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Fjell AM. Aging Brain from a Lifespan Perspective. Curr Top Behav Neurosci 2024. [PMID: 38797799 DOI: 10.1007/7854_2024_476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Research during the last two decades has shown that the brain undergoes continuous changes throughout life, with substantial heterogeneity in age trajectories between regions. Especially, temporal and prefrontal cortices show large changes, and these correlate modestly with changes in the corresponding cognitive abilities such as episodic memory and executive function. Changes seen in normal aging overlap with changes seen in neurodegenerative conditions such as Alzheimer's disease; differences between what reflects normal aging vs. a disease-related change are often blurry. This calls for a dimensional view on cognitive decline in aging, where clear-cut distinctions between normality and pathology cannot be always drawn. Although much progress has been made in describing typical patterns of age-related changes in the brain, identifying risk and protective factors, and mapping cognitive correlates, there are still limits to our knowledge that should be addressed by future research. We need more longitudinal studies following the same participants over longer time intervals with cognitive testing and brain imaging, and an increased focus on the representativeness vs. selection bias in neuroimaging research of aging.
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Affiliation(s)
- Anders Martin Fjell
- Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway.
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2
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Phan TX, Baratono S, Drew W, Tetreault AM, Fox MD, Darby RR. Increased Cortical Thickness in Alzheimer's Disease. Ann Neurol 2024; 95:929-940. [PMID: 38400760 PMCID: PMC11060923 DOI: 10.1002/ana.26894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 01/31/2024] [Accepted: 02/03/2024] [Indexed: 02/26/2024]
Abstract
OBJECTIVE Patients with Alzheimer's disease (AD) have diffuse brain atrophy, but some regions, such as the anterior cingulate cortex (ACC), are spared and may even show increase in size compared to controls. The extent, clinical significance, and mechanisms associated with increased cortical thickness in AD remain unknown. Recent work suggested neural facilitation of regions anticorrelated to atrophied regions in frontotemporal dementia. Here, we aim to determine whether increased thickness occurs in sporadic AD, whether it relates to clinical symptoms, and whether it occur in brain regions functionally connected to-but anticorrelated with-locations of atrophy. METHODS Cross-sectional clinical, neuropsychological, and neuroimaging data from the Alzheimer's Disease Neuroimaging Initiative were analyzed to investigate cortical thickness in AD subjects versus controls. Atrophy network mapping was used to identify brain regions functionally connected to locations of increased thickness and atrophy. RESULTS AD patients showed increased thickness in the ACC in a region-of-interest analysis and the visual cortex in an exploratory analysis. Increased thickness in the left ACC was associated with preserved cognitive function, while increased thickness in the left visual cortex was associated with hallucinations. Finally, we found that locations of increased thickness were functionally connected to, but anticorrelated with, locations of brain atrophy (r = -0.81, p < 0.05). INTERPRETATION Our results suggest that increased cortical thickness in Alzheimer's disease is relevant to AD symptoms and preferentially occur in brain regions functionally connected to, but anticorrelated with, areas of brain atrophy. Implications for models of compensatory neuroplasticity in response to neurodegeneration are discussed. ANN NEUROL 2024;95:929-940.
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Affiliation(s)
- Tony X. Phan
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN
| | - Sheena Baratono
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - William Drew
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Aaron M. Tetreault
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN
| | - Michael D. Fox
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - R. Ryan Darby
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN
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3
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Park HRP, Chilver MR, Quidé Y, Montalto A, Schofield PR, Williams LM, Gatt JM. Heritability of cognitive and emotion processing during functional MRI in a twin sample. Hum Brain Mapp 2024; 45:e26557. [PMID: 38224545 PMCID: PMC10785190 DOI: 10.1002/hbm.26557] [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: 05/24/2023] [Revised: 11/22/2023] [Accepted: 11/25/2023] [Indexed: 01/17/2024] Open
Abstract
Despite compelling evidence that brain structure is heritable, the evidence for the heritability of task-evoked brain function is less robust. Findings from previous studies are inconsistent possibly reflecting small samples and methodological variations. In a large national twin sample, we systematically evaluated heritability of task-evoked brain activity derived from functional magnetic resonance imaging. We used established standardised tasks to engage brain regions involved in cognitive and emotional functions. Heritability was evaluated across a conscious and nonconscious Facial Expressions of Emotion Task (FEET), selective attention Oddball Task, N-back task of working memory maintenance, and a Go-NoGo cognitive control task in a sample of Australian adult twins (N ranged from 136 to 226 participants depending on the task and pairs). Two methods for quantifying associations of heritability and brain activity were utilised; a multivariate independent component analysis (ICA) approach and a univariate brain region-of-interest (ROI) approach. Using ICA, we observed that a significant proportion of task-evoked brain activity was heritable, with estimates ranging from 23% to 26% for activity elicited by nonconscious facial emotion stimuli, 27% to 34% for N-back working memory maintenance and sustained attention, and 32% to 33% for selective attention in the Oddball task. Using the ROI approach, we found that activity of regions specifically implicated in emotion processing and selective attention showed significant heritability for three ROIs, including estimates of 33%-34% for the left and right amygdala in the nonconscious processing of sad faces and 29% in the medial superior prefrontal cortex for the Oddball task. Although both approaches show similar levels of heritability for the Nonconscious Faces and Oddball tasks, ICA results displayed a more extensive network of heritable brain function, including additional regions beyond the ROI analysis. Furthermore, multivariate twin modelling of both ICA networks and ROI activation suggested a mix of common genetic and unique environmental factors that contribute to the associations between networks/regions. Together, the results indicate a complex relationship between genetic factors and environmental interactions that ultimately give rise to neural activation underlying cognition and emotion.
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Affiliation(s)
- Haeme R. P. Park
- Neuroscience Research AustraliaSydneyNew South WalesAustralia
- School of PsychologyUniversity of New South WalesSydneyNew South WalesAustralia
| | - Miranda R. Chilver
- Neuroscience Research AustraliaSydneyNew South WalesAustralia
- School of PsychologyUniversity of New South WalesSydneyNew South WalesAustralia
| | - Yann Quidé
- Neuroscience Research AustraliaSydneyNew South WalesAustralia
- School of PsychologyUniversity of New South WalesSydneyNew South WalesAustralia
| | - Arthur Montalto
- Neuroscience Research AustraliaSydneyNew South WalesAustralia
- School of PsychologyUniversity of New South WalesSydneyNew South WalesAustralia
| | - Peter R. Schofield
- Neuroscience Research AustraliaSydneyNew South WalesAustralia
- School of Biomedical SciencesUniversity of New South WalesSydneyNew South WalesAustralia
| | - Leanne M. Williams
- Psychiatry and Behavioral Sciences, Stanford School of MedicineStanford UniversityCaliforniaUSA
| | - Justine M. Gatt
- Neuroscience Research AustraliaSydneyNew South WalesAustralia
- School of PsychologyUniversity of New South WalesSydneyNew South WalesAustralia
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4
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Wang B, Irizar H, Thygesen JH, Zartaloudi E, Austin-Zimmerman I, Bhat A, Harju-Seppänen J, Pain O, Bass N, Gkofa V, Alizadeh BZ, van Amelsvoort T, Arranz MJ, Bender S, Cahn W, Stella Calafato M, Crespo-Facorro B, Di Forti M, Giegling I, de Haan L, Hall J, Hall MH, van Haren N, Iyegbe C, Kahn RS, Kravariti E, Lawrie SM, Lin K, Luykx JJ, Mata I, McDonald C, McIntosh AM, Murray RM, Picchioni M, Powell J, Prata DP, Rujescu D, Rutten BPF, Shaikh M, Simons CJP, Toulopoulou T, Weisbrod M, van Winkel R, Kuchenbaecker K, McQuillin A, Bramon E. Psychosis Endophenotypes: A Gene-Set-Specific Polygenic Risk Score Analysis. Schizophr Bull 2023; 49:1625-1636. [PMID: 37582581 PMCID: PMC10686343 DOI: 10.1093/schbul/sbad088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/17/2023]
Abstract
BACKGROUND AND HYPOTHESIS Endophenotypes can help to bridge the gap between psychosis and its genetic predispositions, but their underlying mechanisms remain largely unknown. This study aims to identify biological mechanisms that are relevant to the endophenotypes for psychosis, by partitioning polygenic risk scores into specific gene sets and testing their associations with endophenotypes. STUDY DESIGN We computed polygenic risk scores for schizophrenia and bipolar disorder restricted to brain-related gene sets retrieved from public databases and previous publications. Three hundred and seventy-eight gene-set-specific polygenic risk scores were generated for 4506 participants. Seven endophenotypes were also measured in the sample. Linear mixed-effects models were fitted to test associations between each endophenotype and each gene-set-specific polygenic risk score. STUDY RESULTS After correction for multiple testing, we found that a reduced P300 amplitude was associated with a higher schizophrenia polygenic risk score of the forebrain regionalization gene set (mean difference per SD increase in the polygenic risk score: -1.15 µV; 95% CI: -1.70 to -0.59 µV; P = 6 × 10-5). The schizophrenia polygenic risk score of forebrain regionalization also explained more variance of the P300 amplitude (R2 = 0.032) than other polygenic risk scores, including the genome-wide polygenic risk scores. CONCLUSIONS Our finding on reduced P300 amplitudes suggests that certain genetic variants alter early brain development thereby increasing schizophrenia risk years later. Gene-set-specific polygenic risk scores are a useful tool to elucidate biological mechanisms of psychosis and endophenotypes, offering leads for experimental validation in cellular and animal models.
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Affiliation(s)
- Baihan Wang
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Haritz Irizar
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Johan H Thygesen
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
- Institute of Health Informatics, University College London, London, UK
| | - Eirini Zartaloudi
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
| | - Isabelle Austin-Zimmerman
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Anjali Bhat
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
| | - Jasmine Harju-Seppänen
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
| | - Oliver Pain
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Nick Bass
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
| | - Vasiliki Gkofa
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
| | - Behrooz Z Alizadeh
- University of Groningen, University Medical Center Groningen, University Center for Psychiatry, Rob Giel Research Center, Groningen, The Netherlands
- Department of Epidemiology, University Medical Center Groningen, Groningen, The Netherlands
| | - Therese van Amelsvoort
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Maria J Arranz
- Fundació Docència i Recerca Mutua Terrassa, Terrassa, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Institut de Recerca Biomédica Sant Pau (IIB-Sant Pau), Barcelona, Spain
| | - Stephan Bender
- Department of Child and Adolescent Psychiatry, Psychosomatic Medicine and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Wiepke Cahn
- Department of Psychiatry, Brain Centre Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Altrecht, General Mental Health Care, Utrecht, The Netherlands
| | - Maria Stella Calafato
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
| | - Benedicto Crespo-Facorro
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Sevilla, Spain
- Department of Psychiatry, University Hospital Virgen del Rocio, School of Medicine, University of Sevilla–IBiS, Sevilla, Spain
| | - Marta Di Forti
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | | | - Ina Giegling
- Comprehensive Centers for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Lieuwe de Haan
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Arkin, Institute for Mental Health, Amsterdam, The Netherlands
| | - Jeremy Hall
- Neuroscience and Mental Health Innovation Institute, School of Medicine, Cardiff University, Hadyn Ellis Building, Mandy Road, Cardiff, UK
| | - Mei-Hua Hall
- Psychosis Neurobiology Laboratory, Harvard Medical School, McLean Hospital, Belmont, MA, USA
| | - Neeltje van Haren
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Sophia’s Children Hospital, Rotterdam, The Netherlands
| | - Conrad Iyegbe
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - René S Kahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Eugenia Kravariti
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Stephen M Lawrie
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Kuang Lin
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jurjen J Luykx
- Department of Psychiatry, Brain Centre Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Department of Translational Neuroscience, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Ignacio Mata
- Fundacion Argibide, Pamplona, Spain
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
| | - Colm McDonald
- The Centre for Neuroimaging & Cognitive Genomics (NICOG) and NCBES Galway Neuroscience Centre, University of Galway, Galway, Ireland
| | - Andrew M McIntosh
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Robin M Murray
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | | | - Marco Picchioni
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- St Magnus Hospital, Surrey, UK
| | - John Powell
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Diana P Prata
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciencias da Universidade de Lisboa, Portugal
| | - Dan Rujescu
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Division of General Psychiatry, Medical University of Vienna, Austria
| | - Bart P F Rutten
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Madiha Shaikh
- North East London Foundation Trust, London, UK
- Research Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | - Claudia J P Simons
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
- GGzE Institute for Mental Health Care, Eindhoven, The Netherlands
| | - Timothea Toulopoulou
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Interdisciplinary Program in Neuroscience, Aysel Sabuncu Brain Research Center, Bilkent University, Ankara, Türkiye
- National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Türkiye
- Department of Psychology, Bilkent University, Ankara, Türkiye
- School of Medicine, Department of Psychiatry, National and Kapodistrian University of Athens, Athens, Greece
- Department of Psychiatry and Behavioral Health System, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Matthias Weisbrod
- Department of General Psychiatry, Center of Psychosocial Medicine, University of Heidelberg, Germany
- SRH Klinikum, Karlsbad-Langensteinbach, Germany
| | - Ruud van Winkel
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
- KU Leuven, Department of Neuroscience, Research Group Psychiatry, Leuven, Belgium
| | - Karoline Kuchenbaecker
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
- UCL Genetics Institute, Division of Biosciences, University College London, London, UK
| | - Andrew McQuillin
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
| | - Elvira Bramon
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
- Institute of Cognitive Neuroscience, University College London, London, UK
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5
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Wang H, Makowski C, Zhang Y, Qi A, Kaufmann T, Smeland OB, Fiecas M, Yang J, Visscher PM, Chen CH. Chromosomal inversion polymorphisms shape human brain morphology. Cell Rep 2023; 42:112896. [PMID: 37505983 PMCID: PMC10508191 DOI: 10.1016/j.celrep.2023.112896] [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: 12/23/2022] [Revised: 06/27/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
The impact of chromosomal inversions on human brain morphology remains underexplored. We studied 35 common inversions classified from genotypes of 33,018 adults with European ancestry. The inversions at 2p22.3, 16p11.2, and 17q21.31 reach genome-wide significance, followed by 8p23.1 and 6p21.33, in their association with cortical and subcortical morphology. The 17q21.31, 8p23.1, and 16p11.2 regions comprise the LRRC37, OR7E, and NPIP duplicated gene families. We find the 17q21.31 MAPT inversion region, known for harboring neurological risk, to be the most salient locus among common variants for shaping and patterning the cortex. Overall, we observe the inverted orientations decreasing brain size, with the exception that the 2p22.3 inversion is associated with increased subcortical volume and the 8p23.1 inversion is associated with increased motor cortex. These significant inversions are in the genomic hotspots of neuropsychiatric loci. Our findings are generalizable to 3,472 children and demonstrate inversions as essential genetic variation to understand human brain phenotypes.
