1
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Segal A, Smith RE, Chopra S, Oldham S, Parkes L, Aquino K, Kia SM, Wolfers T, Franke B, Hoogman M, Beckmann CF, Westlye LT, Andreassen OA, Zalesky A, Harrison BJ, Davey CG, Soriano-Mas C, Cardoner N, Tiego J, Yücel M, Braganza L, Suo C, Berk M, Cotton S, Bellgrove MA, Marquand AF, Fornito A. Multiscale heterogeneity of white matter morphometry in psychiatric disorders. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2025:S2451-9022(25)00127-2. [PMID: 40204235 DOI: 10.1016/j.bpsc.2025.03.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2024] [Revised: 02/12/2025] [Accepted: 03/26/2025] [Indexed: 04/11/2025]
Abstract
BACKGROUND Inter-individual variability in the neurobiological and clinical characteristics of mental illnesses are often overlooked by classical group-mean case-control studies. Studies using normative modelling to infer person-specific deviations of grey matter volume have indicated that group means are not representative of most individuals. The extent to which this variability is present in white matter morphometry, which is integral to brain function, remains unclear. METHODS We applied Warped Bayesian Linear Regression normative models to T1-weighted magnetic resonance imaging data and mapped inter-individual variability in person-specific white matter volume deviations in 1,294 cases (58% male) diagnosed with one of six disorders (attention-deficit/hyperactivity, autism, bipolar, major depressive, obsessive-compulsive and schizophrenia) and 1,465 matched controls (54% male) recruited across 25 scan sites. We developed a framework to characterize deviation heterogeneity at multiple spatial scales, from individual voxels, through inter-regional connections, specific brain regions, and spatially extended brain networks. RESULTS The specific locations of white matter volume deviations were highly heterogeneous across participants, affecting the same voxel in fewer than 8% of individuals with the same diagnosis. For autism and schizophrenia, negative deviations (i.e., areas where volume is lower than normative expectations) aggregated into common tracts, regions, and large-scale networks in up to 69% of individuals. CONCLUSIONS The prevalence of white matter volume deviations was lower than previously observed in grey matter, and the specific location of these deviations was highly heterogeneous when considering voxel-wise spatial resolution. Evidence of aggregation within common pathways and networks was apparent in schizophrenia and autism, but not other disorders.
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Affiliation(s)
- Ashlea Segal
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia; Monash Biomedical Imaging, Monash University, Melbourne, Australia; Wu Tsai Institute, Department of Neuroscience, Yale University, New Haven, United States.
| | - Robert E Smith
- The Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia; Florey Department of Neuroscience and Mental Health, Parkville, Victoria, Australia
| | - Sidhant Chopra
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Stuart Oldham
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia; Monash Biomedical Imaging, Monash University, Melbourne, Australia; Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia
| | - Linden Parkes
- Department of Psychiatry, Rutgers University, Piscataway, NJ 08854, USA
| | | | - Seyed Mostafa Kia
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands; Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands; Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands; Department of Cognitive Science and Artificial Intelligence, Tilburg School of Humanities and Digital Sciences, Tilburg University, Tilburg, the Netherlands
| | - Thomas Wolfers
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo & Oslo University Hospital, Oslo, Norway; Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health (TÜCMH), University of Tübingen, Tübingen, Germany
| | - Barbara Franke
- Department of Cognitive Neuroscience, Donders Institute of Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands; Department of Human Genetics, Donders Institute of Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Martine Hoogman
- Department of Human Genetics, Donders Institute of Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands; Department of Psychiatry, Donders Institute of Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Christian F Beckmann
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands; Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands; Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Lars T Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo & Oslo University Hospital, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Ole A Andreassen
