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Bayrak Ş, de Wael RV, Schaare HL, Hettwer MD, Caldairou B, Bernasconi A, Bernasconi N, Bernhardt BC, Valk SL. Heritability of hippocampal functional and microstructural organisation. Neuroimage 2022; 264:119656. [PMID: 36183945 DOI: 10.1016/j.neuroimage.2022.119656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 09/18/2022] [Accepted: 09/28/2022] [Indexed: 01/07/2023] Open
Abstract
The hippocampus is a uniquely infolded allocortical structure in the medial temporal lobe that consists of the microstructurally and functionally distinct subregions: subiculum, cornu ammonis, and dentate gyrus. The hippocampus is a remarkably plastic region that is implicated in learning and memory. At the same time it has been shown that hippocampal subregion volumes are heritable, and that genetic expression varies along a posterior to anterior axis. Here, we studied how a heritable, stable, hippocampal organisation may support its flexible function in healthy adults. Leveraging the twin set-up of the Human Connectome Project with multimodal neuroimaging, we observed that the functional connectivity between hippocampus and cortex was heritable and that microstructure of the hippocampus genetically correlated with cortical microstructure. Moreover, both functional and microstructural organisation could be consistently captured by anterior-to-posterior and medial-to-lateral axes across individuals. However, heritability of functional, relative to microstructural, organisation was found reduced, suggesting individual variation in functional organisation may be explained by experience-driven factors. Last, we demonstrate that structure and function couple along an inherited macroscale organisation, suggesting an interplay of stability and plasticity within the hippocampus. Our study provides new insights on the heritability of the hippocampal of the structure and function within the hippocampal organisation.
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Affiliation(s)
- Şeyma Bayrak
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, FZ Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Department of Cognitive Neurology, University Hospital Leipzig and Faculty of Medicine, University of Leipzig, Leipzig, Germany.
| | - Reinder Vos de Wael
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - H Lina Schaare
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, FZ Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Meike D Hettwer
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, FZ Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Max Planck School of Cognition, Max Planck Institute of Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Benoit Caldairou
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Sofie L Valk
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, FZ Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
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2
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Hettwer MD, Larivière S, Park BY, van den Heuvel OA, Schmaal L, Andreassen OA, Ching CRK, Hoogman M, Buitelaar J, van Rooij D, Veltman DJ, Stein DJ, Franke B, van Erp TGM, Jahanshad N, Thompson PM, Thomopoulos SI, Bethlehem RAI, Bernhardt BC, Eickhoff SB, Valk SL. Coordinated cortical thickness alterations across six neurodevelopmental and psychiatric disorders. Nat Commun 2022; 13:6851. [PMID: 36369423 PMCID: PMC9652311 DOI: 10.1038/s41467-022-34367-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 10/24/2022] [Indexed: 11/13/2022] Open
Abstract
Neuropsychiatric disorders are increasingly conceptualized as overlapping spectra sharing multi-level neurobiological alterations. However, whether transdiagnostic cortical alterations covary in a biologically meaningful way is currently unknown. Here, we studied co-alteration networks across six neurodevelopmental and psychiatric disorders, reflecting pathological structural covariance. In 12,024 patients and 18,969 controls from the ENIGMA consortium, we observed that co-alteration patterns followed normative connectome organization and were anchored to prefrontal and temporal disease epicenters. Manifold learning revealed frontal-to-temporal and sensory/limbic-to-occipitoparietal transdiagnostic gradients, differentiating shared illness effects on cortical thickness along these axes. The principal gradient aligned with a normative cortical thickness covariance gradient and established a transcriptomic link to cortico-cerebello-thalamic circuits. Moreover, transdiagnostic gradients segregated functional networks involved in basic sensory, attentional/perceptual, and domain-general cognitive processes, and distinguished between regional cytoarchitectonic profiles. Together, our findings indicate that shared illness effects occur in a synchronized fashion and along multiple levels of hierarchical cortical organization.