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Affiliation(s)
- Hao Wang
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA 92093, USA
| | - Carolina Makowski
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA 92093, USA
| | - Yanxiao Zhang
- Ludwig Institute for Cancer Research, La Jolla, CA 92093, USA; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Anna Qi
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA 92093, USA
| | - Tobias Kaufmann
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, 72076 Tübingen, Germany; Norwegian Centre for Mental Disorders Research, Oslo University Hospital and University of Oslo, 0450 Oslo, Norway
| | - Olav B Smeland
- Norwegian Centre for Mental Disorders Research, Oslo University Hospital and University of Oslo, 0450 Oslo, Norway
| | - Mark Fiecas
- Division of Biostatistics, University of Minnesota School of Public Health, Minneapolis, MN 55455, USA
| | - Jian Yang
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Chi-Hua Chen
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA 92093, USA.
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6
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Huang Y, Wu Z, Li T, Wang X, Wang Y, Xing L, Zhu H, Lin W, Wang L, Guo L, Gilmore JH, Li G. Mapping Genetic Topography of Cortical Thickness and Surface Area in Neonatal Brains. J Neurosci 2023; 43:6010-6020. [PMID: 37369585 PMCID: PMC10451118 DOI: 10.1523/jneurosci.1841-22.2023] [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: 09/26/2022] [Revised: 06/05/2023] [Accepted: 06/10/2023] [Indexed: 06/29/2023] Open
Abstract
Adult twin neuroimaging studies have revealed that cortical thickness (CT) and surface area (SA) are differentially influenced by genetic information, leading to their spatially distinct genetic patterning and topography. However, the postnatal origins of the genetic topography of CT and SA remain unclear, given the dramatic cortical development from neonates to adults. To fill this critical gap, this study unprecedentedly explored how genetic information differentially regulates the spatial topography of CT and SA in the neonatal brain by leveraging brain magnetic resonance (MR) images from 202 twin neonates with minimal influence by the complicated postnatal environmental factors. We capitalized on infant-dedicated computational tools and a data-driven spectral clustering method to parcellate the cerebral cortex into a set of distinct regions purely according to the genetic correlation of cortical vertices in terms of CT and SA, respectively, and accordingly created the first genetically informed cortical parcellation maps of neonatal brains. Both genetic parcellation maps exhibit bilaterally symmetric and hierarchical patterns, but distinct spatial layouts. For CT, regions with closer genetic relationships demonstrate an anterior-posterior (A-P) division, while for SA, regions with greater genetic proximity are typically within the same lobe. Certain genetically informed regions exhibit strong similarities between neonates and adults, with the most striking similarities in the medial surface in terms of SA, despite their overall substantial differences in genetic parcellation maps. These results greatly advance our understanding of the development of genetic influences on the spatial patterning of cortical morphology.SIGNIFICANCE STATEMENT Genetic influences on cortical thickness (CT) and surface area (SA) are complex and could evolve throughout the lifespan. However, studies revealing distinct genetic topography of CT and SA have been limited to adults. Using brain structural magnetic resonance (MR) images of twins, we unprecedentedly discovered the distinct genetically-informed parcellation maps of CT and SA in neonatal brains, respectively. Each genetic parcellation map comprises a distinct spatial layout of cortical regions, where vertices within the same region share high genetic correlation. These genetic parcellation maps of CT and SA of neonates largely differ from those of adults, despite their highly remarkable similarities in the medial cortex of SA. These discoveries provide important insights into the genetic organization of the early cerebral cortex development.
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Affiliation(s)
- Ying Huang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514
| | - Tengfei Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514
| | - Xifeng Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27516
| | - Ya Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514
| | - Lei Xing
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27516
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi 710129, China
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514
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7
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Lee HM, Hong SJ, Gill R, Caldairou B, Wang I, Zhang JG, Deleo F, Schrader D, Bartolomei F, Guye M, Cho KH, Barba C, Sisodiya S, Jackson G, Hogan RE, Wong-Kisiel L, Cascino GD, Schulze-Bonhage A, Lopes-Cendes I, Cendes F, Guerrini R, Bernhardt B, Bernasconi N, Bernasconi A. Multimodal mapping of regional brain vulnerability to focal cortical dysplasia. Brain 2023; 146:3404-3415. [PMID: 36852571 PMCID: PMC10393418 DOI: 10.1093/brain/awad060] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 01/17/2023] [Accepted: 02/02/2023] [Indexed: 03/01/2023] Open
Abstract
Focal cortical dysplasia (FCD) type II is a highly epileptogenic developmental malformation and a common cause of surgically treated drug-resistant epilepsy. While clinical observations suggest frequent occurrence in the frontal lobe, mechanisms for such propensity remain unexplored. Here, we hypothesized that cortex-wide spatial associations of FCD distribution with cortical cytoarchitecture, gene expression and organizational axes may offer complementary insights into processes that predispose given cortical regions to harbour FCD. We mapped the cortex-wide MRI distribution of FCDs in 337 patients collected from 13 sites worldwide. We then determined its associations with (i) cytoarchitectural features using histological atlases by Von Economo and Koskinas and BigBrain; (ii) whole-brain gene expression and spatiotemporal dynamics from prenatal to adulthood stages using the Allen Human Brain Atlas and PsychENCODE BrainSpan; and (iii) macroscale developmental axes of cortical organization. FCD lesions were preferentially located in the prefrontal and fronto-limbic cortices typified by low neuron density, large soma and thick grey matter. Transcriptomic associations with FCD distribution uncovered a prenatal component related to neuroglial proliferation and differentiation, likely accounting for the dysplastic makeup, and a postnatal component related to synaptogenesis and circuit organization, possibly contributing to circuit-level hyperexcitability. FCD distribution showed a strong association with the anterior region of the antero-posterior axis derived from heritability analysis of interregional structural covariance of cortical thickness, but not with structural and functional hierarchical axes. Reliability of all results was confirmed through resampling techniques. Multimodal associations with cytoarchitecture, gene expression and axes of cortical organization indicate that prenatal neurogenesis and postnatal synaptogenesis may be key points of developmental vulnerability of the frontal lobe to FCD. Concordant with a causal role of atypical neuroglial proliferation and growth, our results indicate that FCD-vulnerable cortices display properties indicative of earlier termination of neurogenesis and initiation of cell growth. They also suggest a potential contribution of aberrant postnatal synaptogenesis and circuit development to FCD epileptogenicity.
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Affiliation(s)
- Hyo M Lee
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Seok-Jun Hong
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute, McGill University, Montreal, Canada
- Center for Neuroscience Imaging, Research Institute for Basic Science, Department of Global Biomedical Engineering, SungKyunKwan University, Suwon, KoreaSuwon, Korea
| | - Ravnoor Gill
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Benoit Caldairou
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Irene Wang
- Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Jian-guo Zhang
- Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Francesco Deleo
- Epilepsy Unit, Fondazione IRCCS Istituto Neurologico C. Besta, Milano, Italy
| | - Dewi Schrader
- Department of Pediatrics, British Columbia Children’s Hospital, Vancouver, Canada
| | - Fabrice Bartolomei
- Aix Marseille Univ, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille, 13005, France
| | - Maxime Guye
- Aix Marseille University, CNRS, CRMBM UMR 7339, Marseille, France
| | - Kyoo Ho Cho
- Department of Neurology, Yonsei University College of Medicine, Seoul, Korea
| | - Carmen Barba
- Meyer Children's Hospital IRCCS, Florence, Italy
- University of Florence, 50121 Florence, Italy
| | - Sanjay Sisodiya
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
| | - Graeme Jackson
- The Florey Institute of Neuroscience and Mental Health and The University of Melbourne, Victoria, Australia
| | - R Edward Hogan
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | | | | | | | - Iscia Lopes-Cendes
- Department of Translational Medicine, School of Medical Sciences, University of Campinas (UNICAMP) and the Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas SP, Brazil
| | - Fernando Cendes
- Department of Neurology, School of Medical Sciences, University of Campinas (UNICAMP), and the Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas SP, Brazil
| | - Renzo Guerrini
- Meyer Children's Hospital IRCCS, Florence, Italy
- University of Florence, 50121 Florence, Italy
| | - Boris Bernhardt
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute, McGill University, Montreal, Canada
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8
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Lanjewar AL, Jagetia S, Khan ZM, Eagleson KL, Levitt P. Subclass-specific expression patterns of MET receptor tyrosine kinase during development in medial prefrontal and visual cortices. J Comp Neurol 2023; 531:132-148. [PMID: 36201439 PMCID: PMC9691614 DOI: 10.1002/cne.25418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 08/17/2022] [Accepted: 09/05/2022] [Indexed: 01/12/2023]
Abstract
Met encodes a receptor tyrosine kinase (MET) that is expressed during development and regulates cortical synapse maturation. Conditional deletion of Met in the nervous system during embryonic development leads to deficits in adult contextual fear learning, a medial prefrontal cortex (mPFC)-dependent cognitive task. MET also regulates the timing of critical period plasticity for ocular dominance in primary visual cortex (V1). However, the underlying circuitry responsible remains unknown. Therefore, this study determines the broad expression patterns of MET throughout postnatal development in mPFC and V1 projection neurons (PNs), providing insight into similarities and differences in the neuronal subtypes and temporal patterns of MET expression between cortical areas. Using a transgenic mouse line that expresses green fluorescent protein (GFP) in Met+ neurons, immunofluorescence and confocal microscopy were performed to visualize MET-GFP+ cell bodies and PN subclass-specific protein markers. Analyses reveal that the MET expression is highly enriched in infragranular layers of mPFC, but in supragranular layers of V1. Interestingly, temporal regulation of the percentage of MET+ neurons across development not only differs between cortical regions but also is distinct between lamina within a cortical region. Further, MET is expressed predominantly in the subcerebral PN subclass in mPFC, but the intratelencephalic PN subclass in V1. The data suggest that MET signaling influences the development of distinct circuits in mPFC and V1 that underlie subcerebral and intracortical functional deficits following Met deletion, respectively.
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Affiliation(s)
- Alexandra L. Lanjewar
- Program in Developmental Neuroscience and Neurogenetics, Children's Hospital Los AngelesThe Saban Research InstituteLos AngelesCaliforniaUSA,Department of PediatricsKeck School of Medicine of University of Southern CaliforniaLos AngelesCaliforniaUSA,Neuroscience Graduate ProgramUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Sonum Jagetia
- Program in Developmental Neuroscience and Neurogenetics, Children's Hospital Los AngelesThe Saban Research InstituteLos AngelesCaliforniaUSA,Department of PediatricsKeck School of Medicine of University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Zuhayr M. Khan
- Program in Developmental Neuroscience and Neurogenetics, Children's Hospital Los AngelesThe Saban Research InstituteLos AngelesCaliforniaUSA,Department of PediatricsKeck School of Medicine of University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Kathie L. Eagleson
- Program in Developmental Neuroscience and Neurogenetics, Children's Hospital Los AngelesThe Saban Research InstituteLos AngelesCaliforniaUSA,Department of PediatricsKeck School of Medicine of University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Pat Levitt
- Program in Developmental Neuroscience and Neurogenetics, Children's Hospital Los AngelesThe Saban Research InstituteLos AngelesCaliforniaUSA,Department of PediatricsKeck School of Medicine of University of Southern CaliforniaLos AngelesCaliforniaUSA
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9
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Gostyukhina AA, Samoylova AV, Bolshakov MA, Mochalova VM, Zaitsev KV, Kutenkov OP, Rostov VV. Stimulation of Burn Wound Healing in Rats by Nanosecond Repetitive Pulsed Microwave Radiation. BIOL BULL+ 2022. [DOI: 10.1134/s1062359022050089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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10
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Mapping developmental regionalization and patterns of cortical surface area from 29 post-menstrual weeks to 2 years of age. Proc Natl Acad Sci U S A 2022; 119:e2121748119. [PMID: 35939665 PMCID: PMC9388141 DOI: 10.1073/pnas.2121748119] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Surface area of the human cerebral cortex expands extremely dynamically and regionally heterogeneously from the third trimester of pregnancy to 2 y of age, reflecting the spatial heterogeneity of the underlying microstructural and functional development of the cerebral cortex. However, little is known about the developmental patterns and regionalization of cortical surface area during this critical stage, due to the lack of high-quality imaging data and accurate computational tools for pediatric brain MRI data. To fill this critical knowledge gap, by leveraging 1,037 high-quality MRI scans with the age between 29 post-menstrual weeks and 24 mo from 735 pediatric subjects in two complementary datasets, i.e., the Baby Connectome Project (BCP) and the developing Human Connectome Project (dHCP), and state-of-the-art dedicated image-processing tools, we unprecedentedly parcellate the cerebral cortex into a set of distinct subdivisions purely according to the developmental patterns of the cortical surface. Our discovered developmentally distinct subdivisions correspond well to structurally and functionally meaningful regions and reveal spatially contiguous, hierarchical, and bilaterally symmetric patterns of early cortical surface expansion. We also show that high-order association subdivisions, where cortical folds emerge later during prenatal stages, undergo more dramatic cortical surface expansion during infancy, compared with the central regions, especially the sensorimotor and insula cortices, thus forming a distinct central-pole division in early cortical surface expansion. These results provide an important reference for exploring and understanding dynamic early brain development in health and disease.
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11
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Makowski C, van der Meer D, Dong W, Wang H, Wu Y, Zou J, Liu C, Rosenthal SB, Hagler DJ, Fan CC, Kremen WS, Andreassen OA, Jernigan TL, Dale AM, Zhang K, Visscher PM, Yang J, Chen CH. Discovery of genomic loci of the human cerebral cortex using genetically informed brain atlases. Science 2022; 375:522-528. [PMID: 35113692 DOI: 10.1126/science.abe8457] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
To determine the impact of genetic variants on the brain, we used genetically informed brain atlases in genome-wide association studies of regional cortical surface area and thickness in 39,898 adults and 9136 children. We uncovered 440 genome-wide significant loci in the discovery cohort and 800 from a post hoc combined meta-analysis. Loci in adulthood were largely captured in childhood, showing signatures of negative selection, and were linked to early neurodevelopment and pathways associated with neuropsychiatric risk. Opposing gradations of decreased surface area and increased thickness were associated with common inversion polymorphisms. Inferior frontal regions, encompassing Broca's area, which is important for speech, were enriched for human-specific genomic elements. Thus, a mixed genetic landscape of conserved and human-specific features is concordant with brain hierarchy and morphogenetic gradients.