- Department of Psychology, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Victoria, Australia; Department of Biomedical Engineering, The University of Melbourne, Victoria, Australia
| | - Ben J Harrison
- Department of Psychiatry, The University of Melbourne, Victoria, Australia
| | | | - Carles Soriano-Mas
- Department of Psychiatry, Bellvitge University Hospital. Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Carlos III Health Institute, Madrid, Spain; Department of Social Psychology and Quantitative Psychology, Universitat de Barcelona-UB, Barcelona, Spain
| | - Narcís Cardoner
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Carlos III Health Institute, Madrid, Spain; Sant Pau Mental Health Research Group, Institut d'Investigació Biomèdica Sant Pau (IIB-Sant Pau), Hospital de la Santa Creu i Sant Pau, Barcelona, Spain; Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Jeggan Tiego
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia; Monash Biomedical Imaging, Monash University, Melbourne, Australia
| | - Murat Yücel
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia; QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Leah Braganza
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Chao Suo
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia; Monash Biomedical Imaging, Monash University, Melbourne, Australia; Australian Characterisation Commons at Scale (ACCS) Project, Monash eResearch Centre, Melbourne, Australia
| | - Michael Berk
- Deakin University, IMPACT - the Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, Geelong, Australia; Orygen, Melbourne, Australia; Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia; Florey Institute for Neuroscience and Mental Health, Parkville, Australia
| | - Sue Cotton
- Orygen, Melbourne, Australia; Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
| | - Mark A Bellgrove
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Andre F Marquand
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands; Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands; Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands; Department of Neuroimaging, Centre of Neuroimaging Sciences, Institute of Psychiatry, King's College London, London, The United Kingdom
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia; Monash Biomedical Imaging, Monash University, Melbourne, Australia
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Zhang J, Toulopoulou T, Li Q, Niu L, Peng L, Dai H, Chen K, Wang X, Huang R, Wei X, Zhang R. Charting brain GABA and glutamate levels across psychiatric disorders by quantitative analysis of 121 1H-MRS studies. Psychol Med 2024:1-12. [PMID: 39564744 DOI: 10.1017/s0033291724001673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/21/2024]
Abstract
BACKGROUND Psychiatric diagnosis is based on categorical diagnostic classification, yet similarities in genetics and clinical features across disorders suggest that these classifications share commonalities in neurobiology, particularly regarding neurotransmitters. Glutamate (Glu) and gamma-aminobutyric acid (GABA), the brain's primary excitatory and inhibitory neurotransmitters, play critical roles in brain function and physiological processes. METHODS We examined the levels of Glu, combined glutamate and glutamine (Glx), and GABA across psychiatric disorders by pooling data from 121 1H-MRS studies and further divided the sample based on Axis I disorders. RESULTS Statistically significant differences in GABA levels were found in the combined psychiatric group compared with healthy controls (Hedge's g = -0.112, p = 0.008). Further analyses based on brain regions showed that brain GABA levels significantly differed across Axis I disorders and controls in the parieto-occipital cortex (Hedge's g = 0.277, p = 0.019). Furthermore, GABA levels were reduced in affective disorders in the occipital cortex (Hedge's g = -0.468, p = 0.043). Reductions in Glx levels were found in neurodevelopmental disorders (Hedge's g = -0.287, p = 0.022). Analysis focusing on brain regions suggested that Glx levels decreased in the frontal cortex (Hedge's g = -0.226, p = 0.025), and the reduction of Glu levels in patients with affective disorders in the frontal cortex is marginally significant (Hedge's g = -0.172, p = 0.052). When analyzing the anterior cingulate cortex and prefrontal cortex separately, reductions were only found in GABA levels in the former (Hedge's g = - 0.191, p = 0.009) across all disorders. CONCLUSIONS Altered glutamatergic and GABAergic metabolites were found across psychiatric disorders, indicating shared dysfunction. We found reduced GABA levels across psychiatric disorders and lower Glu levels in affective disorders. These results highlight the significance of GABA and Glu in psychiatric etiology and partially support rethinking current diagnostic categories.