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Affiliation(s)
- M. D. Hettwer
- grid.411327.20000 0001 2176 9917Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany ,grid.419524.f0000 0001 0041 5028Max Planck School of Cognition, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany ,grid.8385.60000 0001 2297 375XInstitute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Centre Jülich, Jülich, Germany ,grid.419524.f0000 0001 0041 5028Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - S. Larivière
- grid.416102.00000 0004 0646 3639Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC Canada
| | - B. Y. Park
- grid.416102.00000 0004 0646 3639Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC Canada ,grid.202119.90000 0001 2364 8385Department of Data Science, Inha University, Incheon, Republic of Korea ,grid.410720.00000 0004 1784 4496Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
| | - O. A. van den Heuvel
- grid.484519.5Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy and Neuroscience and Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - L. Schmaal
- grid.1008.90000 0001 2179 088XCentre for Youth Mental Health, The University of Melbourne, Melbourne, VIC Australia ,grid.488501.00000 0004 8032 6923Orygen, Parkville, VIC Australia
| | - O. A. Andreassen
- grid.5510.10000 0004 1936 8921NORMENT Centre, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - C. R. K. Ching
- grid.42505.360000 0001 2156 6853Imaging Genetics Center, Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA USA
| | - M. Hoogman
- grid.10417.330000 0004 0444 9382Departments of Psychiatry and Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - J. Buitelaar
- grid.10417.330000 0004 0444 9382Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - D. van Rooij
- grid.10417.330000 0004 0444 9382Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - D. J. Veltman
- grid.484519.5Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy and Neuroscience and Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - D. J. Stein
- grid.7836.a0000 0004 1937 1151South African Medical Research Council Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry & Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - B. Franke
- grid.10417.330000 0004 0444 9382Departments of Psychiatry and Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - T. G. M. van Erp
- grid.266093.80000 0001 0668 7243Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine Hall, Irvine, CA USA ,grid.266093.80000 0001 0668 7243Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, CA USA
| | | | | | | | | | | | | | - N. Jahanshad
- grid.42505.360000 0001 2156 6853Imaging Genetics Center, Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA USA
| | - P. M. Thompson
- grid.42505.360000 0001 2156 6853Imaging Genetics Center, Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA USA
| | - S. I. Thomopoulos
- grid.42505.360000 0001 2156 6853Imaging Genetics Center, Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA USA
| | - R. A. I. Bethlehem
- grid.5335.00000000121885934Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK ,grid.5335.00000000121885934Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - B. C. Bernhardt
- grid.416102.00000 0004 0646 3639Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC Canada
| | - S. B. Eickhoff
- grid.411327.20000 0001 2176 9917Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany ,grid.8385.60000 0001 2297 375XInstitute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Centre Jülich, Jülich, Germany
| | - S. L. Valk
- grid.411327.20000 0001 2176 9917Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany ,grid.8385.60000 0001 2297 375XInstitute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Centre Jülich, Jülich, Germany ,grid.419524.f0000 0001 0041 5028Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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Di Biase MA, Geaghan MP, Reay WR, Seidlitz J, Weickert CS, Pébay A, Green MJ, Quidé Y, Atkins JR, Coleman MJ, Bouix S, Knyazhanskaya EE, Lyall AE, Pasternak O, Kubicki M, Rathi Y, Visco A, Gaunnac M, Lv J, Mesholam-Gately RI, Lewandowski KE, Holt DJ, Keshavan MS, Pantelis C, Öngür D, Breier A, Cairns MJ, Shenton ME, Zalesky A. Cell type-specific manifestations of cortical thickness heterogeneity in schizophrenia. Mol Psychiatry 2022; 27:2052-2060. [PMID: 35145230 PMCID: PMC9126812 DOI: 10.1038/s41380-022-01460-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 01/06/2022] [Accepted: 01/20/2022] [Indexed: 12/16/2022]
Abstract
Brain morphology differs markedly between individuals with schizophrenia, but the cellular and genetic basis of this heterogeneity is poorly understood. Here, we sought to determine whether cortical thickness (CTh) heterogeneity in schizophrenia relates to interregional variation in distinct neural cell types, as inferred from established gene expression data and person-specific genomic variation. This study comprised 1849 participants in total, including a discovery (140 cases and 1267 controls) and a validation cohort (335 cases and 185 controls). To characterize CTh heterogeneity, normative ranges were established for 34 cortical regions and the extent of deviation from these ranges was measured for each individual with schizophrenia. CTh deviations were explained by interregional gene expression levels of five out of seven neural cell types examined: (1) astrocytes; (2) endothelial cells; (3) oligodendrocyte progenitor cells (OPCs); (4) excitatory neurons; and (5) inhibitory neurons. Regional alignment