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Affiliation(s)
- Carolina Makowski
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA
| | - Dennis van der Meer
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
| | - Weixiu Dong
- Department of Bioengineering, University of California, San Diego, CA, USA
| | - Hao Wang
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA
| | - Yan Wu
- Department of Bioengineering, University of California, San Diego, CA, USA
| | - Jingjing Zou
- Division of Biostatistics, Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, CA, USA
| | - Cin Liu
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA
| | - Sara B Rosenthal
- Center for Computational Biology and Bioinformatics, University of California, San Diego, CA, USA
| | - Donald J Hagler
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA
| | - Chun Chieh Fan
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA
| | - William S Kremen
- Department of Psychiatry and Center for Behavior Genetics of Aging, University of California, San Diego, CA, USA
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Terry L Jernigan
- Center for Human Development, University of California, San Diego, CA, USA
| | - Anders M Dale
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA.,Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Kun Zhang
- Department of Bioengineering, University of California, San Diego, CA, USA
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.,School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
| | - Chi-Hua Chen
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA
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12
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Chen YC, Arnatkevičiūtė A, McTavish E, Pang JC, Chopra S, Suo C, Fornito A, Aquino KM. The individuality of shape asymmetries of the human cerebral cortex. eLife 2022; 11:75056. [PMID: 36197720 PMCID: PMC9668337 DOI: 10.7554/elife.75056] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 10/04/2022] [Indexed: 01/05/2023] Open
Abstract
Asymmetries of the cerebral cortex are found across diverse phyla and are particularly pronounced in humans, with important implications for brain function and disease. However, many prior studies have confounded asymmetries due to size with those due to shape. Here, we introduce a novel approach to characterize asymmetries of the whole cortical shape, independent of size, across different spatial frequencies using magnetic resonance imaging data in three independent datasets. We find that cortical shape asymmetry is highly individualized and robust, akin to a cortical fingerprint, and identifies individuals more accurately than size-based descriptors, such as cortical thickness and surface area, or measures of inter-regional functional coupling of brain activity. Individual identifiability is optimal at coarse spatial scales (~37 mm wavelength), and shape asymmetries show scale-specific associations with sex and cognition, but not handedness. While unihemispheric cortical shape shows significant heritability at coarse scales (~65 mm wavelength), shape asymmetries are determined primarily by subject-specific environmental effects. Thus, coarse-scale shape asymmetries are highly personalized, sexually dimorphic, linked to individual differences in cognition, and are primarily driven by stochastic environmental influences.
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Affiliation(s)
- Yu-Chi Chen
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia,Monash Biomedical Imaging, Monash UniversityMelbourneAustralia,Monash Data Futures Institute, Monash UniversityMelbourneAustralia
| | - Aurina Arnatkevičiūtė
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia
| | - Eugene McTavish
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia,Monash Biomedical Imaging, Monash UniversityMelbourneAustralia,Healthy Brain and Mind Research Centre, Faculty of Health Sciences, Australian Catholic UniversityFitzroyAustralia
| | - James C Pang
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia,Monash Biomedical Imaging, Monash UniversityMelbourneAustralia
| | - Sidhant Chopra
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia,Monash Biomedical Imaging, Monash UniversityMelbourneAustralia,Department of Psychology, Yale UniversityNew HavenUnited States
| | - Chao Suo
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia,Monash Biomedical Imaging, Monash UniversityMelbourneAustralia,BrainPark, Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia,Monash Biomedical Imaging, Monash UniversityMelbourneAustralia
| | - Kevin M Aquino
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia,Monash Biomedical Imaging, Monash UniversityMelbourneAustralia,School of Physics, University of SydneySydneyAustralia,Center of Excellence for Integrative Brain Function, University of SydneySydneyAustralia,BrainKey IncSan FranciscoUnited States
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13
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Nano PR, Nguyen CV, Mil J, Bhaduri A. Cortical Cartography: Mapping Arealization Using Single-Cell Omics Technology. Front Neural Circuits 2021; 15:788560. [PMID: 34955761 PMCID: PMC8707733 DOI: 10.3389/fncir.2021.788560] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 11/05/2021] [Indexed: 11/30/2022] Open
Abstract
The cerebral cortex derives its cognitive power from a modular network of specialized areas processing a multitude of information. The assembly and organization of these regions is vital for human behavior and perception, as evidenced by the prevalence of area-specific phenotypes that manifest in neurodevelopmental and psychiatric disorders. Generations of scientists have examined the architecture of the human cortex, but efforts to capture the gene networks which drive arealization have been hampered by the lack of tractable models of human neurodevelopment. Advancements in "omics" technologies, imaging, and computational power have enabled exciting breakthroughs into the molecular and structural characteristics of cortical areas, including transcriptomic, epigenomic, metabolomic, and proteomic profiles of mammalian models. Here we review the single-omics atlases that have shaped our current understanding of cortical areas, and their potential to fuel a new era of multi-omic single-cell endeavors to interrogate both the developing and adult human cortex.
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Affiliation(s)
| | | | | | - Aparna Bhaduri
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, United States
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14
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Chien YL, Chen YC, Chiu YN, Tsai WC, Gau SSF. A translational exploration of the effects of WNT2 variants on altered cortical structures in autism spectrum disorder. J Psychiatry Neurosci 2021; 46:E647-E658. [PMID: 34862305 PMCID: PMC8648347 DOI: 10.1503/jpn.210022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 06/19/2021] [Accepted: 07/28/2021] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND Evidence suggests that cortical anatomy may be aytpical in autism spectrum disorder. The wingless-type MMTV integration site family, member 2 (WNT2), a candidate gene for autism spectrum disorder, may regulate cortical development. However, it is unclear whether WNT2 variants are associated with altered cortical thickness in autism spectrum disorder. METHODS In a sample of 118 people with autism spectrum disorder and 122 typically developing controls, we investigated cortical thickness using FreeSurfer software. We then examined the main effects of the WNT2 variants and the interactions of group × SNP and age × SNP for each hemisphere and brain region that was altered in people with autism spectrum disorder. RESULTS Compared to neurotypical controls, people with autism spectrum disorder showed reduced mean cortical thickness in both hemispheres and 9 cortical regions after false discovery rate correction, including the right cingulate gyrus, the orbital gyrus, the insula, the inferior frontal gyrus (orbital part and triangular part), the lateral occipitotemporal gyrus, the posterior transverse collateral sulcus, the lateral sulcus and the superior temporal sulcus. In the full sample, 2 SNPs of WNT2 (rs6950765 and rs2896218) showed age × SNP interactions for the mean cortical thickness of both hemispheres, the middle-posterior cingulate cortex and the superior temporal cortex. LIMITATIONS We examined the genetic effect for each hemisphere and the 9 regions that were altered in autism spectrum disorder. The age effect we found in this cross-sectional study needs to be examined in longitudinal studies. CONCLUSION Based on neuroimaging and genetic data, our findings suggest that WNT2 variants might be associated with altered cortical thickness in autism spectrum disorder. Whether and how these WNT2 variants might involve cortical thinning requires further investigation. TRIAL REGISTRATION ClinicalTrials.gov no. NCT01582256. PROTOCOL REGISTRATION National Institutes of Health no. NCT00494754.
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Affiliation(s)
| | | | | | | | - Susan Shur-Fen Gau
- From the Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan (Chien, Chen, Chiu, Tsai, Gau); and the Graduate Institute of Clinical Medicine, and Graduate Institute of Brain and Mind Sciences, College of Medicine, National Taiwan University, Taipei, Taiwan (Chen, Gau)
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15
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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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16
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Ho TC, King LS. Mechanisms of neuroplasticity linking early adversity to depression: developmental considerations. Transl Psychiatry 2021; 11:517. [PMID: 34628465 PMCID: PMC8501358 DOI: 10.1038/s41398-021-01639-6] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 09/11/2021] [Accepted: 09/23/2021] [Indexed: 12/17/2022] Open
Abstract
Early exposure to psychosocial adversity is among the most potent predictors of depression. Because depression commonly emerges prior to adulthood, we must consider the fundamental principles of developmental neuroscience when examining how experiences of childhood adversity, including abuse and neglect, can lead to depression. Considering that both the environment and the brain are highly dynamic across the period spanning gestation through adolescence, the purpose of this review is to discuss and integrate stress-based models of depression that center developmental processes. We offer a general framework for understanding how psychosocial adversity in early life disrupts or calibrates the biobehavioral systems implicated in depression. Specifically, we propose that the sources and nature of the environmental input shaping the brain, and the mechanisms of neuroplasticity involved, change across development. We contend that the effects of adversity largely depend on the developmental stage of the organism. First, we summarize leading neurobiological models that focus on the effects of adversity on risk for mental disorders, including depression. In particular, we highlight models of allostatic load, acceleration maturation, dimensions of adversity, and sensitive or critical periods. Second, we expound on and review evidence for the formulation that distinct mechanisms of neuroplasticity are implicated depending on the timing of adverse experiences, and that inherent within certain windows of development are constraints on the sources and nature of these experiences. Finally, we consider other important facets of adverse experiences (e.g., environmental unpredictability, perceptions of one's experiences) before discussing promising research directions for the future of the field.
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Affiliation(s)
- Tiffany C Ho
- Department of Psychiatry and Behavioral Sciences and Weill Institute for Neurosciences, University of California, San Francisco, CA, USA.
| | - Lucy S King
- Department of Psychiatry and Behavioral Sciences, Tulane University School of Medicine, New Orleans, LA, USA
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
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17
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Sydnor VJ, Larsen B, Bassett DS, Alexander-Bloch A, Fair DA, Liston C, Mackey AP, Milham MP, Pines A, Roalf DR, Seidlitz J, Xu T, Raznahan A, Satterthwaite TD. Neurodevelopment of the association cortices: Patterns, mechanisms, and implications for psychopathology. Neuron 2021; 109:2820-2846. [PMID: 34270921 PMCID: PMC8448958 DOI: 10.1016/j.neuron.2021.06.016] [Citation(s) in RCA: 210] [Impact Index Per Article: 70.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 05/24/2021] [Accepted: 06/11/2021] [Indexed: 12/11/2022]
Abstract
The human brain undergoes a prolonged period of cortical development that spans multiple decades. During childhood and adolescence, cortical development progresses from lower-order, primary and unimodal cortices with sensory and motor functions to higher-order, transmodal association cortices subserving executive, socioemotional, and mentalizing functions. The spatiotemporal patterning of cortical maturation thus proceeds in a hierarchical manner, conforming to an evolutionarily rooted, sensorimotor-to-association axis of cortical organization. This developmental program has been characterized by data derived from multimodal human neuroimaging and is linked to the hierarchical unfolding of plasticity-related neurobiological events. Critically, this developmental program serves to enhance feature variation between lower-order and higher-order regions, thus endowing the brain's association cortices with unique functional properties. However, accumulating evidence suggests that protracted plasticity within late-maturing association cortices, which represents a defining feature of the human developmental program, also confers risk for diverse developmental psychopathologies.
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Affiliation(s)
- Valerie J Sydnor
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Danielle S Bassett
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical & Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Santa Fe Institute, Santa Fe, NM 87501, USA
| | - Aaron Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, 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
| | - Conor Liston
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10065, USA
| | - Allyson P Mackey
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022, USA; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY 10962, USA
| | - Adam Pines
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - David R Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jakob Seidlitz
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022, USA
| | - Armin Raznahan
- Section on Developmental Neurogenomics, NIMH Intramural Research Program, NIH, Bethesda, MD 20892, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA.
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18
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Seok JW, Bajaj S, Soltis-Vaughan B, Lerdahl A, Garvey W, Bohn A, Edwards R, Kratochvil CJ, Blair J, Hwang S. Structural atrophy of the right superior frontal gyrus in adolescents with severe irritability. Hum Brain Mapp 2021; 42:4611-4622. [PMID: 34288223 PMCID: PMC8410540 DOI: 10.1002/hbm.25571] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Revised: 05/22/2021] [Accepted: 06/10/2021] [Indexed: 12/27/2022] Open
Abstract
Severe irritability is common in youths with psychiatric disorders and results in significant dysfunction across domains (academic, social, and familial). Prior structural MRI studies in the pediatric population demonstrated that aberrations of cortical thickness (CT) and gray matter volume (GMV) in the fronto‐striatal‐temporal regions which have been associated with irritability. However, the directions of the correlations between structural alteration and irritability in the individual indices were not consistent. Thus, we aim to address this by implementing comprehensive assessments of CT, GMV, and local gyrification index (LGI) simultaneously in youths with severe levels of irritability by voxel‐based morphometry and surface‐based morphometry. One hundred and eight adolescents (46 youths with severe irritability and 62 healthy youths, average age = 14.08 years, standard deviation = 2.36) were scanned with a T1‐weighted MRI sequence. The severity of irritability was measured using the affective reactivity index. In youths with severe irritability, there was decreased CT, GMV, and LGI in the right superior frontal gyrus (SFG) compared to healthy youths, and negative correlations between these indices of the SFG and irritability. Our findings suggest that structural deficits in the SFG, potentially related to its role in inhibitory control, may be critical for the neurobiology of irritability.
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Affiliation(s)
- Ji-Woo Seok
- Department of Psychiatry, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Sahil Bajaj
- Center for Neurobehavioral Research, Boys Town National Research Hospital, Boys Town, Nebraska, USA
| | | | - Arica Lerdahl
- Department of Psychiatry, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - William Garvey
- Department of Psychiatry, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Alexandra Bohn
- Department of Psychiatry, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Ryan Edwards
- Department of Psychiatry, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | | | - James Blair
- Center for Neurobehavioral Research, Boys Town National Research Hospital, Boys Town, Nebraska, USA
| | - Soonjo Hwang
- Department of Psychiatry, University of Nebraska Medical Center, Omaha, Nebraska, USA
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19
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Palmer CE, Zhao W, Loughnan R, Zou J, Fan CC, Thompson WK, Dale AM, Jernigan TL. Distinct Regionalization Patterns of Cortical Morphology are Associated with Cognitive Performance Across Different Domains. Cereb Cortex 2021; 31:3856-3871. [PMID: 33825852 PMCID: PMC8258441 DOI: 10.1093/cercor/bhab054] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 01/26/2021] [Accepted: 02/16/2021] [Indexed: 02/02/2023] Open
Abstract
Cognitive performance in children is predictive of academic and social outcomes; therefore, understanding neurobiological mechanisms underlying individual differences in cognition during development may be important for improving quality of life. The belief that a single, psychological construct underlies many cognitive processes is pervasive throughout society. However, it is unclear if there is a consistent neural substrate underlying many cognitive processes. Here, we show that a distributed configuration of cortical surface area and apparent thickness, when controlling for global imaging measures, is differentially associated with cognitive performance on different types of tasks in a large sample (N = 10 145) of 9-11-year-old children from the Adolescent Brain and Cognitive DevelopmentSM (ABCD) study. The minimal overlap in these regionalization patterns of association has implications for competing theories about developing intellectual functions. Surprisingly, not controlling for sociodemographic factors increased the similarity between these regionalization patterns. This highlights the importance of understanding the shared variance between sociodemographic factors, cognition and brain structure, particularly with a population-based sample such as ABCD.
