorders. However, quantifying FC differences between individuals also provides a rich source of information with which to map to differences in those individuals' biology, experience, genetics or behavior. In this study, graph matching is used to create a novel inter-individual FC metric, called swap distance, that quantifies the distance between pairs of individuals' partial FCs, with a smaller swap distance indicating the individuals have more similar FC. We apply graph matching to align FCs between individuals from the the Human Connectome ProjectN = 997 and find that swap distance (i) increases with increasing familial distance, (ii) increases with subjects' ages, (iii) is smaller for pairs of females compared to pairs of males, and (iv) is larger for females with lower cognitive scores compared to females with larger cognitive scores. Regions that contributed most to individuals' swap distances were in higher-order networks, that is, default-mode and fronto-parietal, that underlie executive function and memory. These higher-order networks' regions also had swap frequencies that varied monotonically with familial relatedness of the individuals in question. We posit that the proposed graph matching technique provides a novel way to study inter-subject differences in FC and enables quantification of how FC may vary with age, relatedness, sex, and behavior.
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Affiliation(s)
- Hussain Bukhari
- Department of NeuroscienceWeill Cornell MedicineNew YorkNew YorkUSA
| | - Chang Su
- Department of BiostatisticsYale UniversityNew HavenConnecticutUSA
| | - Elvisha Dhamala
- Department of PsychologyYale UniversityNew HavenConnecticutUSA
| | - Zijin Gu
- Department of Electrical and Computer EngineeringCornell UniversityIthacaNew YorkUSA
| | - Keith Jamison
- Department of RadiologyWeill Cornell MedicineNew YorkNew YorkUSA
| | - Amy Kuceyeski
- Department of RadiologyWeill Cornell MedicineNew YorkNew YorkUSA
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8
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Pines A, Keller AS, Larsen B, Bertolero M, Ashourvan A, Bassett DS, Cieslak M, Covitz S, Fan Y, Feczko E, Houghton A, Rueter AR, Saggar M, Shafiei G, Tapera TM, Vogel J, Weinstein SM, Shinohara RT, Williams LM, Fair DA, Satterthwaite TD. Development of top-down cortical propagations in youth. Neuron 2023; 111:1316-1330.e5. [PMID: 36803653 PMCID: PMC10121821 DOI: 10.1016/j.neuron.2023.01.014] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 12/08/2022] [Accepted: 01/18/2023] [Indexed: 02/19/2023]
Abstract
Hierarchical processing requires activity propagating between higher- and lower-order cortical areas. However, functional neuroimaging studies have chiefly quantified fluctuations within regions over time rather than propagations occurring over space. Here, we leverage advances in neuroimaging and computer vision to track cortical activity propagations in a large sample of youth (n = 388). We delineate cortical propagations that systematically ascend and descend a cortical hierarchy in all individuals in our developmental cohort, as well as in an independent dataset of densely sampled adults. Further, we demonstrate that top-down, descending hierarchical propagations become more prevalent with greater demands for cognitive control as well as with development in youth. These findings emphasize that hierarchical processing is reflected in the directionality of propagating cortical activity and suggest top-down propagations as a potential mechanism of neurocognitive maturation in youth.
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Affiliation(s)
- Adam Pines
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94304, USA; The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Arielle S Keller
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Bart Larsen
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Maxwell Bertolero
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Arian Ashourvan
- Department of Psychology, The University of Kansas, Lawrence, KS 66045, USA
| | - Dani S Bassett
- Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA; Departments of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Physics & Astronomy, The University of Pennsylvania, Philadelphia, PA 19104, USA; Santa Fe Institute, Santa Fe, NM 87051, USA
| | - Matthew Cieslak
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Sydney Covitz
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Yong Fan
- Department of Radiology, The University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Eric Feczko
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55414, USA
| | - Audrey Houghton
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55414, USA
| | - Amanda R Rueter
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55414, USA
| | - Manish Saggar
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94304, USA
| | - Golia Shafiei
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Tinashe M Tapera
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Jacob Vogel
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Sarah M Weinstein
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Leanne M Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94304, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55414, USA
| | - Theodore D Satterthwaite
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA.