between CTh alterations with cell type transcriptional maps distinguished broad patient subtypes, which were validated against genomic data drawn from the same individuals. In a predominantly neuronal/endothelial subtype (22% of patients), CTh deviations covaried with polygenic risk for schizophrenia (sczPRS) calculated specifically from genes marking neuronal and endothelial cells (r = -0.40, p = 0.010). Whereas, in a predominantly glia/OPC subtype (43% of patients), CTh deviations covaried with sczPRS calculated from glia and OPC-linked genes (r = -0.30, p = 0.028). This multi-scale analysis of genomic, transcriptomic, and brain phenotypic data may indicate that CTh heterogeneity in schizophrenia relates to inter-individual variation in cell-type specific functions. Decomposing heterogeneity in relation to cortical cell types enables prioritization of schizophrenia subsets for future disease modeling efforts.
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Affiliation(s)
- Maria A Di Biase
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia.
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Michael P Geaghan
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Newcastle, NSW, Australia
- Centre for Brain and Mental Health Research, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - William R Reay
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Newcastle, NSW, Australia
- Centre for Brain and Mental Health Research, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Cynthia Shannon Weickert
- Neuroscience Research Australia, Randwick, NSW, Australia
- Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, NSW, Australia
- Department of Neuroscience & Physiology, Upstate Medical University, Syracuse, NY, USA
| | - Alice Pébay
- Department of Anatomy and Physiology, School of Biomedical Sciences, The University of Melbourne, Melbourne, VIC, Australia
- Department of Surgery, Royal Melbourne Hospital, Melbourne Medical School, The University of Melbourne, Melbourne, VIC, Australia
| | - Melissa J Green
- Neuroscience Research Australia, Randwick, NSW, Australia
- Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, NSW, Australia
| | - Yann Quidé
- Neuroscience Research Australia, Randwick, NSW, Australia
- Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, NSW, Australia
| | - Joshua R Atkins
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Newcastle, NSW, Australia
| | - Michael J Coleman
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sylvain Bouix
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Amanda E Lyall
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ofer Pasternak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Marek Kubicki
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Yogesh Rathi
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Andrew Visco
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Megan Gaunnac
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Jinglei Lv
- School of Biomedical Engineering & Brain and Mind Centre, The University of Sydney, Camperdown, NSW, Australia
| | | | - Kathryn E Lewandowski
- Division of Psychotic Disorders, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Daphne J Holt
- Massachusetts General Hospital, Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - Matcheri S Keshavan
- Harvard Medical School and Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
| | - Dost Öngür
- Division of Psychotic Disorders, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Alan Breier
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Newcastle, NSW, Australia
- Centre for Brain and Mental Health Research, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Martha E Shenton
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
- Melbourne School of Engineering, The University of Melbourne, Parkville, VIC, Australia
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Burger B, Nenning KH, Schwartz E, Margulies DS, Goulas A, Liu H, Neubauer S, Dauwels J, Prayer D, Langs G. Disentangling cortical functional connectivity strength and topography reveals divergent roles of genes and environment. Neuroimage 2021; 247:118770. [PMID: 34861392 DOI: 10.1016/j.neuroimage.2021.118770] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 11/10/2021] [Accepted: 11/29/2021] [Indexed: 10/19/2022] Open
Abstract
The human brain varies across individuals in its morphology, function, and cognitive capacities. Variability is particularly high in phylogenetically modern regions associated with higher order cognitive abilities, but its relationship to the layout and strength of functional networks is poorly understood. In this study we disentangled the variability of two key aspects of functional connectivity: strength and topography. We then compared the genetic and environmental influences on these two features. Genetic contribution is heterogeneously distributed across the cortex and differs for strength and topography. In heteromodal areas genes predominantly affect the topography of networks, while their connectivity strength is shaped primarily by random environmental influence such as learning. We identified peak areas of genetic control of topography overlapping with parts of the processing stream from primary areas to network hubs in the default mode network, suggesting the coordination of spatial configurations across those processing pathways. These findings provide a detailed map of the diverse contribution of heritability and individual experience to the strength and topography of functional brain architecture.