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Affiliation(s)
- C E Palmer
- Center for Human Development, University of California, San Diego, La Jolla, CA 92161, USA
| | - W Zhao
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA 92093, USA
| | - R Loughnan
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA 92093, USA
| | - J Zou
- Division of Biostatistics, Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA 92161, USA
| | - C C Fan
- Center for Human Development, University of California, San Diego, La Jolla, CA 92161, USA
- Center for Multimodal Imaging and Genetics, University of California, San Diego School of Medicine, La Jolla, CA 92037, USA
| | - W K Thompson
- Division of Biostatistics, Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA 92161, USA
| | - A M Dale
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Radiology, University of California, San Diego School of Medicine, La Jolla, CA 92037, USA
- Department of Neuroscience, University of California, San Diego School of Medicine, La Jolla, CA 92037, USA
- Department of Psychiatry, University of California, San Diego School of Medicine, La Jolla, CA 92037, USA
| | - T L Jernigan
- Center for Human Development, University of California, San Diego, La Jolla, CA 92161, USA
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Radiology, University of California, San Diego School of Medicine, La Jolla, CA 92037, USA
- Department of Psychiatry, University of California, San Diego School of Medicine, La Jolla, CA 92037, USA
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20
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Fjell AM, Grydeland H, Wang Y, Amlien IK, Bartres-Faz D, Brandmaier AM, Düzel S, Elman J, Franz CE, Håberg AK, Kietzmann TC, Kievit RA, Kremen WS, Krogsrud SK, Kühn S, Lindenberger U, Macía D, Mowinckel AM, Nyberg L, Panizzon MS, Solé-Padullés C, Sørensen Ø, Westerhausen R, Walhovd KB. The genetic organization of longitudinal subcortical volumetric change is stable throughout the lifespan. eLife 2021; 10:66466. [PMID: 34180395 PMCID: PMC8260220 DOI: 10.7554/elife.66466] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 06/26/2021] [Indexed: 11/13/2022] Open
Abstract
Development and aging of the cerebral cortex show similar topographic organization and are governed by the same genes. It is unclear whether the same is true for subcortical regions, which follow fundamentally different ontogenetic and phylogenetic principles. We tested the hypothesis that genetically governed neurodevelopmental processes can be traced throughout life by assessing to which degree brain regions that develop together continue to change together through life. Analyzing over 6000 longitudinal MRIs of the brain, we used graph theory to identify five clusters of coordinated development, indexed as patterns of correlated volumetric change in brain structures. The clusters tended to follow placement along the cranial axis in embryonic brain development, suggesting continuity from prenatal stages, and correlated with cognition. Across independent longitudinal datasets, we demonstrated that developmental clusters were conserved through life. Twin-based genetic correlations revealed distinct sets of genes governing change in each cluster. Single-nucleotide polymorphisms-based analyses of 38,127 cross-sectional MRIs showed a similar pattern of genetic volume–volume correlations. In conclusion, coordination of subcortical change adheres to fundamental principles of lifespan continuity and genetic organization.
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Affiliation(s)
- Anders Martin Fjell
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway.,Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Hakon Grydeland
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Yunpeng Wang
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Inge K Amlien
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - David Bartres-Faz
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona, and Institut de Neurociències, Universitat de Barcelona, Barcelona, Spain
| | - Andreas M Brandmaier
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.,Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany
| | - Sandra Düzel
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Jeremy Elman
- Center for Behavioral Genomics Twin Research Laboratory, University of California, San Diego, La Jolla, United States
| | - Carol E Franz
- Center for Behavioral Genomics Twin Research Laboratory, University of California, San Diego, La Jolla, United States
| | - Asta K Håberg
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.,Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Tim C Kietzmann
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom.,Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Rogier Andrew Kievit
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
| | - William S Kremen
- Center for Behavioral Genomics Twin Research Laboratory, University of California, San Diego, La Jolla, United States.,Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, La Jolla, United States
| | - Stine K Krogsrud
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Simone Kühn
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.,Lise Meitner Group for Environmental Neuroscience, Max Planck Institute for Human Development, Berlin, Germany
| | - Ulman Lindenberger
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.,Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany
| | - Didac Macía
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona, and Institut de Neurociències, Universitat de Barcelona, Barcelona, Spain
| | - Athanasia Monika Mowinckel
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Lars Nyberg
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway.,Department of Radiation Sciences, Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden
| | - Matthew S Panizzon
- Center for Behavioral Genomics Twin Research Laboratory, University of California, San Diego, La Jolla, United States.,Department of Psychiatry, University of California, San Diego, La Jolla, United States
| | - Cristina Solé-Padullés
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona, and Institut de Neurociències, Universitat de Barcelona, Barcelona, Spain
| | - Øystein Sørensen
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Rene Westerhausen
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Kristine Beate Walhovd
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway.,Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
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21
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Alvarez I, Finlayson NJ, Ei S, de Haas B, Greenwood JA, Schwarzkopf DS. Heritable functional architecture in human visual cortex. Neuroimage 2021; 239:118286. [PMID: 34153449 PMCID: PMC7611349 DOI: 10.1016/j.neuroimage.2021.118286] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 06/08/2021] [Accepted: 06/17/2021] [Indexed: 11/23/2022] Open
Abstract
We analyzed retinotopic maps from monozygotic and dizygotic twin pairs. Visual field maps in V1-V3 are more similar in monozygotic twins. Heritability is greater in V1 and V3 for polar angle and population receptive field sizes. Eccentricity maps show lesser degree of heritability. Further evidence for link between cortical morphology and topology of retinotopic maps.
How much of the functional organization of our visual system is inherited? Here we tested the heritability of retinotopic maps in human visual cortex using functional magnetic resonance imaging. We demonstrate that retinotopic organization shows a closer correspondence in monozygotic (MZ) compared to dizygotic (DZ) twin pairs, suggesting a partial genetic determination. Using population receptive field (pRF) analysis to examine the preferred spatial location and selectivity of these neuronal populations, we estimate a heritability around 10–20% for polar angle preferences and spatial selectivity, as quantified by pRF size, in extrastriate areas V2 and V3. Our findings are consistent with heritability in both the macroscopic arrangement of visual regions and stimulus tuning properties of visual cortex. This could constitute a neural substrate for variations in a range of perceptual effects, which themselves have been found to be at least partially genetically determined. These findings also add convergent evidence for the hypothesis that functional map topology is linked with cortical morphology.
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Affiliation(s)
- Ivan Alvarez
- Helen Wills Neuroscience Institute, University of California, Berkeley, United States; Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom
| | - Nonie J Finlayson
- Experimental Psychology, University College London, United Kingdom; Ipsos, Brisbane, Queensland, Australia
| | - Shwe Ei
- Experimental Psychology, University College London, United Kingdom; GKT School of Medical Education, Kings College London, United Kingdom
| | - Benjamin de Haas
- Experimental Psychology, University College London, United Kingdom; Department of Psychology, Justus-Liebig University, Giessen, Germany
| | - John A Greenwood
- Experimental Psychology, University College London, United Kingdom
| | - D Samuel Schwarzkopf
- Experimental Psychology, University College London, United Kingdom; School of Optometry & Vision Science, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand.
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22
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Craig MM, Misic B, Pappas I, Adapa RM, Menon DK, Stamatakis EA. Propofol sedation-induced alterations in brain connectivity reflect parvalbumin interneurone distribution in human cerebral cortex. Br J Anaesth 2020; 126:835-844. [PMID: 33386125 DOI: 10.1016/j.bja.2020.11.035] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 10/16/2020] [Accepted: 11/10/2020] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Propofol, a commonly used intravenous anaesthetic, binds to type A gamma aminobutyric acid (GABA) receptors in mammalian brain. Previous work on its anaesthetic action has characterised either the biochemistry underlying propofol binding or the associated changes in brain network dynamics during sedation. Despite these advances, no study has focused on understanding how propofol action at the cellular level results in changes in brain network connectivity. METHODS We used human whole-brain microarray data to generate distribution maps for genes that mark the primary GABAergic cortical interneurone subtypes (somatostatin, parvalbumin [PV], and 5-hydroxytryptamine 3A. Next, 25 healthy participants underwent propofol-induced sedation during resting state functional MRI scanning. We used partial least squares analysis to identify the brain regions in which connectivity patterns were most impacted by propofol sedation. We then correlated these multimodal cortical patterns to determine if a specific interneurone subtype was disproportionately expressed in brain regions in which connectivity patterns were altered during sedation. RESULTS Brain networks that were significantly altered by propofol sedation had a high density of PV-expressing GABAergic interneurones. Brain networks that anticorrelated during normal wakefulness, namely the default mode network and attentional and frontoparietal control networks, increased in correlation during sedation. CONCLUSIONS PV-expressing interneurones are highly expressed in brain regions with altered connectivity profiles during propofol-induced sedation. This study also demonstrates the utility of leveraging multiple datasets to address multiscale neurobiological problems.
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Affiliation(s)
- Michael M Craig
- Division of Anaesthesia, Department of Medicine, School of Clinical Medicine, University of Cambridge, Cambridge, UK; Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
| | - Bratislav Misic
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Ioannis Pappas
- Division of Anaesthesia, Department of Medicine, School of Clinical Medicine, University of Cambridge, Cambridge, UK; Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Ram M Adapa
- Division of Anaesthesia, Department of Medicine, School of Clinical Medicine, University of Cambridge, Cambridge, UK; Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - David K Menon
- Division of Anaesthesia, Department of Medicine, School of Clinical Medicine, University of Cambridge, Cambridge, UK; Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, Department of Medicine, School of Clinical Medicine, University of Cambridge, Cambridge, UK; Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Cambridge, UK
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23
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Fjell AM, Chen CH, Sederevicius D, Sneve MH, Grydeland H, Krogsrud SK, Amlien I, Ferschmann L, Ness H, Folvik L, Beck D, Mowinckel AM, Tamnes CK, Westerhausen R, Håberg AK, Dale AM, Walhovd KB. Continuity and Discontinuity in Human Cortical Development and Change From Embryonic Stages to Old Age. Cereb Cortex 2020; 29:3879-3890. [PMID: 30357317 DOI: 10.1093/cercor/bhy266] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Revised: 08/28/2018] [Indexed: 11/12/2022] Open
Abstract
The human cerebral cortex is highly regionalized, and this feature emerges from morphometric gradients in the cerebral vesicles during embryonic development. We tested if this principle of regionalization could be traced from the embryonic development to the human life span. Data-driven fuzzy clustering was used to identify regions of coordinated longitudinal development of cortical surface area (SA) and thickness (CT) (n = 301, 4-12 years). The principal divide for the developmental SA clusters extended from the inferior-posterior to the superior-anterior cortex, corresponding to the major embryonic morphometric anterior-posterior (AP) gradient. Embryonic factors showing a clear AP gradient were identified, and we found significant differences in gene expression of these factors between the anterior and posterior clusters. Further, each identified developmental SA and CT clusters showed distinguishable life span trajectories in a larger longitudinal dataset (4-88 years, 1633 observations), and the SA and CT clusters showed differential relationships to cognitive functions. This means that regions that developed together in childhood also changed together throughout life, demonstrating continuity in regionalization of cortical changes. The AP divide in SA development also characterized genetic patterning obtained in an adult twin sample. In conclusion, the development of cortical regionalization is a continuous process from the embryonic stage throughout life.
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Affiliation(s)
- Anders M Fjell
- Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway.,Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Chi-Hua Chen
- Department of Radiology, University of California, San Diego, La Jolla, CA, USA
| | - Donatas Sederevicius
- Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
| | - Markus H Sneve
- Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
| | - Håkon Grydeland
- Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
| | - Stine K Krogsrud
- Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
| | - Inge Amlien
- Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
| | - Lia Ferschmann
- Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
| | - Hedda Ness
- Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
| | - Line Folvik
- Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
| | - Dani Beck
- Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
| | - Athanasia M Mowinckel
- Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
| | - Christian K Tamnes
- Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
| | - René Westerhausen
- Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
| | - Asta K Håberg
- Department of Medical Imaging, St. Olav's Hospital, Trondheim, Norway.,Department of Neuroscience, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Anders M Dale
- Department of Radiology, University of California, San Diego, La Jolla, CA, USA.,Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Kristine B Walhovd
- Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway.,Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
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24
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Di Nardo AA, Joliot A, Prochiantz A. Homeoprotein transduction in neurodevelopment and physiopathology. SCIENCE ADVANCES 2020; 6:6/44/eabc6374. [PMID: 33115744 PMCID: PMC7608782 DOI: 10.1126/sciadv.abc6374] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 09/11/2020] [Indexed: 05/28/2023]
Abstract
Homeoproteins were originally identified for embryonic cell-autonomous transcription activity, but they also have non-cell-autonomous activity owing to transfer between cells. This Review discusses transfer mechanisms and focuses on some established functions, such as neurodevelopmental regulation of axon guidance, and postnatal critical periods of brain plasticity that affect sensory processing and cognition. Homeoproteins are present across all eukaryotes, and intercellular transfer occurs in plants and animals. Proposed functions have evolutionary relevance, such as morphogenetic activity and sexual exchange during the mating of unicellular eukaryotes, while others have physiopathological relevance, such as regulation of mood and cognition by influencing brain compartmentalization, connectivity, and plasticity. There are more than 250 known homeoproteins with conserved transfer domains, suggesting that this is a common mode of signal transduction but with many undiscovered functions.
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Affiliation(s)
- Ariel A Di Nardo
- Centre for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS UMR 7241, INSERM U1050, PSL University, Labex MemoLife, 75005 Paris, France.
| | - Alain Joliot
- Centre for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS UMR 7241, INSERM U1050, PSL University, Labex MemoLife, 75005 Paris, France
| | - Alain Prochiantz
- Centre for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS UMR 7241, INSERM U1050, PSL University, Labex MemoLife, 75005 Paris, France.
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25
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Hofer E, Roshchupkin GV, Adams HHH, Knol MJ, Lin H, Li S, Zare H, Ahmad S, Armstrong NJ, Satizabal CL, Bernard M, Bis JC, Gillespie NA, Luciano M, Mishra A, Scholz M, Teumer A, Xia R, Jian X, Mosley TH, Saba Y, Pirpamer L, Seiler S, Becker JT, Carmichael O, Rotter JI, Psaty BM, Lopez OL, Amin N, van der Lee SJ, Yang Q, Himali JJ, Maillard P, Beiser AS, DeCarli C, Karama S, Lewis L, Harris M, Bastin ME, Deary IJ, Veronica Witte A, Beyer F, Loeffler M, Mather KA, Schofield PR, Thalamuthu A, Kwok JB, Wright MJ, Ames D, Trollor J, Jiang J, Brodaty H, Wen W, Vernooij MW, Hofman A, Uitterlinden AG, Niessen WJ, Wittfeld K, Bülow R, Völker U, Pausova Z, Bruce Pike G, Maingault S, Crivello F, Tzourio C, Amouyel P, Mazoyer B, Neale MC, Franz CE, Lyons MJ, Panizzon MS, Andreassen OA, Dale AM, Logue M, Grasby KL, Jahanshad N, Painter JN, Colodro-Conde L, Bralten J, Hibar DP, Lind PA, Pizzagalli F, Stein JL, Thompson PM, Medland SE, Sachdev PS, Kremen WS, Wardlaw JM, Villringer A, van Duijn CM, Grabe HJ, Longstreth WT, Fornage M, Paus T, Debette S, Ikram MA, Schmidt H, Schmidt R, Seshadri S. Genetic correlations and genome-wide associations of cortical structure in general population samples of 22,824 adults. Nat Commun 2020; 11:4796. [PMID: 32963231 PMCID: PMC7508833 DOI: 10.1038/s41467-020-18367-y] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Accepted: 08/20/2020] [Indexed: 12/22/2022] Open
Abstract
Cortical thickness, surface area and volumes vary with age and cognitive function, and in neurological and psychiatric diseases. Here we report heritability, genetic correlations and genome-wide associations of these cortical measures across the whole cortex, and in 34 anatomically predefined regions. Our discovery sample comprises 22,824 individuals from 20 cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank. We identify genetic heterogeneity between cortical measures and brain regions, and 160 genome-wide significant associations pointing to wnt/β-catenin, TGF-β and sonic hedgehog pathways. There is enrichment for genes involved in anthropometric traits, hindbrain development, vascular and neurodegenerative disease and psychiatric conditions. These data are a rich resource for studies of the biological mechanisms behind cortical development and aging. Cortex morphology varies with age, cognitive function, and in neurological and psychiatric diseases. Here the authors report 160 genome-wide significant associations with thickness, surface area and volume of the total cortex and 34 cortical regions from a GWAS meta-analysis in 22,824 adults.