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Affiliation(s)
- Jiayuan Zhang
- Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Timothea Toulopoulou
- Department of Psychology & National Magnetic Resonance Research Center (UMRAM) & Aysel Sabuncu Brain Research Center, Bilkent University, Ankara, Turkey
- Department of Psychiatry, National and Kapodistrian University of Athens, Athens, Greece
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Qian Li
- Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Lijing Niu
- Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Lanxin Peng
- Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Haowei Dai
- Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Keyin Chen
- Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Xingqin Wang
- Department of Neurosurgery, Institute of Brain Diseases, Nanfang Hospital of Southern Medical University, Guangzhou, PR China
| | - Ruiwang Huang
- School of Psychology, South China Normal University, Guangzhou, China
| | - Xinhua Wei
- Department of Radiology, Guangzhou First Affiliated Hospital, Guangzhou, PR China
| | - Ruibin Zhang
- Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Guangdong-Hong Kong Joint Laboratory for Psychiatric Disorders, Guangdong Basic Research Center of Excellence for Integrated Traditional and Western Medicine for Qingzhi Diseases
- Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, PR China
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Murray SO, Kolodny T, Webb SJ. Linking cortical surface area to computational properties in human visual perception. iScience 2024; 27:110490. [PMID: 39148711 PMCID: PMC11325354 DOI: 10.1016/j.isci.2024.110490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 05/17/2024] [Accepted: 07/09/2024] [Indexed: 08/17/2024] Open
Abstract
Cortical structure and function are closely linked, shaping the neural basis of human behavior. This study explores how cortical surface area (SA), a structural feature, influences computational properties in human visual perception. Using a combination of psychophysical, neuroimaging, and computational modeling approaches, we find that variations in SA across the parietal and frontal cortices are linked to distinct behavioral patterns in a motion perception task. These differences in behavior correspond to specific parameters within a divisive normalization model, indicating a unique contribution of SA to the spatial organization of cortical circuitry. This work highlights the importance of cortical architecture in modifying computational processes that underlie perception, enhancing our understanding of how structural differences can influence neural function and behavior.
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Affiliation(s)
- Scott O. Murray
- Department of Psychology, University of Washington, Seattle, WA 98195, USA
| | - Tamar Kolodny
- Department of Psychology and the School of Brain Sciences and Cognition, Ben-Gurion University, Beer Sheva, Israel
| | - Sara Jane Webb
- Department of Psychiatry and Behavioral Science, University of Washington, Seattle, WA 98195, USA
- Seattle Children’s Research Institute, 1920 Terry Avenue, Building Cure-03, Seattle, WA 98101, USA
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4
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Segal A, Smith RE, Chopra S, Oldham S, Parkes L, Aquino K, Kia SM, Wolfers T, Franke B, Hoogman M, Beckmann CF, Westlye LT, Andreassen OA, Zalesky A, Harrison BJ, Davey CG, Soriano-Mas C, Cardoner N, Tiego J, Yücel M, Braganza L, Suo C, Berk M, Cotton S, Bellgrove MA, Marquand AF, Fornito A. Multiscale heterogeneity of white matter morphometry in psychiatric disorders. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.04.606523. [PMID: 39149253 PMCID: PMC11326206 DOI: 10.1101/2024.08.04.606523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Background Inter-individual variability in neurobiological and clinical characteristics in mental illness is often overlooked by classical group-mean case-control studies. Studies using normative modelling to infer person-specific deviations of grey matter volume have indicated that group means are not representative of most individuals. The extent to which this variability is present in white matter morphometry, which is integral to brain function, remains unclear. Methods We applied Warped Bayesian Linear Regression normative models to T1-weighted magnetic resonance imaging data and mapped inter-individual variability in person-specific white matter volume deviations in 1,294 cases (58% male) diagnosed with one of six disorders (attention-deficit/hyperactivity, autism, bipolar, major depressive, obsessive-compulsive and schizophrenia) and 1,465 matched controls (54% male) recruited across 25 scan sites. We developed a framework to characterize deviation heterogeneity at multiple spatial scales, from individual voxels, through inter-regional connections, specific brain regions, and spatially extended brain networks. Results The specific locations of white matter volume deviations were highly heterogeneous across participants, affecting the same voxel in fewer than 8% of individuals with the same diagnosis. For autism and schizophrenia, negative deviations (i.e., areas where volume is lower than normative expectations) aggregated into common tracts, regions and large-scale networks in up to 35% of individuals. Conclusions The prevalence of white matter volume deviations was lower than previously observed in grey matter, and the specific location of these deviations was highly heterogeneous when considering voxel-wise spatial resolution. Evidence of aggregation within common pathways and networks was apparent in schizophrenia and autism but not other disorders.