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9
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Elbau IG, Lynch CJ, Downar J, Vila-Rodriguez F, Power JD, Solomonov N, Daskalakis ZJ, Blumberger DM, Liston C. Functional Connectivity Mapping for rTMS Target Selection in Depression. Am J Psychiatry 2023; 180:230-240. [PMID: 36855880 DOI: 10.1176/appi.ajp.20220306] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/02/2023]
Abstract
OBJECTIVE Repetitive transcranial magnetic stimulation (rTMS) protocols increasingly use subgenual anterior cingulate cortex (sgACC) functional connectivity to individualize treatment targets. However, the efficacy of this approach is unclear, with conflicting findings and varying effect sizes across studies. Here, the authors investigated the effect of the stimulation site's functional connectivity with the sgACC (sgACC-StimFC) on treatment outcome to rTMS in 295 patients with major depression. METHODS The reliability and accuracy of estimating sgACC functional connectivity were validated with data from individuals who underwent extensive functional MRI testing. Electric field modeling was used to analyze associations between sgACC-StimFC and clinical improvement using standardized assessments and to evaluate sources of heterogeneity. RESULTS An imputation-based method provided reliable and accurate sgACC functional connectivity estimates. Treatment responses weakly but robustly correlated with sgACC-StimFC (r=-0.16), but only when the stimulated cortex was identified using electric field modeling. Surprisingly, this association was driven by patients with strong global signal fluctuations stemming from a specific periodic respiratory pattern (r=-0.49). CONCLUSIONS Functional connectivity between the sgACC and the stimulated cortex was correlated with individual differences in treatment outcomes, but the association was weaker than those observed in previous studies and was accentuated in a subgroup of patients with distinct, respiration-related signal patterns in their scans. These findings indicate that in a large representative sample of patients with major depressive disorder, individual differences in sgACC-StimFC explained only ∼3% of the variance in outcomes, which may limit the utility of existing sgACC-based targeting protocols. However, these data also provide strong evidence for a true-albeit small-effect and highlight opportunities for incorporating additional functional connectivity measures to generate models of rTMS response with enhanced predictive power.
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Affiliation(s)
- Immanuel G Elbau
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York (Elbau, Lynch, Power, Solomonov, Liston); Department of Psychiatry and Institute of Medical Science, Faculty of Medicine, University of Toronto, and Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto (Downar, Blumberger); Non-Invasive Neurostimulation Therapies Lab and Department of Psychiatry, University of British Columbia, Vancouver (Vila-Rodriguez); Department of Psychiatry, University of California, San Diego (Daskalakis)
| | - Charles J Lynch
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York (Elbau, Lynch, Power, Solomonov, Liston); Department of Psychiatry and Institute of Medical Science, Faculty of Medicine, University of Toronto, and Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto (Downar, Blumberger); Non-Invasive Neurostimulation Therapies Lab and Department of Psychiatry, University of British Columbia, Vancouver (Vila-Rodriguez); Department of Psychiatry, University of California, San Diego (Daskalakis)
| | - Jonathan Downar
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York (Elbau, Lynch, Power, Solomonov, Liston); Department of Psychiatry and Institute of Medical Science, Faculty of Medicine, University of Toronto, and Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto (Downar, Blumberger); Non-Invasive Neurostimulation Therapies Lab and Department of Psychiatry, University of British Columbia, Vancouver (Vila-Rodriguez); Department of Psychiatry, University of California, San Diego (Daskalakis)
| | - Fidel Vila-Rodriguez
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York (Elbau, Lynch, Power, Solomonov, Liston); Department of Psychiatry and Institute of Medical Science, Faculty of Medicine, University of Toronto, and Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto (Downar, Blumberger); Non-Invasive Neurostimulation Therapies Lab and Department of Psychiatry, University of British Columbia, Vancouver (Vila-Rodriguez); Department of Psychiatry, University of California, San Diego (Daskalakis)
| | - Jonathan D Power