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Affiliation(s)
- Edith Hofer
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria.,Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
| | - Gennady V Roshchupkin
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.,Department of Medical Informatics, Erasmus MC, Rotterdam, The Netherlands.,Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Hieab H H Adams
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.,Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Maria J Knol
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Honghuang Lin
- Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Shuo Li
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Habil Zare
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, USA.,Department of Cell Systems & Anatomy, The University of Texas Health Science Center, San Antonio, TX, USA
| | - Shahzad Ahmad
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | | | - Claudia L Satizabal
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, USA.,Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | | | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, Epidemiology and Health Services, University of Washington, Seattle, WA, USA
| | - Nathan A Gillespie
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, VA, USA.,QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Michelle Luciano
- Centre for Cognitive Epidemiology and Cognitive Ageing, University of Edinburgh, Edinburgh, UK.,Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Aniket Mishra
- University of Bordeaux, Bordeaux Population Health Research Center, INSERM UMR 1219, Bordeaux, France
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany.,LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Rui Xia
- Institute of Molecular Medicine and Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Xueqiu Jian
- Institute of Molecular Medicine and Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Thomas H Mosley
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Yasaman Saba
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging, Medical University of Graz, Graz, Austria
| | - Lukas Pirpamer
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria
| | - Stephan Seiler
- Imaging of Dementia and Aging (IDeA) Laboratory, Department of Neurology, University of California-Davis, Davis, CA, USA.,Department of Neurology and Center for Neuroscience, University of California at Davis, Sacramento, CA, USA
| | - James T Becker
- Departments of Psychiatry, Neurology, and Psychology, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute and Pediatrics at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, Epidemiology and Health Services, University of Washington, Seattle, WA, USA
| | - Oscar L Lopez
- Departments of Psychiatry, Neurology, and Psychology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Najaf Amin
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | | | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Jayandra J Himali
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Pauline Maillard
- Imaging of Dementia and Aging (IDeA) Laboratory, Department of Neurology, University of California-Davis, Davis, CA, USA.,Department of Neurology and Center for Neuroscience, University of California at Davis, Sacramento, CA, USA
| | - Alexa S Beiser
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.,Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Charles DeCarli
- Imaging of Dementia and Aging (IDeA) Laboratory, Department of Neurology, University of California-Davis, Davis, CA, USA.,Department of Neurology and Center for Neuroscience, University of California at Davis, Sacramento, CA, USA
| | - Sherif Karama
- McGill University, Montreal Neurological Institute, Montreal, QC, Canada
| | - Lindsay Lewis
- McGill University, Montreal Neurological Institute, Montreal, QC, Canada
| | - Mat Harris
- Centre for Cognitive Epidemiology and Cognitive Ageing, University of Edinburgh, Edinburgh, UK.,Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.,Brain Research Imaging Centre, University of Edinburgh, Edinburgh, UK.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Department of Neuroimaging Sciences, The University of Edinburgh, Edinburgh, UK
| | - Mark E Bastin
- Centre for Cognitive Epidemiology and Cognitive Ageing, University of Edinburgh, Edinburgh, UK.,Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.,Brain Research Imaging Centre, University of Edinburgh, Edinburgh, UK.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Department of Neuroimaging Sciences, The University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Centre for Cognitive Epidemiology and Cognitive Ageing, University of Edinburgh, Edinburgh, UK.,Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - A Veronica Witte
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Faculty of Medicine, CRC 1052 Obesity Mechanisms, University of Leipzig, Leipzig, Germany
| | - Frauke Beyer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Faculty of Medicine, CRC 1052 Obesity Mechanisms, University of Leipzig, Leipzig, Germany
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany.,LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Karen A Mather
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia.,Neuroscience Research Australia, Sydney, Australia
| | - Peter R Schofield
- Neuroscience Research Australia, Sydney, Australia.,School of Medical Sciences, University of New South Wales, Sydney, Australia
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia
| | - John B Kwok
- School of Medical Sciences, University of New South Wales, Sydney, Australia.,Brain and Mind Centre - The University of Sydney, Camperdown, NSW, Australia
| | - Margaret J Wright
- Queensland Brain Institute, The University of Queensland, St Lucia, QLD, Australia.,Centre for Advanced Imaging, The University of Queensland, St Lucia, QLD, Australia
| | - David Ames
- National Ageing Research Institute, Royal Melbourne Hospital, Parkvill, VIC, Australia.,Academic Unit for Psychiatry of Old Age, University of Melbourne, St George's Hospital, Kew, VIC, Australia
| | - Julian Trollor
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia.,Department of Developmental Disability Neuropsychiatry, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - Jiyang Jiang
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Henry Brodaty
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia.,Dementia Centre for Research Collaboration, University of New South Wales, Sydney, NSW, Australia
| | - Wei Wen
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Meike W Vernooij
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.,Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Albert Hofman
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | - Wiro J Niessen
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.,Imaging Physics, Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands
| | - Katharina Wittfeld
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Greifswald, Germany.,Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Robin Bülow
- Institute for Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Uwe Völker
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Zdenka Pausova
- Hospital for Sick Children, Toronto, ON, Canada.,Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto, ON, Canada
| | - G Bruce Pike
- Departments of Radiology and Clinial Neurosciences, University of Calgary, Calgary, AB, Canada
| | - Sophie Maingault
- Institut des Maladies Neurodégénratives UMR5293, CEA, CNRS, University of Bordeaux, Bordeaux, France
| | - Fabrice Crivello
- Institut des Maladies Neurodégénratives UMR5293, CEA, CNRS, University of Bordeaux, Bordeaux, France
| | - Christophe Tzourio
- University of Bordeaux, Bordeaux Population Health Research Center, INSERM UMR 1219, Bordeaux, France.,Pole de santé publique, Centre Hospitalier Universitaire de Bordeaux, Bordeaux, France
| | - Philippe Amouyel
- Centre Hospitalier Universitaire de Bordeaux, France; Inserm U1167, Lille, France.,Department of Epidemiology and Public Health, Pasteur Institute of Lille, Lille, France.,Department of Public Health, Lille University Hospital, Lille, France
| | - Bernard Mazoyer
- Institut des Maladies Neurodégénratives UMR5293, CEA, CNRS, University of Bordeaux, Bordeaux, France
| | - Michael C Neale
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Carol E Franz
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Michael J Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Matthew S Panizzon
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Ole A Andreassen
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Anders M Dale
- Departments of Radiology and Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Mark Logue
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.,National Center for PTSD at Boston VA Healthcare System, Boston, MA, USA.,Department of Psychiatry and Department of Medicine-Biomedical Genetics Section, Boston University School of Medicine, Boston, MA, USA
| | - Katrina L Grasby
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Jodie N Painter
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Lucía Colodro-Conde
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Janita Bralten
- Department of Human Genetics, Radboud university medical center, Nijmegen, The Netherlands.,Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Derrek P Hibar
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.,Neuroscience Biomarkers, Janssen Research and Development, LLC, San Diego, CA, USA
| | - Penelope A Lind
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Fabrizio Pizzagalli
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Jason L Stein
- Department of Genetics & UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Sarah E Medland
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | | | - Perminder S Sachdev
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia.,Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia
| | - William S Kremen
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Joanna M Wardlaw
- Centre for Cognitive Epidemiology and Cognitive Ageing, University of Edinburgh, Edinburgh, UK.,Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.,Brain Research Imaging Centre, University of Edinburgh, Edinburgh, UK.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Department of Neuroimaging Sciences, The University of Edinburgh, Edinburgh, UK
| | - Arno Villringer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Day Clinic for Cognitive Neurology, University Hospital Leipzig, Leipzig, Germany
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands.,Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Hans J Grabe
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Greifswald, Germany.,Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - William T Longstreth
- Departments of Neurology and Epidemiology, University of Washington, Seattle, WA, USA
| | - Myriam Fornage
- Institute of Molecular Medicine and Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Tomas Paus
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada.,Departments of Psychology and Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Stephanie Debette
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA.,University of Bordeaux, Bordeaux Population Health Research Center, INSERM UMR 1219, Bordeaux, France.,CHU de Bordeaux, Department of Neurology, F-33000, Bordeaux, France
| | - M Arfan Ikram
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.,Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands.,Department of Neurology, Erasmus MC, Rotterdam, The Netherlands
| | - Helena Schmidt
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging, Medical University of Graz, Graz, Austria
| | - Reinhold Schmidt
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria.
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, USA. .,Department of Neurology, Boston University School of Medicine, Boston, MA, USA.
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26
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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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27
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Alexander-Bloch AF, Mathias SR, Fox PT, Olvera RL, Göring HHH, Duggirala R, Curran JE, Blangero J, Glahn DC. Human Cortical Thickness Organized into Genetically-determined Communities across Spatial Resolutions. Cereb Cortex 2020; 29:106-118. [PMID: 29190330 DOI: 10.1093/cercor/bhx309] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Indexed: 12/13/2022] Open
Abstract
The cerebral cortex may be organized into anatomical genetic modules, communities of brain regions with shared genetic influences via pleiotropy. Such modules could represent novel phenotypes amenable to large-scale gene discovery. This modular structure was investigated with network analysis of in vivo MRI of extended pedigrees, revealing a "multiscale" structure where smaller and larger modules exist simultaneously and in partially overlapping fashion across spatial scales, in contrast to prior work suggesting a specific number of cortical thickness modules. Inter-regional genetic correlations, gene co-expression patterns and computational models indicate that two simple organizational principles account for a large proportion of the apparent complexity in the network of genetic correlations. First, regions are strongly genetically correlated with their homologs in the opposite cerebral hemisphere. Second, regions are strongly genetically correlated with nearby regions in the same hemisphere, with an initial steep decrease in genetic correlation with anatomical distance, followed by a more gradual decline. Understanding underlying organizational principles of genetic influence is a critical step towards a mechanistic model of how specific genes influence brain anatomy and mediate neuropsychiatric risk.
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Affiliation(s)
| | - Samuel R Mathias
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.,Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA
| | - Peter T Fox
- South Texas Diabetes and Obesity Institute, University of Texas Health Science Center at San Antonio, TX, USA.,University of Texas of the Rio Grande Valley, Brownsville, TX, USA
| | - Rene L Olvera
- South Texas Diabetes and Obesity Institute, University of Texas Health Science Center at San Antonio, TX, USA.,University of Texas of the Rio Grande Valley, Brownsville, TX, USA
| | - Harold H H Göring
- South Texas Diabetes and Obesity Institute, University of Texas Health Science Center at San Antonio, TX, USA.,University of Texas of the Rio Grande Valley, Brownsville, TX, USA
| | - Ravi Duggirala
- South Texas Diabetes and Obesity Institute, University of Texas Health Science Center at San Antonio, TX, USA.,University of Texas of the Rio Grande Valley, Brownsville, TX, USA
| | - Joanne E Curran
- South Texas Diabetes and Obesity Institute, University of Texas Health Science Center at San Antonio, TX, USA.,University of Texas of the Rio Grande Valley, Brownsville, TX, USA
| | - John Blangero
- South Texas Diabetes and Obesity Institute, University of Texas Health Science Center at San Antonio, TX, USA.,University of Texas of the Rio Grande Valley, Brownsville, TX, USA
| | - David C Glahn
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.,Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA
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28
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Genetic influence is linked to cortical morphology in category-selective areas of visual cortex. Nat Commun 2020; 11:709. [PMID: 32024844 PMCID: PMC7002610 DOI: 10.1038/s41467-020-14610-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Accepted: 01/22/2020] [Indexed: 01/24/2023] Open
Abstract
Human visual cortex contains discrete areas that respond selectively to specific object categories such as faces, bodies, and places. A long-standing question is whether these areas are shaped by genetic or environmental factors. To address this question, here we analyzed functional MRI data from an unprecedented number (n = 424) of monozygotic (MZ) and dizygotic (DZ) twins. Category-selective maps were more identical in MZ than DZ twins. Within each category-selective area, distinct subregions showed significant genetic influence. Structural MRI analysis revealed that the ‘genetic voxels’ were predominantly located in regions with higher cortical curvature (gyral crowns in face areas and sulcal fundi in place areas). Moreover, we found that cortex was thicker and more myelinated in genetic voxels of face areas, while it was thinner and less myelinated in genetic voxels of place areas. This double dissociation suggests a differential development of face and place areas in cerebral cortex. It remains unclear whether the functional organization of the visual cortex is shaped by genetic or environmental factors. Using fMRI in twins (n = 424), these authors show that activation patterns in category-selective areas are heritable, and that the genetic effects in these areas are linked to structural properties of cortical tissue.
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29
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Abstract
The Vietnam Era Twin Study of Aging (VETSA) is a longitudinal behavioral genetic study with a primary focus on cognitive and brain aging in men, particularly early identification of risk for mild cognitive impairment (MCI) and Alzheimer's disease (AD). It comprises a subset of over 1600 twins from the Vietnam Era Twin Registry. Twins live all over the USA. Assessments began when participants were in their 50s. Follow-ups were conducted every 5-6 years, and wave 3 has been completed as of this writing. The age range of participants is narrow (about 10 years). An extensive neurocognitive test battery has added precision in assessing differences in middle-aged adults, and predicting progression to MCI. Young adult cognitive test data (at an average age of 20 years) provide a means of disentangling aging effects from longstanding differences. Genome wide genotyping and plasma assays of AD biomarkers from waves 1 and 3 were conducted in wave 3. These features make the VETSA ideal for studying the heterogeneity of within-individual trajectories from midlife to old age, and for early detection of risk factors for cognitive decline.
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30
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Variable functional connectivity architecture of the preterm human brain: Impact of developmental cortical expansion and maturation. Proc Natl Acad Sci U S A 2019; 117:1201-1206. [PMID: 31888985 DOI: 10.1073/pnas.1907892117] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
Functional connectivity (FC) is known to be individually unique and to reflect cognitive variability. Although FC can serve as a valuable correlate and potential predictor of (patho-) physiological nervous function in high-risk constellations, such as preterm birth, templates for individualized FC analysis are lacking, and knowledge about the capacity of the premature brain to develop FC variability is limited. In a cohort of prospectively recruited, preterm-born infants undergoing magnetic resonance imaging close to term-equivalent age, we show that the overall pattern could be reliably detected with a broad range of interindividual FC variability in regions of higher-order cognitive functions (e.g., association cortices) and less interindividual variability in unimodal regions (e.g., visual and motor cortices). However, when comparing the preterm and adult brains, some brain regions showed a marked shift in variability toward adulthood. This shift toward greater variability was strongest in cognitive networks like the attention and frontoparietal networks and could be partially predicted by developmental cortical expansion. Furthermore, FC variability was reflected by brain tissue characteristics indicating cortical maturation. Brain regions with high functional variability (e.g., the inferior frontal gyrus and temporoparietal junction) displayed lower cortical maturation at birth compared with somatosensory cortices. In conclusion, the overall pattern of interindividual variability in FC is already present preterm; however, some brain regions show increased variability toward adulthood, identifying characteristic patterns, such as in cognitive networks. These changes are related to postnatal cortical expansion and maturation, allowing for environmental and developmental factors to translate into marked individual differences in FC.