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Affiliation(s)
- Ashlea Segal
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
- Wu Tsai Institute, Department of Neuroscience, Yale University, New Haven, United States
| | - Robert E Smith
- The Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia
- Florey Department of Neuroscience and Mental Health, Parkville, Victoria, Australia
| | - Sidhant Chopra
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Stuart Oldham
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
- Developmental Imaging, Murdoch Children’s Research Institute, Melbourne, Australia
| | - Linden Parkes
- Department of Psychiatry, Rutgers University, Piscataway, NJ 08854, USA
| | | | - Seyed Mostafa Kia
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Cognitive Science and Artificial Intelligence, Tilburg School of Humanities and Digital Sciences, Tilburg University, Tilburg, the Netherlands
| | - Thomas Wolfers
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo & Oslo University Hospital, Oslo, Norway
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health (TÜCMH), University of Tübingen, Tübingen, Germany
| | - Barbara Franke
- Department of Cognitive Neuroscience, Donders Institute of Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Human Genetics, Donders Institute of Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Martine Hoogman
- Department of Human Genetics, Donders Institute of Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Psychiatry, Donders Institute of Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Christian F. Beckmann
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
- Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Lars T. Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo & Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Ole A. Andreassen
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Victoria, Australia
- Department of Biomedical Engineering, The University of Melbourne, Victoria, Australia
| | - Ben J. Harrison
- Department of Psychiatry, The University of Melbourne, Victoria, Australia
| | | | - Carles Soriano-Mas
- Department of Psychiatry, Bellvitge University Hospital. Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Carlos III Health Institute, Madrid, Spain
- Department of Social Psychology and Quantitative Psychology, Universitat de Barcelona-UB, Barcelona, Spain
| | - Narcís Cardoner
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Carlos III Health Institute, Madrid, Spain
- Sant Pau Mental Health Research Group, Institut d’Investigació Biomèdica Sant Pau (IIB-Sant Pau), Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Jeggan Tiego
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
| | - Murat Yücel
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Leah Braganza
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Chao Suo
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
- Australian Characterisation Commons at Scale (ACCS) Project, Monash eResearch Centre, Melbourne, Australia
| | - Michael Berk
- Deakin University, IMPACT – the Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, Geelong, Australia
- Orygen, Melbourne, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
- Florey Institute for Neuroscience and Mental Health, Parkville, Australia
| | - Sue Cotton
- Orygen, Melbourne, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
| | - Mark A. Bellgrove
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Andre F. Marquand
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
- Department of Neuroimaging, Centre of Neuroimaging Sciences, Institute of Psychiatry, King’s College London, London, The United Kingdom
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
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5
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Segal A, Parkes L, Aquino K, Kia SM, Wolfers T, Franke B, Hoogman M, Beckmann CF, Westlye LT, Andreassen OA, Zalesky A, Harrison BJ, Davey CG, Soriano-Mas C, Cardoner N, Tiego J, Yücel M, Braganza L, Suo C, Berk M, Cotton S, Bellgrove MA, Marquand AF, Fornito A. Regional, circuit and network heterogeneity of brain abnormalities in psychiatric disorders. Nat Neurosci 2023; 26:1613-1629. [PMID: 37580620 PMCID: PMC10471501 DOI: 10.1038/s41593-023-01404-6] [Citation(s) in RCA: 68] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 07/13/2023] [Indexed: 08/16/2023]
Abstract
The substantial individual heterogeneity that characterizes people with mental illness is often ignored by classical case-control research, which relies on group mean comparisons. Here we present a comprehensive, multiscale characterization of the heterogeneity of gray matter volume (GMV) differences in 1,294 cases diagnosed with one of six conditions (attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, depression, obsessive-compulsive disorder and schizophrenia) and 1,465 matched controls. Normative models indicated that person-specific deviations from population expectations for regional GMV were highly heterogeneous, affecting the same area in <7% of people with the same diagnosis. However, these deviations were embedded within common functional circuits and networks in up to 56% of cases. The salience-ventral attention system was implicated transdiagnostically, with other systems selectively involved in depression, bipolar disorder, schizophrenia and attention-deficit/hyperactivity disorder. Phenotypic differences between cases assigned the same diagnosis may thus arise from the heterogeneous localization of specific regional deviations, whereas phenotypic similarities may be attributable to the dysfunction of common functional circuits and networks.