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Affiliation(s)
- Bianca Burger
- Department of Biomedical Imaging and Image-Guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Vienna 1090, Austria
| | - Karl-Heinz Nenning
- Department of Biomedical Imaging and Image-Guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Vienna 1090, Austria; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, United States
| | - Ernst Schwartz
- Department of Biomedical Imaging and Image-Guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Vienna 1090, Austria
| | - Daniel S Margulies
- Université de Paris, CNRS, Integrative Neuroscience and Cognition Center, 75006 Paris, France; Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, United Kingdom
| | - Alexandros Goulas
- Institute for Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinstr. 52, 20246 Hamburg, Germany
| | - Hesheng Liu
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, 29466, USAs
| | - Simon Neubauer
- Department of Human Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig 04103, Germany
| | - Justin Dauwels
- TU Delft Fac. EEMCS Mekelweg 4 2628 CD Delft; Nayang Technological University, 639798, Singapore
| | - Daniela Prayer
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Neuroradiology and Musculo-skeletal Radiology, Medical University of Vienna, Vienna 1090, Austria
| | - Georg Langs
- Department of Biomedical Imaging and Image-Guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Vienna 1090, Austria; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, United States.
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5
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Arnatkeviciute A, Fulcher BD, Bellgrove MA, Fornito A. Imaging Transcriptomics of Brain Disorders. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2021; 2:319-331. [PMID: 36324650 PMCID: PMC9616271 DOI: 10.1016/j.bpsgos.2021.10.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 10/06/2021] [Accepted: 10/11/2021] [Indexed: 01/05/2023] Open
Abstract
Noninvasive neuroimaging is a powerful tool for quantifying diverse aspects of brain structure and function in vivo, and it has been used extensively to map the neural changes associated with various brain disorders. However, most neuroimaging techniques offer only indirect measures of underlying pathological mechanisms. The recent development of anatomically comprehensive gene expression atlases has opened new opportunities for studying the transcriptional correlates of noninvasively measured neural phenotypes, offering a rich framework for evaluating pathophysiological hypotheses and putative mechanisms. Here, we provide an overview of some fundamental methods in imaging transcriptomics and outline their application to understanding brain disorders of neurodevelopment, adulthood, and neurodegeneration. Converging evidence indicates that spatial variations in gene expression are linked to normative changes in brain structure during age-related maturation and neurodegeneration that are in part associated with cell-specific gene expression markers of gene expression. Transcriptional correlates of disorder-related neuroimaging phenotypes are also linked to transcriptionally dysregulated genes identified in ex vivo analyses of patient brains. Modeling studies demonstrate that spatial patterns of gene expression are involved in regional vulnerability to neurodegeneration and the spread of disease across the brain. This growing body of work supports the utility of transcriptional atlases in testing hypotheses about the molecular mechanism driving disease-related changes in macroscopic neuroimaging phenotypes.