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31
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Ye C, Albert M, Brown T, Bilgel M, Hsu J, Ma T, Caffo B, Miller MI, Mori S, Oishi K. Extended multimodal whole-brain anatomical covariance analysis: detection of disrupted correlation networks related to amyloid deposition. Heliyon 2019; 5:e02074. [PMID: 31372540 PMCID: PMC6656959 DOI: 10.1016/j.heliyon.2019.e02074] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 04/22/2019] [Accepted: 07/08/2019] [Indexed: 01/27/2023] Open
Abstract
Background An anatomical covariance analysis (ACA) enables to elucidate inter-regional connections on a group basis, but little is known about the connections among white matter structures or among gray and white matter structures. Effect of including multiple magnetic resonance imaging (MRI) modalities into ACA framework in detecting white-to-white or gray-to-white connections is yet to be investigated. New method Proposed extended anatomical covariance analysis (eACA), analyzes correlations among gray and white matter structures (multi-structural) in various types of imaging modalities (T1-weighted images, T2 maps obtained from dual-echo sequences, and diffusion tensor images (DTI)). To demonstrate the capability to detect a disruption of the correlation network affected by pathology, we applied the eACA to two groups of cognitively-normal elderly individuals, one with (PiB+) and one without (PiB-) amyloid deposition in their brains. Results The volume of each anatomical structure was symmetric and functionally related structures formed a cluster. The pseudo-T2 value was highly homogeneous across the entire cortex in the PiB- group, while a number of physiological correlations were altered in the PiB + group. The DTI demonstrated unique correlation network among structures within the same phylogenetic portions of the brain that were altered in the PiB + group. Comparison with Existing Method The proposed eACA expands the concept of existing ACA to the connections among the white matter structures. The extension to other image modalities expands the way in which connectivity may be detected. Conclusion The eACA has potential to evaluate alterations of the anatomical network related to pathological processes.
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Affiliation(s)
- Chenfei Ye
- Department of Electronics and Information, Harbin Institute of Technology at Shenzhen, Shenzhen, Guangdong Province, China.,The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.,Peng Cheng Laboratory, Shenzhen, Guangdong, China
| | - Marilyn Albert
- Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.,The Johns Hopkins Alzheimer's Disease Research Center, Baltimore, MD, USA
| | - Timothy Brown
- Center for Imaging Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, Intramural Research Program, National Institute on Aging, Baltimore, MD, USA
| | - Johnny Hsu
- Peng Cheng Laboratory, Shenzhen, Guangdong, China
| | - Ting Ma
- Department of Electronics and Information, Harbin Institute of Technology at Shenzhen, Shenzhen, Guangdong Province, China.,Peng Cheng Laboratory, Shenzhen, Guangdong, China
| | - Brian Caffo
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA
| | - Michael I Miller
- Center for Imaging Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Susumu Mori
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Kenichi Oishi
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
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32
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Kennedy JT, Astafiev SV, Golosheykin S, Korucuoglu O, Anokhin AP. Shared genetic influences on adolescent body mass index and brain structure: A voxel-based morphometry study in twins. Neuroimage 2019; 199:261-272. [PMID: 31163268 DOI: 10.1016/j.neuroimage.2019.05.053] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2019] [Revised: 05/17/2019] [Accepted: 05/19/2019] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Previous research has demonstrated significant relationships between obesity and brain structure. Both phenotypes are heritable, but it is not known whether they are influenced by common genetic factors. We investigated the genetic etiology of the relationship between individual variability in brain morphology and BMIz using structural MRI in adolescent twins. METHOD The sample (n = 258) consisted of 54 monozygotic and 75 dizygotic twin pairs (mean(SD) age = 13.61(0.505), BMIz = 0.608(1.013). Brain structure (volume and density of gray and white matter) was assessed using VBM. Significant voxelwise heritability of brain structure was established using the Accelerated Permutation inference for ACE models (APACE) program, with structural heritability varying from 15 to 97%, depending on region. Bivariate heritability analyses were carried out comparing additive genetic and unique environment models with and without shared genetics on BMIz and the voxels showing significant heritability in the APACE analyses. RESULTS BMIz was positively related to gray matter volume in the brainstem and thalamus and negatively related to gray matter volume in the bilateral uncus and medial orbitofrontal cortex, gray matter density in the cerebellum, prefrontal lobe, temporal lobe, and limbic system, and white matter density in the brainstem. Bivariate heritability analyses showed that BMIz and brain structure share ∼1/3 of their genes and that ∼95% of the phenotypic correlation between BMIz and brain structure is due to shared additive genetic influences. These regions included areas related to decision-making, motivation, liking vs. wanting, taste, interoception, reward processing/learning, caloric evaluation, and inhibition. CONCLUSION These results suggested genetic factors are responsible for the relationship between BMIz and heritable BMIz related brain structure in areas related to eating behavior.
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Affiliation(s)
- James T Kennedy
- Department of Psychiatry, Washington University School of Medicine, United States.
| | - Serguei V Astafiev
- Department of Psychiatry, Washington University School of Medicine, United States
| | - Semyon Golosheykin
- Department of Psychiatry, Washington University School of Medicine, United States
| | - Ozlem Korucuoglu
- Department of Psychiatry, Washington University School of Medicine, United States
| | - Andrey P Anokhin
- Department of Psychiatry, Washington University School of Medicine, United States
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Winkler AM, Greve DN, Bjuland KJ, Nichols TE, Sabuncu MR, Håberg AK, Skranes J, Rimol LM. Joint Analysis of Cortical Area and Thickness as a Replacement for the Analysis of the Volume of the Cerebral Cortex. Cereb Cortex 2019; 28:738-749. [PMID: 29190325 PMCID: PMC5972607 DOI: 10.1093/cercor/bhx308] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2017] [Accepted: 10/27/2017] [Indexed: 12/21/2022] Open
Abstract
Cortical surface area is an increasingly used brain morphology metric that is ontogenetically and phylogenetically distinct from cortical thickness and offers a separate index of neurodevelopment and disease. However, the various existing methods for assessment of cortical surface area from magnetic resonance images have never been systematically compared. We show that the surface area method implemented in FreeSurfer corresponds closely to the exact, but computationally more demanding, mass-conservative (pycnophylactic) method, provided that images are smoothed. Thus, the data produced by this method can be interpreted as estimates of cortical surface area, as opposed to areal expansion. In addition, focusing on the joint analysis of thickness and area, we compare an improved, analytic method for measuring cortical volume to a permutation-based nonparametric combination (NPC) method. We use the methods to analyze area, thickness and volume in young adults born preterm with very low birth weight, and show that NPC analysis is a more sensitive option for studying joint effects on area and thickness, giving equal weight to variation in both of these 2 morphological features.
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Affiliation(s)
- Anderson M Winkler
- Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK.,Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA.,Big Data Analytics Group, Hospital Israelita Albert Einstein, São Paulo, SP 05652-900, Brazil
| | - Douglas N Greve
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital/ Harvard Medical School, Charlestown, MA 02129, USA
| | - Knut J Bjuland
- Department of Laboratory Medicine, Children's and Women's Health, Norwegian University of Science and Technology, Trondheim 7030, Norway
| | - Thomas E Nichols
- Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK.,Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Mert R Sabuncu
- School of Electrical and Computer Engineering, and Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Asta K Håberg
- Department of Neuroscience, Norwegian University of Science and Technology, Trondheim 7030, Norway.,Department of Radiology, St. Olav's Hospital, Trondheim University Hospital, Trondheim 7030, Norway
| | - Jon Skranes
- Department of Laboratory Medicine, Children's and Women's Health, Norwegian University of Science and Technology, Trondheim 7030, Norway.,Department of Pediatrics, Sørlandet Hospital, 4838 Arendal, Norway
| | - Lars M Rimol
- Department of Laboratory Medicine, Children's and Women's Health, Norwegian University of Science and Technology, Trondheim 7030, Norway.,Norwegian Advisory Unit for Functional MRI, Department of Radiology, St. Olav's University Hospital, Trondheim 7006, Norway
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34
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Paul S, Mukherjee S, Bhattacharyya S. Network organization of co-opetitive genetic influences on morphologies of the human cerebral cortex. J Neural Eng 2019; 16:026028. [PMID: 30654334 DOI: 10.1088/1741-2552/aaff85] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE The brain can be represented as a network, where anatomical regions are nodes and relations between regions are edges. Within a network, the co-existence of co-operative and competitive relationships between different nodes is called co-opetition. Inter-regional genetic influences on morphological phenotypes (thickness, surface area) of the cerebral cortex display such co-opetitive relationships. However, whether these co-operative and competitive genetic influences are organized similarly has remained elusive. How the collective organization of the co-operative and competitive genetic influences is related to the inter-individual variations of cortical morphological phenotypes has also remained unexplored. APPROACH We constructed inter-regional genetic influence networks underlying the morphologies (thickness, surface area) of the human cerebral cortex combining the T1 weighted MRI of genetically confirmed 593 siblings and twin-study design. Graph theory was used to characterize the genetic influence networks and the collective organizations of genetic influences were characterized using the theory of structural balance. Principal component (PC) analysis was used to estimate the principal modes of morphological phenotype variations. MAIN RESULTS The inter-regional co-operative genetic influences are assortative, while competitive influences are disassortative. Co-operative genetic influences are more cohesive and less diverse than the competitive influences. The collective organization of co-opetitive genetic influences partially explains the fifth principal modes of inter-individual variation of cortical morphological phenotypes. Other principal modes were not significantly associated with collective genetic influences. SIGNIFICANCE Our study furnishes fundamental insight regarding the organization of co-opetitive genetic influences underlying the morphologies of the human cerebral cortex. In future studies, investigation of the alterations of co-opetitive genetic network properties in brain disorders may furnish disorder-specific insight that may be associated with the disease state or lead to vulnerability to those conditions.
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Affiliation(s)
- Subhadip Paul
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom
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35
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Fornito A, Arnatkevičiūtė A, Fulcher BD. Bridging the Gap between Connectome and Transcriptome. Trends Cogn Sci 2019; 23:34-50. [DOI: 10.1016/j.tics.2018.10.005] [Citation(s) in RCA: 156] [Impact Index Per Article: 31.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Revised: 10/10/2018] [Accepted: 10/23/2018] [Indexed: 11/24/2022]
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36
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Liu S, Li A, Zhu M, Li J, Liu B. Genetic influences on cortical myelination in the human brain. GENES BRAIN AND BEHAVIOR 2018; 18:e12537. [PMID: 30394688 DOI: 10.1111/gbb.12537] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Revised: 10/30/2018] [Accepted: 10/31/2018] [Indexed: 12/12/2022]
Abstract
Cortical myelination, which is essential for interneuronal communication and neurodevelopment, has been reported to be under genetic control. However, the degree to which genes contribute to the variability of myelination, the pattern of genetic control, and how genes influence the organization of myelination are largely unknown. To answer these questions, the present study calculated heritability estimates for myelination of the cortical regions using the high quality structural magnetic resonance imaging (MRI) scans from the Human Connectome Project pedigree cohort (n = 873, 383/490 M/F, 22-36 years of age). Then, we used transcriptional profiles to evaluate the contribution of myelination-related genes (data from the Allen Human Brain Atlas) to explain interregional variations in cortical myelination. Our results showed that all the cortical areas were modestly to moderately influenced by genetic factors (h2 = 29%-66%, all Ps < 0.05 after Bonferroni correction). The genetic control of cortical myelination showed bilateral symmetry and an anterior-to-posterior gradation. A bivariate model indicated that the regions are strongly genetically correlated with their homologs in the opposite cerebral hemisphere. A cross-modal analysis did not find a correlation between cortical myelination and the expression levels of myelination-related genes. This could have been due to the small number of samples with expression data in each cortical region. Overall, our findings suggest that cortical myelination is shaped by genetic factors and may be useful to bridge the underlying genetic variants and the cognitive functioning and related neuropsychiatric disorders.
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Affiliation(s)
- Shu Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Ang Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Meifang Zhu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Jin Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Bing Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China.,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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37
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Scheppele M, Evans JL, Brown TT. Patterns of structural lateralization in cortical language areas of older adolescents. Laterality 2018; 24:450-481. [DOI: 10.1080/1357650x.2018.1543312] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Meredith Scheppele
- Department of Communication Sciences and Disorders, University of Texas-Dallas, Richardson, TX, USA
| | - Julia L. Evans
- Department of Communication Sciences and Disorders, University of Texas-Dallas, Richardson, TX, USA
| | - Timothy T. Brown
- Department of Neurosciences and Center for Multimodal Imaging and Genetics, School of Medicine, University of California, San Diego, La Jolla, CA, USA
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38
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Abbate C. Topographic Markers Drive Proteinopathies to Selection of Target Brain Areas at Onset in Neurodegenerative Dementias. Front Aging Neurosci 2018; 10:308. [PMID: 30344489 PMCID: PMC6182099 DOI: 10.3389/fnagi.2018.00308] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Accepted: 09/14/2018] [Indexed: 01/10/2023] Open
Affiliation(s)
- Carlo Abbate
- Geriatric Unit, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
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39
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Jha SC, Xia K, Schmitt JE, Ahn M, Girault JB, Murphy VA, Li G, Wang L, Shen D, Zou F, Zhu H, Styner M, Knickmeyer RC, Gilmore JH. Genetic influences on neonatal cortical thickness and surface area. Hum Brain Mapp 2018; 39:4998-5013. [PMID: 30144223 DOI: 10.1002/hbm.24340] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Revised: 07/18/2018] [Accepted: 07/20/2018] [Indexed: 01/07/2023] Open
Abstract
Genetic and environmental influences on cortical thickness (CT) and surface area (SA) are thought to vary in a complex and dynamic way across the lifespan. It has been established that CT and SA are genetically distinct in older children, adolescents, and adults, and that heritability varies across cortical regions. Very little, however, is known about how genetic and environmental factors influence infant CT and SA. Using structural MRI, we performed the first assessment of genetic and environmental influences on normal variation of SA and CT in 360 twin neonates. We observed strong and significant additive genetic influences on total SA (a2 = 0.78) and small and nonsignificant genetic influences on average CT (a2 = 0.29). Moreover, we found significant genetic overlap (genetic correlation = 0.65) between these global cortical measures. Regionally, there were minimal genetic influences across the cortex for both CT and SA measures and no distinct patterns of genetic regionalization. Overall, outcomes from this study suggest a dynamic relationship between CT and SA during the neonatal period and provide novel insights into how genetic influences shape cortical structure during early development.