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York (Elbau, Lynch, Power, Solomonov, Liston); Department of Psychiatry and Institute of Medical Science, Faculty of Medicine, University of Toronto, and Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto (Downar, Blumberger); Non-Invasive Neurostimulation Therapies Lab and Department of Psychiatry, University of British Columbia, Vancouver (Vila-Rodriguez); Department of Psychiatry, University of California, San Diego (Daskalakis)
| | - Nili Solomonov
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York (Elbau, Lynch, Power, Solomonov, Liston); Department of Psychiatry and Institute of Medical Science, Faculty of Medicine, University of Toronto, and Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto (Downar, Blumberger); Non-Invasive Neurostimulation Therapies Lab and Department of Psychiatry, University of British Columbia, Vancouver (Vila-Rodriguez); Department of Psychiatry, University of California, San Diego (Daskalakis)
| | - Zafiris J Daskalakis
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York (Elbau, Lynch, Power, Solomonov, Liston); Department of Psychiatry and Institute of Medical Science, Faculty of Medicine, University of Toronto, and Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto (Downar, Blumberger); Non-Invasive Neurostimulation Therapies Lab and Department of Psychiatry, University of British Columbia, Vancouver (Vila-Rodriguez); Department of Psychiatry, University of California, San Diego (Daskalakis)
| | - Daniel M Blumberger
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York (Elbau, Lynch, Power, Solomonov, Liston); Department of Psychiatry and Institute of Medical Science, Faculty of Medicine, University of Toronto, and Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto (Downar, Blumberger); Non-Invasive Neurostimulation Therapies Lab and Department of Psychiatry, University of British Columbia, Vancouver (Vila-Rodriguez); Department of Psychiatry, University of California, San Diego (Daskalakis)
| | - Conor Liston
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York (Elbau, Lynch, Power, Solomonov, Liston); Department of Psychiatry and Institute of Medical Science, Faculty of Medicine, University of Toronto, and Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto (Downar, Blumberger); Non-Invasive Neurostimulation Therapies Lab and Department of Psychiatry, University of British Columbia, Vancouver (Vila-Rodriguez); Department of Psychiatry, University of California, San Diego (Daskalakis)
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10
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Kalin NH. Insights and Advances Into Treatments for Major Depression. Am J Psychiatry 2023; 180:173-176. [PMID: 36855877 DOI: 10.1176/appi.ajp.20230041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/02/2023]
Affiliation(s)
- Ned H Kalin
- Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison
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11
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Cutts SA, Faskowitz J, Betzel RF, Sporns O. Uncovering individual differences in fine-scale dynamics of functional connectivity. Cereb Cortex 2023; 33:2375-2394. [PMID: 35690591 DOI: 10.1093/cercor/bhac214] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 05/07/2022] [Accepted: 05/08/2022] [Indexed: 01/01/2023] Open
Abstract
Functional connectivity (FC) profiles contain subject-specific features that are conserved across time and have potential to capture brain-behavior relationships. Most prior work has focused on spatial features (nodes and systems) of these FC fingerprints, computed over entire imaging sessions. We propose a method for temporally filtering FC, which allows selecting specific moments in time while also maintaining the spatial pattern of node-based activity. To this end, we leverage a recently proposed decomposition of FC into edge time series (eTS). We systematically analyze functional magnetic resonance imaging frames to define features that enhance identifiability across multiple fingerprinting metrics, similarity metrics, and data sets. Results show that these metrics characteristically vary with eTS cofluctuation amplitude, similarity of frames within a run, transition velocity, and expression of functional systems. We further show that data-driven optimization of features that maximize fingerprinting metrics isolates multiple spatial patterns of system expression at specific moments in time. Selecting just 10% of the data can yield stronger fingerprints than are obtained from the full data set. Our findings support the idea that FC fingerprints are differentially expressed across time and suggest that multiple distinct fingerprints can be identified when spatial and temporal characteristics are considered simultaneously.