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Affiliation(s)
- Aurina Arnatkeviciute
- Turner Institute for Brain and Mental Health, School of Psychological Science, Monash University, Melbourne, Victoria, Australia
- Address correspondence to Aurina Arnatkeviciute, Ph.D
| | - Ben D. Fulcher
- School of Physics, The University of Sydney, Camperdown, New South Wales, Australia
| | - Mark A. Bellgrove
- Turner Institute for Brain and Mental Health, School of Psychological Science, Monash University, Melbourne, Victoria, Australia
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Science, Monash University, Melbourne, Victoria, Australia
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Xenophontos A, Seidlitz J, Liu S, Clasen LS, Blumenthal JD, Giedd JN, Alexander-Bloch A, Raznahan A. Altered Sex Chromosome Dosage Induces Coordinated Shifts in Cortical Anatomy and Anatomical Covariance. Cereb Cortex 2021; 30:2215-2228. [PMID: 31828307 DOI: 10.1093/cercor/bhz235] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Sex chromosome dosage (SCD) variation increases risk for neuropsychiatric impairment, which may reflect direct SCD effects on brain organization. Here, we 1) map cumulative X- and Y-chromosome dosage effects on regional cortical thickness (CT) and investigate potential functional implications of these effects using Neurosynth, 2) test if this map is organized by patterns of CT covariance that are evident in health, and 3) characterize SCD effects on CT covariance itself. We modeled SCD effects on CT and CT covariance for 308 equally sized regions of the cortical sheet using structural neuroimaging data from 301 individuals with varying numbers of sex chromosomes (169 euploid, 132 aneuploid). Mounting SCD increased CT in the rostral frontal cortex and decreased CT in the lateral temporal cortex, bilaterally. Regions targeted by SCD were associated with social functioning, language processing, and comprehension. Cortical regions with a similar degree of SCD-sensitivity showed heightened CT covariance in health. Finally, greater SCD also increased covariance among regions similarly affected by SCD. Our study both 1) develops novel methods for comparing typical and disease-related structural covariance networks in the brain and 2) uses these techniques to resolve and identify organizing principles for SCD effects on regional cortical anatomy and anatomical covariance.
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Affiliation(s)
- Anastasia Xenophontos
- Developmental Neurogenomics Unit, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Jakob Seidlitz
- Developmental Neurogenomics Unit, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD 20892, USA.,Department of Psychiatry, University of Cambridge, Cambridge CB2 1TN, UK
| | - Siyuan Liu
- Developmental Neurogenomics Unit, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Liv S Clasen
- Developmental Neurogenomics Unit, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Jonathan D Blumenthal
- Developmental Neurogenomics Unit, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Jay N Giedd
- Department of Psychiatry, University of California, La Jolla, CA 92093, USA
| | - Aaron Alexander-Bloch
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA 19104.,Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
| | - Armin Raznahan
- Developmental Neurogenomics Unit, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD 20892, USA
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7
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White TL, Gonsalves MA. Dignity neuroscience: universal rights are rooted in human brain science. Ann N Y Acad Sci 2021; 1505:40-54. [PMID: 34350987 PMCID: PMC9291326 DOI: 10.1111/nyas.14670] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 06/25/2021] [Accepted: 07/08/2021] [Indexed: 01/22/2023]
Abstract
Universal human rights are defined by international agreements, law, foreign policy, and the concept of inherent human dignity. However, rights defined on this basis can be readily subverted by overt and covert disagreements and can be treated as distant geopolitical events rather than bearing on individuals’ everyday lives. A robust case for universal human rights is urgently needed and must meet several disparate requirements: (1) a framework that resolves tautological definitions reached solely by mutual, revocable agreement; (2) a rationale that transcends differences in beliefs, creed, and culture; and (3) a personalization that empowers both individuals and governments to further human rights protections. We propose that human rights in existing agreements comprise five elemental types: (1) agency, autonomy, and self‐determination; (2) freedom from want; (3) freedom from fear; (4) uniqueness; and (5) unconditionality, including protections for vulnerable populations. We further propose these rights and protections are rooted in fundamental properties of the human brain. We provide a robust, empirical foundation for universal rights based on emerging work in human brain science that we term dignity neuroscience. Dignity neuroscience provides an empirical foundation to support and foster human dignity, universal rights, and their active furtherance by individuals, nations, and international law.