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Affiliation(s)
- Shaili C Jha
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina
| | - Kai Xia
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina
| | - James Eric Schmitt
- Brain Behavior Laboratory, Departments of Radiology and Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Mihye Ahn
- Department of Mathematics and Statistics, University of Nevada, Reno, Nevada
| | - Jessica B Girault
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina
| | - Veronica A Murphy
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina.,Curriculum in Neuroscience, University of North Carolina, Chapel Hill, North Carolina
| | - Gang Li
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, North Carolina
| | - Li Wang
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, North Carolina
| | - Dinggang Shen
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, North Carolina
| | - Fei Zou
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina.,Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Martin Styner
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina.,Department of Computer Science, University of North Carolina, Chapel Hill, North Carolina
| | - Rebecca C Knickmeyer
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina
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40
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Reardon PK, Seidlitz J, Vandekar S, Liu S, Patel R, Park MTM, Alexander-Bloch A, Clasen LS, Blumenthal JD, Lalonde FM, Giedd JN, Gur RC, Gur RE, Lerch JP, Chakravarty MM, Satterthwaite TD, Shinohara RT, Raznahan A. Normative brain size variation and brain shape diversity in humans. Science 2018; 360:1222-1227. [PMID: 29853553 PMCID: PMC7485526 DOI: 10.1126/science.aar2578] [Citation(s) in RCA: 117] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2017] [Accepted: 04/09/2018] [Indexed: 01/09/2023]
Abstract
Brain size variation over primate evolution and human development is associated with shifts in the proportions of different brain regions. Individual brain size can vary almost twofold among typically developing humans, but the consequences of this for brain organization remain poorly understood. Using in vivo neuroimaging data from more than 3000 individuals, we find that larger human brains show greater areal expansion in distributed frontoparietal cortical networks and related subcortical regions than in limbic, sensory, and motor systems. This areal redistribution recapitulates cortical remodeling across evolution, manifests by early childhood in humans, and is linked to multiple markers of heightened metabolic cost and neuronal connectivity. Thus, human brain shape is systematically coupled to naturally occurring variations in brain size through a scaling map that integrates spatiotemporally diverse aspects of neurobiology.
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Affiliation(s)
- P K Reardon
- Developmental Neurogenomics Unit, National Institute of Mental Health, NIH, Bethesda, MD, USA
- Department of Physiology, Anatomy and Genetics, Oxford University, UK
- Vagelos College of Physicians and Surgeons, New York, NY, USA
| | - Jakob Seidlitz
- Developmental Neurogenomics Unit, National Institute of Mental Health, NIH, Bethesda, MD, USA
- Department of Psychiatry, Cambridge University, Cambridge, UK
| | - Simon Vandekar
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Siyuan Liu
- Developmental Neurogenomics Unit, National Institute of Mental Health, NIH, Bethesda, MD, USA
| | - Raihaan Patel
- Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, Quebec, Canada
- Department of Biological and Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - Min Tae M Park
- Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, Quebec, Canada
- Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | | | - Liv S Clasen
- Developmental Neurogenomics Unit, National Institute of Mental Health, NIH, Bethesda, MD, USA
| | - Jonathan D Blumenthal
- Developmental Neurogenomics Unit, National Institute of Mental Health, NIH, Bethesda, MD, USA
| | - Francois M Lalonde
- Developmental Neurogenomics Unit, National Institute of Mental Health, NIH, Bethesda, MD, USA
| | - Jay N Giedd
- Department of Psychiatry, University of California-San Diego, La Jolla, CA, USA
| | - Ruben C Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jason P Lerch
- Mouse Imaging Center, Hospital for Sick Children, Toronto, ON, Canada
| | - M Mallar Chakravarty
- Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, Quebec, Canada
- Department of Biological and Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | | | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Armin Raznahan
- Developmental Neurogenomics Unit, National Institute of Mental Health, NIH, Bethesda, MD, USA.
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41
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Romero-Garcia R, Whitaker KJ, Váša F, Seidlitz J, Shinn M, Fonagy P, Dolan RJ, Jones PB, Goodyer IM, Bullmore ET, Vértes PE. Structural covariance networks are coupled to expression of genes enriched in supragranular layers of the human cortex. Neuroimage 2018; 171:256-267. [PMID: 29274746 PMCID: PMC5883331 DOI: 10.1016/j.neuroimage.2017.12.060] [Citation(s) in RCA: 122] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2017] [Revised: 12/01/2017] [Accepted: 12/19/2017] [Indexed: 12/18/2022] Open
Abstract
Complex network topology is characteristic of many biological systems, including anatomical and functional brain networks (connectomes). Here, we first constructed a structural covariance network from MRI measures of cortical thickness on 296 healthy volunteers, aged 14-24 years. Next, we designed a new algorithm for matching sample locations from the Allen Brain Atlas to the nodes of the SCN. Subsequently we used this to define, transcriptomic brain networks by estimating gene co-expression between pairs of cortical regions. Finally, we explored the hypothesis that transcriptional networks and structural MRI connectomes are coupled. A transcriptional brain network (TBN) and a structural covariance network (SCN) were correlated across connection weights and showed qualitatively similar complex topological properties: assortativity, small-worldness, modularity, and a rich-club. In both networks, the weight of an edge was inversely related to the anatomical (Euclidean) distance between regions. There were differences between networks in degree and distance distributions: the transcriptional network had a less fat-tailed degree distribution and a less positively skewed distance distribution than the SCN. However, cortical areas connected to each other within modules of the SCN had significantly higher levels of whole genome co-expression than expected by chance. Nodes connected in the SCN had especially high levels of expression and co-expression of a human supragranular enriched (HSE) gene set that has been specifically located to supragranular layers of human cerebral cortex and is known to be important for large-scale, long-distance cortico-cortical connectivity. This coupling of brain transcriptome and connectome topologies was largely but not entirely accounted for by the common constraint of physical distance on both networks.
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Affiliation(s)
| | - Kirstie J Whitaker
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK; The Alan Turing Institute for Data Science, British Library, 96 Euston Road, London, NW1 2DB, United Kingdom
| | - František Váša
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - Jakob Seidlitz
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - Maxwell Shinn
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - Peter Fonagy
- Research Department of Clinical, Educational and Health Psychology, University College London, London, WC1E 6BT, UK
| | - Raymond J Dolan
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, WC1N 3BG, UK; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, WC1B 5EH, UK
| | - Peter B Jones
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK; Cambridgeshire and Peterborough NHS Foundation Trust, Huntingdon, PE29 3RJ, UK
| | - Ian M Goodyer
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK; Cambridgeshire and Peterborough NHS Foundation Trust, Huntingdon, PE29 3RJ, UK
| | - Edward T Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK; Cambridgeshire and Peterborough NHS Foundation Trust, Huntingdon, PE29 3RJ, UK; ImmunoPsychiatry, Immuno-Inflammation Therapeutic Area Unit, GlaxoSmithKline R&D, Stevenage, SG1 2NY, UK
| | - Petra E Vértes
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
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Abstract
Imaging genetics is a research methodology studying the effect of genetic variation on brain structure, function, behavior, and risk for psychopathology. Since the early 2000s, imaging genetics has been increasingly used in the research of schizophrenia (SZ). SZ is a severe mental disorder with no precise knowledge of its underlying neurobiology, however, new genetic and neurobiological data generate a climate for new avenues. The accumulating data of genome wide association studies (GWAS) continuously decode SZ risk genes. Global neuroimaging consortia produce collections of brain phenotypes from tens of thousands of people. In this context, imaging genetics will be strategically important both for the validation and discovery of SZ related findings. Thus, the study of GWAS supported risk variants as candidate genes to validate by neuroimaging is one trend. The study of epigenetic differences in relation to variations of brain phenotypes and the study of large scale multivariate analysis of genome wide and brain wide associations are other trends. While these studies hold a big potential for understanding the neurobiology of SZ, the problem of reproducibility appears as a major challenge, which requires standardizations in study designs and compensations of methodological limitations such as sensitivity and specificity. On the other hand, advancements of neuroimaging, optical and electron microscopy along with the use of genetically encoded fluorescent probes and robust statistical approaches will not only catalyze integrative methodologies but also will help better design the imaging genetics studies. In this invited paper, I will discuss the current perspective of imaging genetics and emerging opportunities of SZ research.
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Affiliation(s)
- Ayla Arslan
- Faculty of Engineering and Natural Sciences, Department of Genetics and Bioengineering, International University of Sarajevo, Sarajevo, Bosnia and Herzegovina; Faculty of Engineering and Natural Sciences, Department of Molecular Biology and Genetics, Uskudar University, Istanbul, Turkey.
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43
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Mufford MS, Stein DJ, Dalvie S, Groenewold NA, Thompson PM, Jahanshad N. Neuroimaging genomics in psychiatry-a translational approach. Genome Med 2017; 9:102. [PMID: 29179742 PMCID: PMC5704437 DOI: 10.1186/s13073-017-0496-z] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Neuroimaging genomics is a relatively new field focused on integrating genomic and imaging data in order to investigate the mechanisms underlying brain phenotypes and neuropsychiatric disorders. While early work in neuroimaging genomics focused on mapping the associations of candidate gene variants with neuroimaging measures in small cohorts, the lack of reproducible results inspired better-powered and unbiased large-scale approaches. Notably, genome-wide association studies (GWAS) of brain imaging in thousands of individuals around the world have led to a range of promising findings. Extensions of such approaches are now addressing epigenetics, gene–gene epistasis, and gene–environment interactions, not only in brain structure, but also in brain function. Complementary developments in systems biology might facilitate the translation of findings from basic neuroscience and neuroimaging genomics to clinical practice. Here, we review recent approaches in neuroimaging genomics—we highlight the latest discoveries, discuss advantages and limitations of current approaches, and consider directions by which the field can move forward to shed light on brain disorders.
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Affiliation(s)
- Mary S Mufford
- UCT/MRC Human Genetics Research Unit, Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa, 7925
| | - Dan J Stein
- MRC Unit on Risk and Resilience, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa, 7925.,Department of Psychiatry and Mental Health, Groote Schuur Hospital, Cape Town, South Africa, 7925
| | - Shareefa Dalvie
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa, 7925
| | - Nynke A Groenewold
- Department of Psychiatry and Mental Health, Groote Schuur Hospital, Cape Town, South Africa, 7925
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the University of Southern California, Los Angeles, CA, 90292, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the University of Southern California, Los Angeles, CA, 90292, USA.
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44
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Curley LB, Newman E, Thompson WK, Brown TT, Hagler DJ, Akshoomoff N, Reuter C, Dale AM, Jernigan TL. Cortical morphology of the pars opercularis and its relationship to motor-inhibitory performance in a longitudinal, developing cohort. Brain Struct Funct 2017; 223:211-220. [PMID: 28756486 PMCID: PMC5772141 DOI: 10.1007/s00429-017-1480-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2017] [Accepted: 07/18/2017] [Indexed: 10/29/2022]
Abstract
This study investigates the relationship between variability in cortical surface area and thickness of the pars opercularis of the inferior frontal gyrus and motor-inhibitory performance on a stop-signal task in a longitudinal, typically developing cohort of children and adolescents. Linear mixed-effects models were used to investigate the hypotheses that (1) cortical thinning and (2) a relatively larger cortical surface area of the bilateral pars opercularis of the inferior frontal gyrus would predict better performance on the stop-signal task in a cohort of 110 children and adolescents 4-13 years of age, with one to four observations (totaling 232 observations). Cortical thickness of the bilateral opercular region was not related to inhibitory performance. However, independent of age, gender, and total cortical surface area, relatively larger cortical surface area of the bilateral opercular region of the inferior frontal gyrus was associated with better motor-inhibitory performance. Follow-up analyses showed a significant effect of surface area of the right pars opercularis, but no evidence for an effect of area of left pars opercularis, on motor-inhibitory performance. These findings are consistent with the previous work in adults showing that cortical morphology of the pars opercularis is related to inhibitory functioning. It also expands upon this literature by showing that, in contrast to earlier work highlighting the importance of cortical thickness of this region in adults, relative cortical surface area of the pars opercularis may be related to developing motor-inhibitory functions during childhood and adolescence. Relationships between cortical phenotypes and individual differences in behavioral measures may vary across the lifespan.
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Affiliation(s)
- Lauren B Curley
- Department of Cognitive Science, University of California, San Diego, La Jolla, USA
| | - Erik Newman
- Center for Human Development, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0115, USA
| | - Wesley K Thompson
- Center for Human Development, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0115, USA.,Department of Psychiatry, University of California, San Diego School of Medicine, La Jolla, USA
| | - Timothy T Brown
- Center for Human Development, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0115, USA.,Center for Multimodal Imaging and Genetics, University of California, San Diego School of Medicine, La Jolla, USA.,Department of Neurosciences, University of California, San Diego School of Medicine, La Jolla, USA
| | - Donald J Hagler
- Center for Multimodal Imaging and Genetics, University of California, San Diego School of Medicine, La Jolla, USA.,Department of Radiology, University of California, San Diego School of Medicine, La Jolla, USA
| | - Natacha Akshoomoff
- Center for Human Development, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0115, USA.,Department of Psychiatry, University of California, San Diego School of Medicine, La Jolla, USA
| | - Chase Reuter
- Department of Psychiatry, University of California, San Diego School of Medicine, La Jolla, USA
| | - Anders M Dale
- Department of Cognitive Science, University of California, San Diego, La Jolla, USA.,Department of Psychiatry, University of California, San Diego School of Medicine, La Jolla, USA.,Center for Multimodal Imaging and Genetics, University of California, San Diego School of Medicine, La Jolla, USA.,Department of Neurosciences, University of California, San Diego School of Medicine, La Jolla, USA.,Department of Radiology, University of California, San Diego School of Medicine, La Jolla, USA
| | - Terry L Jernigan
- Department of Cognitive Science, University of California, San Diego, La Jolla, USA. .,Center for Human Development, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0115, USA. .,Department of Psychiatry, University of California, San Diego School of Medicine, La Jolla, USA. .,Department of Radiology, University of California, San Diego School of Medicine, La Jolla, USA.
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45
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Geng X, Li G, Lu Z, Gao W, Wang L, Shen D, Zhu H, Gilmore JH. Structural and Maturational Covariance in Early Childhood Brain Development. Cereb Cortex 2017; 27:1795-1807. [PMID: 26874184 DOI: 10.1093/cercor/bhw022] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Brain structural covariance networks (SCNs) composed of regions with correlated variation are altered in neuropsychiatric disease and change with age. Little is known about the development of SCNs in early childhood, a period of rapid cortical growth. We investigated the development of structural and maturational covariance networks, including default, dorsal attention, primary visual and sensorimotor networks in a longitudinal population of 118 children after birth to 2 years old and compared them with intrinsic functional connectivity networks. We found that structural covariance of all networks exhibit strong correlations mostly limited to their seed regions. By Age 2, default and dorsal attention structural networks are much less distributed compared with their functional maps. The maturational covariance maps, however, revealed significant couplings in rates of change between distributed regions, which partially recapitulate their functional networks. The structural and maturational covariance of the primary visual and sensorimotor networks shows similar patterns to the corresponding functional networks. Results indicate that functional networks are in place prior to structural networks, that correlated structural patterns in adult may arise in part from coordinated cortical maturation, and that regional co-activation in functional networks may guide and refine the maturation of SCNs over childhood development.