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Affiliation(s)
- Sarah A Cutts
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States.,Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States.,Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States
| | - Richard F Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States.,Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States.,Network Science Institute, Indiana University, Bloomington, IN 47408, United States.,Cognitive Science Program, Indiana University, Bloomington, IN 47405, United States
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States.,Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States.,Network Science Institute, Indiana University, Bloomington, IN 47408, United States.,Cognitive Science Program, Indiana University, Bloomington, IN 47405, United States
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12
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Wang Y, van Gelderen P, de Zwart JA, Özbay PS, Mandelkow H, Picchioni D, Duyn JH. Cerebrovascular activity is a major factor in the cerebrospinal fluid flow dynamics. Neuroimage 2022; 258:119362. [PMID: 35688316 PMCID: PMC9271599 DOI: 10.1016/j.neuroimage.2022.119362] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 06/02/2022] [Accepted: 06/06/2022] [Indexed: 11/17/2022] Open
Abstract
Cerebrospinal fluid (CSF) provides physical protection to the central nervous system as well as an essential homeostatic environment for the normal functioning of neurons. Additionally, it has been proposed that the pulsatile movement of CSF may assist in glymphatic clearance of brain metabolic waste products implicated in neurodegeneration. In awake humans, CSF flow dynamics are thought to be driven primarily by cerebral blood volume fluctuations resulting from a number of mechanisms, including a passive vascular response to blood pressure variations associated with cardiac and respiratory cycles. Recent research has shown that mechanisms that rely on the action of vascular smooth muscle cells ("cerebrovascular activity") such as neuronal activity, changes in intravascular CO2, and autonomic activation from the brainstem, may lead to CSF pulsations as well. Nevertheless, the relative contribution of these mechanisms to CSF flow remains unclear. To investigate this further, we developed an MRI approach capable of disentangling and quantifying CSF flow components of different time scales associated with these mechanisms. This approach was evaluated on human control subjects (n = 12) performing intermittent voluntary deep inspirations, by determining peak flow velocities and displaced volumes between these mechanisms in the fourth ventricle. We found that peak flow velocities were similar between the different mechanisms, while displaced volumes per cycle were about a magnitude larger for deep inspirations. CSF flow velocity peaked at around 10.4 s (range 7.1-14.8 s, n = 12) following deep inspiration, consistent with known cerebrovascular activation delays for this autonomic challenge. These findings point to an important role of cerebrovascular activity in the genesis of CSF pulsations. Other regulatory triggers for cerebral blood flow such as autonomic arousal and orthostatic challenges may create major CSF pulsatile movement as well. Future quantitative comparison of these and possibly additional types of CSF pulsations with the proposed approach may help clarify the conditions that affect CSF flow dynamics.
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Affiliation(s)
- Yicun Wang
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States.
| | - Peter van Gelderen
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Jacco A de Zwart
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Pinar S Özbay
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Hendrik Mandelkow
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Dante Picchioni
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Jeff H Duyn
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
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13
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Individualized event structure drives individual differences in whole-brain functional connectivity. Neuroimage 2022; 252:118993. [DOI: 10.1016/j.neuroimage.2022.118993] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 12/25/2021] [Accepted: 02/10/2022] [Indexed: 01/04/2023] Open
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14
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Schaeffer S, Iadecola C. Revisiting the neurovascular unit. Nat Neurosci 2021; 24:1198-1209. [PMID: 34354283 PMCID: PMC9462551 DOI: 10.1038/s41593-021-00904-7] [Citation(s) in RCA: 225] [Impact Index Per Article: 75.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 06/30/2021] [Indexed: 02/06/2023]
Abstract
The brain is supplied by an elaborate vascular network that originates extracranially and reaches deep into the brain. The concept of the neurovascular unit provides a useful framework to investigate how neuronal signals regulate nearby microvessels to support the metabolic needs of the brain, but it does not consider the role of larger cerebral arteries and systemic vasoactive signals. Furthermore, the recently emerged molecular heterogeneity of cerebrovascular cells indicates that there is no prototypical neurovascular unit replicated at all levels of the vascular network. Here, we examine the cellular and molecular diversity of the cerebrovascular tree and the relative contribution of systemic and brain-intrinsic factors to neurovascular function. Evidence supports the concept of a 'neurovascular complex' composed of segmentally diverse functional modules that implement coordinated vascular responses to central and peripheral signals to maintain homeostasis of the brain. This concept has major implications for neurovascular regulation in health and disease and for brain imaging.