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Affiliation(s)
- Tara L White
- Department of Behavioral and Social Sciences, School of Public Health, Brown University, Providence, Rhode Island.,Center for Alcohol and Addiction Studies, Brown University, Providence, Rhode Island.,Carney Institute for Brain Science, Brown University, Providence, Rhode Island.,University of Cambridge, England, Cambridge, UK
| | - Meghan A Gonsalves
- Neuroscience Graduate Program, Brown University, Providence, Rhode Island
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8
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Genome-wide haplotype association study in imaging genetics using whole-brain sulcal openings of 16,304 UK Biobank subjects. Eur J Hum Genet 2021; 29:1424-1437. [PMID: 33664500 PMCID: PMC8440755 DOI: 10.1038/s41431-021-00827-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 12/18/2020] [Accepted: 02/04/2021] [Indexed: 11/29/2022] Open
Abstract
Neuroimaging-genetics cohorts gather two types of data: brain imaging and genetic data. They allow the discovery of associations between genetic variants and brain imaging features. They are invaluable resources to study the influence of genetics and environment in the brain features variance observed in normal and pathological populations. This study presents a genome-wide haplotype analysis for 123 brain sulcus opening value (a measure of sulcal width) across the whole brain that include 16,304 subjects from UK Biobank. Using genetic maps, we defined 119,548 blocks of low recombination rate distributed along the 22 autosomal chromosomes and analyzed 1,051,316 haplotypes. To test associations between haplotypes and complex traits, we designed three statistical approaches. Two of them use a model that includes all the haplotypes for a single block, while the last approach considers each haplotype independently. All the statistics produced were assessed as rigorously as possible. Thanks to the rich imaging dataset at hand, we used resampling techniques to assess False Positive Rate for each statistical approach in a genome-wide and brain-wide context. The results on real data show that genome-wide haplotype analyses are more sensitive than single-SNP approach and account for local complex Linkage Disequilibrium (LD) structure, which makes genome-wide haplotype analysis an interesting and statistically sound alternative to the single-SNP counterpart.
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9
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Valk SL, Xu T, Margulies DS, Masouleh SK, Paquola C, Goulas A, Kochunov P, Smallwood J, Yeo BTT, Bernhardt BC, Eickhoff SB. Shaping brain structure: Genetic and phylogenetic axes of macroscale organization of cortical thickness. SCIENCE ADVANCES 2020; 6:eabb3417. [PMID: 32978162 PMCID: PMC7518868 DOI: 10.1126/sciadv.abb3417] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Accepted: 08/04/2020] [Indexed: 05/16/2023]
Abstract
The topology of the cerebral cortex has been proposed to provide an important source of constraint for the organization of cognition. In a sample of twins (n = 1113), we determined structural covariance of thickness to be organized along both a posterior-to-anterior and an inferior-to-superior axis. Both organizational axes were present when investigating the genetic correlation of cortical thickness, suggesting a strong genetic component in humans, and had a comparable organization in macaques, demonstrating they are phylogenetically conserved in primates. In both species, the inferior-superior dimension of cortical organization aligned with the predictions of dual-origin theory, and in humans, we found that the posterior-to-anterior axis related to a functional topography describing a continuum of functions from basic processes involved in perception and action to more abstract features of human cognition. Together, our study provides important insights into how functional and evolutionary patterns converge at the level of macroscale cortical structural organization.
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Affiliation(s)
- Sofie L Valk
- Institute of Neuroscience and Medicine (INM-7: Brain and Behavior), Research Centre Jülich, Jülich, Germany.