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Affiliation(s)
- Xiujuan Geng
- Department of Psychiatry.,State Key Lab of Brain and Cognitive Sciences, University of Hong Kong, Hong Kong, Hong Kong.,Laboratory of Neuropsychology and Laboratory of Social Cognitive and Affective Neuroscience, University of Hong Kong
| | - Gang Li
- IDEA Lab, Department of Radiology and BRIC
| | - Zhaohua Lu
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Wei Gao
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, NC 27514, USA
| | - Li Wang
- IDEA Lab, Department of Radiology and BRIC
| | - Dinggang Shen
- IDEA Lab, Department of Radiology and BRIC.,Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA
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46
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Kumar VJ, van Oort E, Scheffler K, Beckmann CF, Grodd W. Functional anatomy of the human thalamus at rest. Neuroimage 2017; 147:678-691. [DOI: 10.1016/j.neuroimage.2016.12.071] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2016] [Revised: 12/21/2016] [Accepted: 12/22/2016] [Indexed: 12/25/2022] Open
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47
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Roshchupkin GV, Gutman BA, Vernooij MW, Jahanshad N, Martin NG, Hofman A, McMahon KL, van der Lee SJ, van Duijn CM, de Zubicaray GI, Uitterlinden AG, Wright MJ, Niessen WJ, Thompson PM, Ikram MA, Adams HHH. Heritability of the shape of subcortical brain structures in the general population. Nat Commun 2016; 7:13738. [PMID: 27976715 PMCID: PMC5172387 DOI: 10.1038/ncomms13738] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2016] [Accepted: 10/28/2016] [Indexed: 11/24/2022] Open
Abstract
The volumes of subcortical brain structures are highly heritable, but genetic underpinnings of their shape remain relatively obscure. Here we determine the relative contribution of genetic factors to individual variation in the shape of seven bilateral subcortical structures: the nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen and thalamus. In 3,686 unrelated individuals aged between 45 and 98 years, brain magnetic resonance imaging and genotyping was performed. The maximal heritability of shape varies from 32.7 to 53.3% across the subcortical structures. Genetic contributions to shape extend beyond influences on intracranial volume and the gross volume of the respective structure. The regional variance in heritability was related to the reliability of the measurements, but could not be accounted for by technical factors only. These findings could be replicated in an independent sample of 1,040 twins. Differences in genetic contributions within a single region reveal the value of refined brain maps to appreciate the genetic complexity of brain structures.
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Affiliation(s)
- Gennady V. Roshchupkin
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam 3015 CE, The Netherlands
- Department of Medical Informatics, Erasmus MC, Rotterdam 3015 CE, The Netherlands
| | - Boris A. Gutman
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del ReyLos Angeles, California 90292, USA
| | - Meike W. Vernooij
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam 3015 CE, The Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam 3015 CE, The Netherlands
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del ReyLos Angeles, California 90292, USA
| | - Nicholas G. Martin
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4006, Australia
| | - Albert Hofman
- Department of Epidemiology, Erasmus MC, Rotterdam 3015 CE, The Netherlands
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA
| | - Katie L. McMahon
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Queensland 4072, Australia
| | | | - Cornelia M. van Duijn
- Department of Epidemiology, Erasmus MC, Rotterdam 3015 CE, The Netherlands
- Translational Epidemiology, Faculty Science, Leiden University, Leiden, 2333 CC, The Netherlands
| | - Greig I. de Zubicaray
- Faculty of Health, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland 4059, Australia
| | | | - Margaret J. Wright
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Queensland 4072, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Wiro J. Niessen
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam 3015 CE, The Netherlands
- Department of Medical Informatics, Erasmus MC, Rotterdam 3015 CE, The Netherlands
- Faculty of Applied Sciences, Delft University of Technology, Delft 2628 CJ, The Netherlands
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del ReyLos Angeles, California 90292, USA
| | - M. Arfan Ikram
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam 3015 CE, The Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam 3015 CE, The Netherlands
- Department of Neurology, Erasmus MC, Rotterdam 3015 CE, The Netherlands
| | - Hieab H. H. Adams
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam 3015 CE, The Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam 3015 CE, The Netherlands
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48
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Eicher JD, Montgomery AM, Akshoomoff N, Amaral DG, Bloss CS, Libiger O, Schork NJ, Darst BF, Casey BJ, Chang L, Ernst T, Frazier J, Kaufmann WE, Keating B, Kenet T, Kennedy D, Mostofsky S, Murray SS, Sowell ER, Bartsch H, Kuperman JM, Brown TT, Hagler DJ, Dale AM, Jernigan TL, Gruen JR. Dyslexia and language impairment associated genetic markers influence cortical thickness and white matter in typically developing children. Brain Imaging Behav 2016; 10:272-82. [PMID: 25953057 PMCID: PMC4639472 DOI: 10.1007/s11682-015-9392-6] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Dyslexia and language impairment (LI) are complex traits with substantial genetic components. We recently completed an association scan of the DYX2 locus, where we observed associations of markers in DCDC2, KIAA0319, ACOT13, and FAM65B with reading-, language-, and IQ-related traits. Additionally, the effects of reading-associated DYX3 markers were recently characterized using structural neuroimaging techniques. Here, we assessed the neuroimaging implications of associated DYX2 and DYX3 markers, using cortical volume, cortical thickness, and fractional anisotropy. To accomplish this, we examined eight DYX2 and three DYX3 markers in 332 subjects in the Pediatrics Imaging Neurocognition Genetics study. Imaging-genetic associations were examined by multiple linear regression, testing for influence of genotype on neuroimaging. Markers in DYX2 genes KIAA0319 and FAM65B were associated with cortical thickness in the left orbitofrontal region and global fractional anisotropy, respectively. KIAA0319 and ACOT13 were suggestively associated with overall fractional anisotropy and left pars opercularis cortical thickness, respectively. DYX3 markers showed suggestive associations with cortical thickness and volume measures in temporal regions. Notably, we did not replicate association of DYX3 markers with hippocampal measures. In summary, we performed a neuroimaging follow-up of reading-, language-, and IQ-associated DYX2 and DYX3 markers. DYX2 associations with cortical thickness may reflect variations in their role in neuronal migration. Furthermore, our findings complement gene expression and imaging studies implicating DYX3 markers in temporal regions. These studies offer insight into where and how DYX2 and DYX3 risk variants may influence neuroimaging traits. Future studies should further connect the pathways to risk variants associated with neuroimaging/neurocognitive outcomes.
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Affiliation(s)
- John D Eicher
- Department of Genetics, Yale University, New Haven, CT, 06520, USA
| | - Angela M Montgomery
- Department of Pediatrics, Yale University School of Medicine, New Haven, CT, 06520, USA
| | - Natacha Akshoomoff
- Center for Human Development, University of California, La Jolla, San Diego, CA, 92037, USA
- Department of Psychiatry, University of California, La Jolla, San Diego, CA, 92037, USA
| | - David G Amaral
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA, 95817, USA
| | - Cinnamon S Bloss
- Scripps Genomic Medicine, Scripps Health, Scripps Translational Science Institute, La Jolla, CA, 92037, USA
| | - Ondrej Libiger
- Scripps Genomic Medicine, Scripps Health, Scripps Translational Science Institute, La Jolla, CA, 92037, USA
| | - Nicholas J Schork
- Scripps Genomic Medicine, Scripps Health, Scripps Translational Science Institute, La Jolla, CA, 92037, USA
| | - Burcu F Darst
- Scripps Genomic Medicine, Scripps Health, Scripps Translational Science Institute, La Jolla, CA, 92037, USA
| | - B J Casey
- Sackler Institute for Developmental Psychobiology, Weil Cornell Medical College, New York, NY, 10065, USA
| | - Linda Chang
- Department of Medicine, Queen's Medical Center, University of Hawaii, Honolulu, HI, 96813, USA
| | - Thomas Ernst
- Department of Medicine, Queen's Medical Center, University of Hawaii, Honolulu, HI, 96813, USA
| | - Jean Frazier
- Department of Psychiatry, University of Massachusetts Medical School, Boston, MA, 01655, USA
| | - Walter E Kaufmann
- Kennedy Krieger Institute, 707 N. Broadway, Baltimore, MD, 21205, USA
- Department of Neurology, Harvard Medical School, Children's Hospital Boston, Boston, MA, 02115, USA
| | - Brian Keating
- Department of Medicine, Queen's Medical Center, University of Hawaii, Honolulu, HI, 96813, USA
| | - Tal Kenet
- Department of Neurology and Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
| | - David Kennedy
- Department of Psychiatry, University of Massachusetts Medical School, Boston, MA, 01655, USA
| | - Stewart Mostofsky
- Kennedy Krieger Institute, 707 N. Broadway, Baltimore, MD, 21205, USA
| | - Sarah S Murray
- Scripps Genomic Medicine, Scripps Health, Scripps Translational Science Institute, La Jolla, CA, 92037, USA
| | - Elizabeth R Sowell
- Department of Pediatrics, University of Southern California, Los Angeles, CA, 90027, USA
- Developmental Cognitive Neuroimaging Laboratory Children's Hospital, Los Angeles, CA, 90027, USA
| | - Hauke Bartsch
- Multimodal Imaging Laboratory, University of California, La Jolla, San Diego, CA, 92037, USA
| | - Joshua M Kuperman
- Multimodal Imaging Laboratory, University of California, La Jolla, San Diego, CA, 92037, USA
- Department of Neurosciences, University of California, La Jolla, San Diego, CA, 92037, USA
| | - Timothy T Brown
- Center for Human Development, University of California, La Jolla, San Diego, CA, 92037, USA
- Multimodal Imaging Laboratory, University of California, La Jolla, San Diego, CA, 92037, USA
- Department of Neurosciences, University of California, La Jolla, San Diego, CA, 92037, USA
| | - Donald J Hagler
- Multimodal Imaging Laboratory, University of California, La Jolla, San Diego, CA, 92037, USA
- Radiology University of California, La Jolla, San Diego, CA, 92037, USA
| | - Anders M Dale
- Department of Psychiatry, University of California, La Jolla, San Diego, CA, 92037, USA
- Multimodal Imaging Laboratory, University of California, La Jolla, San Diego, CA, 92037, USA
- Department of Neurosciences, University of California, La Jolla, San Diego, CA, 92037, USA
- Radiology University of California, La Jolla, San Diego, CA, 92037, USA
- Cognitive Science University of California, La Jolla, San Diego, CA, 92037, USA
| | - Terry L Jernigan
- Center for Human Development, University of California, La Jolla, San Diego, CA, 92037, USA
- Department of Psychiatry, University of California, La Jolla, San Diego, CA, 92037, USA
- Radiology University of California, La Jolla, San Diego, CA, 92037, USA
- Cognitive Science University of California, La Jolla, San Diego, CA, 92037, USA
| | - Jeffrey R Gruen
- Department of Genetics, Yale University, New Haven, CT, 06520, USA.
- Department of Pediatrics, Yale University School of Medicine, New Haven, CT, 06520, USA.
- Department of Investigative, School of Medicine, Medicine Yale University, New Haven, CT, 06520, USA.
- Department of Pediatrics, Genetics, and Investigative Medicine, Yale Child Health Research Center, 464 Congress Avenue, New Haven, CT, 06520-8081, USA.
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49
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Distinct Genetic Influences on Cortical and Subcortical Brain Structures. Sci Rep 2016; 6:32760. [PMID: 27595976 PMCID: PMC5011703 DOI: 10.1038/srep32760] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2016] [Accepted: 08/09/2016] [Indexed: 12/13/2022] Open
Abstract
This study examined the heritability of brain grey matter structures in a subsample of older adult twins (93 MZ and 68 DZ twin pairs; mean age 70 years) from the Older Australian Twins Study. The heritability estimates of subcortical regions ranged from 0.41 (amygdala) to 0.73 (hippocampus), and of cortical regions, from 0.55 (parietal lobe) to 0.78 (frontal lobe). Corresponding structures in the two hemispheres were influenced by the same genetic factors and high genetic correlations were observed between the two hemispheric regions. There were three genetically correlated clusters, comprising (i) the cortical lobes (frontal, temporal, parietal and occipital lobes); (ii) the basal ganglia (caudate, putamen and pallidum) with weak genetic correlations with cortical lobes, and (iii) the amygdala, hippocampus, thalamus and nucleus accumbens grouped together, which genetically correlated with both basal ganglia and cortical lobes, albeit relatively weakly. Our study demonstrates a complex but patterned and clustered genetic architecture of the human brain, with divergent genetic determinants of cortical and subcortical structures, in particular the basal ganglia.
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50
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Conservation of Distinct Genetically-Mediated Human Cortical Pattern. PLoS Genet 2016; 12:e1006143. [PMID: 27459196 PMCID: PMC4961377 DOI: 10.1371/journal.pgen.1006143] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2016] [Accepted: 06/03/2016] [Indexed: 12/13/2022] Open
Abstract
The many subcomponents of the human cortex are known to follow an anatomical pattern and functional relationship that appears to be highly conserved between individuals. This suggests that this pattern and the relationship among cortical regions are important for cortical function and likely shaped by genetic factors, although the degree to which genetic factors contribute to this pattern is unknown. We assessed the genetic relationships among 12 cortical surface areas using brain images and genotype information on 2,364 unrelated individuals, brain images on 466 twin pairs, and transcriptome data on 6 postmortem brains in order to determine whether a consistent and biologically meaningful pattern could be identified from these very different data sets. We find that the patterns revealed by each data set are highly consistent (p<10−3), and are biologically meaningful on several fronts. For example, close genetic relationships are seen in cortical regions within the same lobes and, the frontal lobe, a region showing great evolutionary expansion and functional complexity, has the most distant genetic relationship with other lobes. The frontal lobe also exhibits the most distinct expression pattern relative to the other regions, implicating a number of genes with known functions mediating immune and related processes. Our analyses reflect one of the first attempts to provide an assessment of the biological consistency of a genetic phenomenon involving the brain that leverages very different types of data, and therefore is not just statistical replication which purposefully use very similar data sets. Although functional and anatomical connections among cortical regions have been intensively explored, genetically-mediated relationships between cortical regions have not been pursued to the same degree. Identifying genetic factors that mediate these relationships among different brain subcomponents can provide insight into how the human brain is organized and functions. We have assessed the genetic relationships among cortical regions using an integrated approach that considers twin data, genotype information among a large set of unrelated individuals, and gene expression measurements from postmortem neural tissues. We looked for evidence that subsets of cortical brain regions are under common or unique genetic control. We found that the patterns of genetic relationships are highly consistent across three independent data sets and multiple lines of evidence, suggesting that the patterning of cortical surface area is strongly mediated by genetic factors and, furthermore, likely reflects underlying anatomical and possibly functional relationships among cortical brain regions.
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