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Affiliation(s)
- Ashlea Segal
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia.
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia.
| | - Linden Parkes
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Rutgers University, Piscataway, NJ, USA
| | - Kevin Aquino
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
- School of Physics, University of Sydney, Sydney, New South Wales, Australia
- BrainKey Inc, Palo alto, CA, USA
| | - Seyed Mostafa Kia
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands
- Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, the Netherlands
| | - Thomas Wolfers
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health (TÜCMH), University of Tübingen, Tübingen, Germany
| | - Barbara Franke
- Department of Psychiatry, Donders Institute of Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Human Genetics, Donders Institute of Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Martine Hoogman
- Department of Psychiatry, Donders Institute of Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Human Genetics, Donders Institute of Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Christian F Beckmann
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, the Netherlands
- Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Lars T Westlye
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia
| | - Ben J Harrison
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia
| | - Christopher G Davey
- Department of Psychiatry, University of Melbourne, Melbourne, Victoria, Australia
| | - Carles Soriano-Mas
- Department of Psychiatry, Bellvitge University Hospital, Bellvitge Biomedical Research Institute, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Carlos III Health Institute, Madrid, Spain
- Department of Social Psychology and Quantitative Psychology, Universitat de Barcelona, Barcelona, Spain
| | - Narcís Cardoner
- Centro de Investigación Biomédica en Red de Salud Mental, Carlos III Health Institute, Madrid, Spain
- Sant Pau Mental Health Research Group, Institut d'Investigació Biomèdica Sant Pau, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Jeggan Tiego
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
| | - Murat Yücel
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Leah Braganza
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Chao Suo
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
- Australian Characterisation Commons at Scale (ACCS) Project, Monash eResearch Centre, Melbourne, Victoria, Australia
| | - Michael Berk
- Institute for Mental and Physical Health and Clinical Translation School of Medicine, Deakin University, Geelong, Victoria, Australia
- Orygen, Melbourne, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
- Florey Institute for Neuroscience and Mental Health, Parkville, Victoria, Australia
| | - Sue Cotton
- Orygen, Melbourne, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Mark A Bellgrove
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Andre F Marquand
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, the Netherlands
- Department of Neuroimaging, Centre of Neuroimaging Sciences, Institute of Psychiatry, King's College London, London, UK
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia.
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia.
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6
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Xiao NG, Emberson LL. Visual Perception Is Highly Flexible and Context Dependent in Young Infants: A Case of Top-Down-Modulated Motion Perception. Psychol Sci 2023; 34:875-886. [PMID: 37310866 PMCID: PMC10477967 DOI: 10.1177/09567976231177968] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 04/19/2023] [Indexed: 06/15/2023] Open
Abstract
Top-down modulation is an essential cognitive component in human perception. Despite mounting evidence of top-down perceptual modulation in adults, it is largely unknown whether infants can engage in this cognitive function. Here, we examined top-down modulation of motion perception in 6- to 8-month-old infants (recruited in North America) via their smooth-pursuit eye movements. In four experiments, we demonstrated that infants' perception of motion direction can be flexibly shaped by briefly learned predictive cues when no coherent motion is available. The current findings present a novel insight into infant perception and its development: Infant perceptual systems respond to predictive signals engendered from higher-level learning systems, leading to a flexible and context-dependent modulation of perception. This work also suggests that the infant brain is sophisticated, interconnected, and active when placed in a context in which it can learn and predict.