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA
| | - Daniel S Margulies
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA
- Frontlab, Centre National de la Recherche Scientifique Institut du Cerveau et de la Moelle Épinière, Paris, France
| | - Shahrzad Kharabian Masouleh
- Institute of Neuroscience and Medicine (INM-7: Brain and Behavior), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Casey Paquola
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Alexandros Goulas
- Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Hamburg, Germany
| | - Peter Kochunov
- Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, Centre for Sleep and Cognition, Centre for Translational MR Research and N.1 Institute for Health, National University of Singapore, Singapore, Singapore
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7: Brain and Behavior), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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10
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Alexander-Bloch AF, Raznahan A, Vandekar SN, Seidlitz J, Lu Z, Mathias SR, Knowles E, Mollon J, Rodrigue A, Curran JE, Görring HHH, Satterthwaite TD, Gur RE, Bassett DS, Hoftman GD, Pearlson G, Shinohara RT, Liu S, Fox PT, Almasy L, Blangero J, Glahn DC. Imaging local genetic influences on cortical folding. Proc Natl Acad Sci U S A 2020; 117:7430-7436. [PMID: 32170019 PMCID: PMC7132284 DOI: 10.1073/pnas.1912064117] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Recent progress in deciphering mechanisms of human brain cortical folding leave unexplained whether spatially patterned genetic influences contribute to this folding. High-resolution in vivo brain MRI can be used to estimate genetic correlations (covariability due to shared genetic factors) in interregional cortical thickness, and biomechanical studies predict an influence of cortical thickness on folding patterns. However, progress has been hampered because shared genetic influences related to folding patterns likely operate at a scale that is much more local (<1 cm) than that addressed in prior imaging studies. Here, we develop methodological approaches to examine local genetic influences on cortical thickness and apply these methods to two large, independent samples. We find that such influences are markedly heterogeneous in strength, and in some cortical areas are notably stronger in specific orientations relative to gyri or sulci. The overall, phenotypic local correlation has a significant basis in shared genetic factors and is highly symmetric between left and right cortical hemispheres. Furthermore, the degree of local cortical folding relates systematically with the strength of local correlations, which tends to be higher in gyral crests and lower in sulcal fundi. The relationship between folding and local correlations is stronger in primary sensorimotor areas and weaker in association areas such as prefrontal cortex, consistent with reduced genetic constraints on the structural topology of association cortex. Collectively, our results suggest that patterned genetic influences on cortical thickness, measurable at the scale of in vivo MRI, may be a causal factor in the development of cortical folding.
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Affiliation(s)
- Aaron F Alexander-Bloch
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA 19104;
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
| | | | - Simon N Vandekar
- Department of Biostatistics, Vanderbilt University, Nashville, TN 37203
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA 19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
| | - Zhixin Lu
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
| | - Samuel R Mathias
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115
| | - Emma Knowles
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115
| | - Josephine Mollon
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115
| | - Amanda Rodrigue
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115
| | - Joanne E Curran
- Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520
| | - Harald H H Görring
- Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520
| | | | - Raquel E Gur
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA 19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
| | - Danielle S Bassett
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104
| | - Gil D Hoftman
- Department of Psychiatry, University of California, Los Angeles, CA 90095
| | - Godfrey Pearlson
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511
- Olin Neuropsychiatric Research Center, Institute of Living, Hartford, CT 06102
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, University of Pennsylvania, Philadelphia, PA 19104
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104
| | - Siyuan Liu
- National Institute of Mental Health, Bethesda, MD 20814
| | - Peter T Fox
- Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520
| | - Laura Almasy
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA 19104
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104
| | - John Blangero
- Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520
| | - David C Glahn
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115
- Olin Neuropsychiatric Research Center, Institute of Living, Hartford, CT 06102
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11
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Vogel JW, La Joie R, Grothe MJ, Diaz-Papkovich A, Doyle A, Vachon-Presseau E, Lepage C, Vos de Wael R, Thomas RA, Iturria-Medina Y, Bernhardt B, Rabinovici GD, Evans AC. A molecular gradient along the longitudinal axis of the human hippocampus informs large-scale behavioral systems. Nat Commun 2020; 11:960. [PMID: 32075960 PMCID: PMC7031290 DOI: 10.1038/s41467-020-14518-3] [Citation(s) in RCA: 77] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Accepted: 12/09/2019] [Indexed: 12/20/2022] Open
Abstract
The functional organization of the hippocampus is distributed as a gradient along its longitudinal axis that explains its differential interaction with diverse brain systems. We show that the location of human tissue samples extracted along the longitudinal axis of the adult human hippocampus can be predicted within 2mm using the expression pattern of less than 100 genes. Futhermore, this model generalizes to an external set of tissue samples from prenatal human hippocampi. We examine variation in this specific gene expression pattern across the whole brain, finding a distinct anterioventral-posteriodorsal gradient. We find frontal and anterior temporal regions involved in social and motivational behaviors, and more functionally connected to the anterior hippocampus, to be clearly differentiated from posterior parieto-occipital regions involved in visuospatial cognition and more functionally connected to the posterior hippocampus. These findings place the human hippocampus at the interface of two major brain systems defined by a single molecular gradient.