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15
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Stickland RC, Zvolanek KM, Moia S, Ayyagari A, Caballero-Gaudes C, Bright MG. A practical modification to a resting state fMRI protocol for improved characterization of cerebrovascular function. Neuroimage 2021; 239:118306. [PMID: 34175427 PMCID: PMC8552969 DOI: 10.1016/j.neuroimage.2021.118306] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 06/18/2021] [Accepted: 06/23/2021] [Indexed: 12/22/2022] Open
Abstract
Cerebrovascular reactivity (CVR), defined here as the Blood Oxygenation Level Dependent (BOLD) response to a CO2 pressure change, is a useful metric of cerebrovascular function. Both the amplitude and the timing (hemodynamic lag) of the CVR response can bring insight into the nature of a cerebrovascular pathology and aid in understanding noise confounds when using functional Magnetic Resonance Imaging (fMRI) to study neural activity. This research assessed a practical modification to a typical resting-state fMRI protocol, to improve the characterization of cerebrovascular function. In 9 healthy subjects, we modelled CVR and lag in three resting-state data segments, and in data segments which added a 2–3 minute breathing task to the start of a resting-state segment. Two different breathing tasks were used to induce fluctuations in arterial CO2 pressure: a breath-hold task to induce hypercapnia (CO2 increase) and a cued deep breathing task to induce hypocapnia (CO2 decrease). Our analysis produced voxel-wise estimates of the amplitude (CVR) and timing (lag) of the BOLD-fMRI response to CO2 by systematically shifting the CO2 regressor in time to optimize the model fit. This optimization inherently increases gray matter CVR values and fit statistics. The inclusion of a simple breathing task, compared to a resting-state scan only, increases the number of voxels in the brain that have a significant relationship between CO2 and BOLD-fMRI signals, and improves our confidence in the plausibility of voxel-wise CVR and hemodynamic lag estimates. We demonstrate the clinical utility and feasibility of this protocol in an incidental finding of Moyamoya disease, and explore the possibilities and challenges of using this protocol in younger populations. This hybrid protocol has direct applications for CVR mapping in both research and clinical settings and wider applications for fMRI denoising and interpretation.
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Affiliation(s)
- Rachael C Stickland
- Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.
| | - Kristina M Zvolanek
- Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States; Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, United States
| | - Stefano Moia
- Basque Center on Cognition, Brain and Language, Donostia, Gipuzkoa, Spain; University of the Basque Country EHU/UPV, Donostia, Gipuzkoa, Spain
| | - Apoorva Ayyagari
- Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States; Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, United States
| | | | - Molly G Bright
- Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States; Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, United States
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16
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Harrison SJ, Bianchi S, Heinzle J, Stephan KE, Iglesias S, Kasper L. A Hilbert-based method for processing respiratory timeseries. Neuroimage 2021; 230:117787. [PMID: 33516897 DOI: 10.1016/j.neuroimage.2021.117787] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 01/09/2021] [Accepted: 01/14/2021] [Indexed: 01/22/2023] Open
Abstract
In this technical note, we introduce a new method for estimating changes in respiratory volume per unit time (RVT) from respiratory bellows recordings. By using techniques from the electrophysiological literature, in particular the Hilbert transform, we show how we can better characterise breathing rhythms, with the goal of improving physiological noise correction in functional magnetic resonance imaging (fMRI). Specifically, our approach leads to a representation with higher time resolution and better captures atypical breathing events than current peak-based RVT estimators. Finally, we demonstrate that this leads to an increase in the amount of respiration-related variance removed from fMRI data when used as part of a typical preprocessing pipeline. Our implementation is publicly available as part of the PhysIO package, which is distributed as part of the open-source TAPAS toolbox (https://translationalneuromodeling.org/tapas).