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Affiliation(s)
- Naiqi G. Xiao
- Department of Psychology, Neuroscience & Behaviour, McMaster University
| | - Lauren L. Emberson
- Department of Psychology, University of British Columbia
- Department of Psychology, Princeton University
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7
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Murray SO, Kolodny T, Webb SJ. Cortical Surface Area Relates to Distinct Computational Properties in Human Visual Perception. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.16.545373. [PMID: 37398212 PMCID: PMC10312808 DOI: 10.1101/2023.06.16.545373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Understanding the relationship between cortical structure and function is essential for elucidating the neural basis of human behavior. However, the impact of cortical structural features on the computational properties of neural circuits remains poorly understood. In this study, we demonstrate that a simple structural feature - cortical surface area (SA) - relates to specific computational properties underlying human visual perception. By combining psychophysical, neuroimaging, and computational modeling approaches, we show that differences in SA in the parietal and frontal cortices are associated with distinct patterns of behavior in a motion perception task. These behavioral differences can be accounted for by specific parameters of a divisive normalization model, suggesting that SA in these regions contributes uniquely to the spatial organization of cortical circuitry. Our findings provide novel evidence linking cortical structure to distinct computational properties and offer a framework for understanding how cortical architecture can impact human behavior.
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Affiliation(s)
- Scott O. Murray
- Department of Psychology, University of Washington, Seattle WA USA 98195
| | - Tamar Kolodny
- Department of Psychology, University of Washington, Seattle WA USA 98195
| | - Sara Jane Webb
- Department of Psychiatry and Behavioral Science, University of Washington, Seattle WA USA 98195
- Seattle Children’s Research Institute, 1920 Terry Ave, Building Cure-03, Seattle WA 98101
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8
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Orekhova EV, Manyukhina VO, Galuta IA, Prokofyev AO, Goiaeva DE, Obukhova TS, Fadeev KA, Schneiderman JF, Stroganova TA. Gamma oscillations point to the role of primary visual cortex in atypical motion processing in autism. PLoS One 2023; 18:e0281531. [PMID: 36780507 PMCID: PMC9925089 DOI: 10.1371/journal.pone.0281531] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 01/25/2023] [Indexed: 02/15/2023] Open
Abstract
Neurophysiological studies suggest that abnormal neural inhibition may explain a range of sensory processing differences in autism spectrum disorders (ASD). In particular, the impaired ability of people with ASD to visually discriminate the motion direction of small-size objects and their reduced perceptual suppression of background-like visual motion may stem from deficient surround inhibition within the primary visual cortex (V1) and/or its atypical top-down modulation by higher-tier cortical areas. In this study, we estimate the contribution of abnormal surround inhibition to the motion-processing deficit in ASD. For this purpose, we used a putative correlate of surround inhibition-suppression of the magnetoencephalographic (MEG) gamma response (GR) caused by an increase in the drift rate of a large annular high-contrast grating. The motion direction discrimination thresholds for the gratings of different angular sizes (1° and 12°) were assessed in a separate psychophysical paradigm. The MEG data were collected in 42 boys with ASD and 37 typically developing (TD) boys aged 7-15 years. Psychophysical data were available in 33 and 34 of these participants, respectively. The results showed that the GR suppression in V1 was reduced in boys with ASD, while their ability to detect the direction of motion was compromised only in the case of small stimuli. In TD boys, the GR suppression directly correlated with perceptual suppression caused by increasing stimulus size, thus suggesting the role of the top-down modulations of V1 in surround inhibition. In ASD, weaker GR suppression was associated with the poor directional sensitivity to small stimuli, but not with perceptual suppression. These results strongly suggest that a local inhibitory deficit in V1 plays an important role in the reduction of directional sensitivity in ASD and that this perceptual deficit cannot be explained exclusively by atypical top-down modulation of V1 by higher-tier cortical areas.