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Affiliation(s)
- Jacob W Vogel
- Montreal Neurological Institute, McGill University, Montréal, QC, Canada.
| | - Renaud La Joie
- Memory and Aging Center, University of California, San Francisco, CA, USA
| | - Michel J Grothe
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - Alexandr Diaz-Papkovich
- McGill University and Genome Quebec Innovation Centre, Montréal, QC, Canada
- Quantitative Life Sciences, McGill University, Montreal, QC, H3A 0G1, Canada
| | - Andrew Doyle
- Montreal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Etienne Vachon-Presseau
- Faculty of Dentistry, Department of Anesthesia, McGill University, Montréal, QC, Canada
- Alan Edwards Centre for Research on Pain (AECRP), McGill University, Montréal, QC, Canada
| | - Claude Lepage
- Montreal Neurological Institute, McGill University, Montréal, QC, Canada
| | | | - Rhalena A Thomas
- Montreal Neurological Institute, McGill University, Montréal, QC, Canada
| | | | - Boris Bernhardt
- Montreal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Gil D Rabinovici
- Memory and Aging Center, University of California, San Francisco, CA, USA
| | - Alan C Evans
- Montreal Neurological Institute, McGill University, Montréal, QC, Canada.
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12
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On testing for spatial correspondence between maps of human brain structure and function. Neuroimage 2018; 178:540-551. [PMID: 29860082 DOI: 10.1016/j.neuroimage.2018.05.070] [Citation(s) in RCA: 272] [Impact Index Per Article: 45.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Revised: 04/23/2018] [Accepted: 05/30/2018] [Indexed: 01/28/2023] Open
Abstract
A critical issue in many neuroimaging studies is the comparison between brain maps. Nonetheless, it remains unclear how one should test hypotheses focused on the overlap or spatial correspondence between two or more brain maps. This "correspondence problem" affects, for example, the interpretation of comparisons between task-based patterns of functional activation, resting-state networks or modules, and neuroanatomical landmarks. To date, this problem has been addressed with remarkable variability in terms of methodological approaches and statistical rigor. In this paper, we address the correspondence problem using a spatial permutation framework to generate null models of overlap by applying random rotations to spherical representations of the cortical surface, an approach for which we also provide a theoretical statistical foundation. We use this method to derive clusters of cognitive functions that are correlated in terms of their functional neuroatomical substrates. In addition, using publicly available data, we formally demonstrate the correspondence between maps of task-based functional activity, resting-state fMRI networks and gyral-based anatomical landmarks. We provide open-access code to implement the methods presented for two commonly-used tools for surface based cortical analysis (https://www.github.com/spin-test). This spatial permutation approach constitutes a useful advance over widely-used methods for the comparison of cortical maps, thereby opening new possibilities for the integration of diverse neuroimaging data.
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