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Affiliation(s)
- Samuel J Harrison
- Translational Neuromodeling Unit, University of Zurich & ETH Zurich, Zurich, Switzerland; FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK.
| | - Samuel Bianchi
- Translational Neuromodeling Unit, University of Zurich & ETH Zurich, Zurich, Switzerland; Institute for Biomedical Engineering, ETH Zurich & University of Zurich, Zurich, Switzerland
| | - Jakob Heinzle
- Translational Neuromodeling Unit, University of Zurich & ETH Zurich, Zurich, Switzerland
| | - Klaas Enno Stephan
- Translational Neuromodeling Unit, University of Zurich & ETH Zurich, Zurich, Switzerland; Max Planck Institute for Metabolism Research, Cologne, Germany; Techna Institute, University Health Network, Toronto, Canada
| | - Sandra Iglesias
- Translational Neuromodeling Unit, University of Zurich & ETH Zurich, Zurich, Switzerland
| | - Lars Kasper
- Translational Neuromodeling Unit, University of Zurich & ETH Zurich, Zurich, Switzerland; Institute for Biomedical Engineering, ETH Zurich & University of Zurich, Zurich, Switzerland; Techna Institute, University Health Network, Toronto, Canada
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17
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Lynch CJ, Power JD, Scult MA, Dubin M, Gunning FM, Liston C. Rapid Precision Functional Mapping of Individuals Using Multi-Echo fMRI. Cell Rep 2020; 33:108540. [PMID: 33357444 PMCID: PMC7792478 DOI: 10.1016/j.celrep.2020.108540] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 10/15/2020] [Accepted: 11/25/2020] [Indexed: 12/20/2022] Open
Abstract
Resting-state functional magnetic resonance imaging (fMRI) is widely used in cognitive and clinical neuroscience, but long-duration scans are currently needed to reliably characterize individual differences in functional connectivity (FC) and brain network topology. In this report, we demonstrate that multi-echo fMRI can improve the reliability of FC-based measurements. In four densely sampled individual humans, just 10 min of multi-echo data yielded better test-retest reliability than 30 min of single-echo data in independent datasets. This effect is pronounced in clinically important brain regions, including the subgenual cingulate, basal ganglia, and cerebellum, and is linked to three biophysical signal mechanisms (thermal noise, regional variability in the rate of T2* decay, and S0-dependent artifacts) with spatially distinct influences. Together, these findings establish the potential utility of multi-echo fMRI for rapid precision mapping using experimentally and clinically tractable scan times and will facilitate longitudinal neuroimaging of clinical populations. Lynch et al. demonstrate that the test-retest reliability of resting-state connectivity measurements can be improved using multi-echo fMRI. This effect is pronounced in clinically important brain regions and could help facilitate precision mapping of functional brain networks in healthy people and patient populations.
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Affiliation(s)
- Charles J Lynch
- Department of Psychiatry, Weill Cornell Medicine, New York, NY 10021, USA.
| | - Jonathan D Power
- Department of Psychiatry, Weill Cornell Medicine, New York, NY 10021, USA
| | - Matthew A Scult
- Department of Psychiatry, Weill Cornell Medicine, New York, NY 10021, USA
| | - Marc Dubin
- Department of Psychiatry, Weill Cornell Medicine, New York, NY 10021, USA
| | - Faith M Gunning
- Department of Psychiatry, Weill Cornell Medicine, New York, NY 10021, USA
| | - Conor Liston
- Department of Psychiatry, Weill Cornell Medicine, New York, NY 10021, USA.
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