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Affiliation(s)
- Elena V. Orekhova
- Center for Neurocognitive Research (MEG Center), Moscow State University of Psychology and Education, Moscow, Russian Federation
- * E-mail:
| | - Viktoriya O. Manyukhina
- Center for Neurocognitive Research (MEG Center), Moscow State University of Psychology and Education, Moscow, Russian Federation
- National Research University Higher School of Economics, Moscow, Russian Federation
| | - Ilia A. Galuta
- Center for Neurocognitive Research (MEG Center), Moscow State University of Psychology and Education, Moscow, Russian Federation
| | - Andrey O. Prokofyev
- Center for Neurocognitive Research (MEG Center), Moscow State University of Psychology and Education, Moscow, Russian Federation
| | - Dzerassa E. Goiaeva
- Center for Neurocognitive Research (MEG Center), Moscow State University of Psychology and Education, Moscow, Russian Federation
| | - Tatiana S. Obukhova
- Center for Neurocognitive Research (MEG Center), Moscow State University of Psychology and Education, Moscow, Russian Federation
| | - Kirill A. Fadeev
- Center for Neurocognitive Research (MEG Center), Moscow State University of Psychology and Education, Moscow, Russian Federation
| | - Justin F. Schneiderman
- MedTech West and the Institute of Neuroscience and Physiology, Sahlgrenska Academy, The University of Gothenburg, Gothenburg, Sweden
| | - Tatiana A. Stroganova
- Center for Neurocognitive Research (MEG Center), Moscow State University of Psychology and Education, Moscow, Russian Federation
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9
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Park S, Zikopoulos B, Yazdanbakhsh A. Visual illusion susceptibility in autism: A neural model. Eur J Neurosci 2022; 56:4246-4265. [PMID: 35701859 PMCID: PMC9541695 DOI: 10.1111/ejn.15739] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 04/04/2022] [Accepted: 06/06/2022] [Indexed: 11/26/2022]
Abstract
While atypical sensory perception is reported among individuals with autism spectrum disorder (ASD), the underlying neural mechanisms of autism that give rise to disruptions in sensory perception remain unclear. We developed a neural model with key physiological, functional and neuroanatomical parameters to investigate mechanisms underlying the range of representations of visual illusions related to orientation perception in typically developed subjects compared to individuals with ASD. Our results showed that two theorized autistic traits, excitation/inhibition imbalance and weakening of top‐down modulation, could be potential candidates for reduced susceptibility to some visual illusions. Parametric correlation between cortical suppression, balance of excitation/inhibition, feedback from higher visual areas on one hand and susceptibility to a class of visual illusions related to orientation perception on the other hand provide the opportunity to investigate the contribution and complex interactions of distinct sensory processing mechanisms in ASD. The novel approach used in this study can be used to link behavioural, functional and neuropathological studies; estimate and predict perceptual and cognitive heterogeneity in ASD; and form a basis for the development of novel diagnostics and therapeutics.
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Affiliation(s)
- Sangwook Park
- Computational Neuroscience and Vision Laboratory, Boston University, Boston, Massachusetts, USA
| | - Basilis Zikopoulos
- Human Systems Neuroscience Laboratory, Department of Health Sciences, Boston University, Boston, Massachusetts, USA.,Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, Massachusetts, USA.,Center for Systems Neuroscience, Boston University, Boston, Massachusetts, USA.,Graduate Program for Neuroscience, Boston University, Boston, Massachusetts, USA
| | - Arash Yazdanbakhsh
- Computational Neuroscience and Vision Laboratory, Boston University, Boston, Massachusetts, USA.,Center for Systems Neuroscience, Boston University, Boston, Massachusetts, USA.,Graduate Program for Neuroscience, Boston University, Boston, Massachusetts, USA.,Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts, USA
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10
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Spiteri S, Crewther D. Neural Mechanisms of Visual Motion Anomalies in Autism: A Two-Decade Update and Novel Aetiology. Front Neurosci 2021; 15:756841. [PMID: 34790092 PMCID: PMC8591069 DOI: 10.3389/fnins.2021.756841] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 10/11/2021] [Indexed: 11/13/2022] Open
Abstract
The 21st century has seen dramatic changes in our understanding of the visual physio-perceptual anomalies of autism and also in the structure and development of the primate visual system. This review covers the past 20 years of research into motion perceptual/dorsal stream anomalies in autism, as well as new understanding of the development of primate vision. The convergence of this literature allows a novel developmental hypothesis to explain the physiological and perceptual differences of the broad autistic spectrum. Central to these observations is the development of motion areas MT+, the seat of the dorsal cortical stream, central area of pre-attentional processing as well as being an anchor of binocular vision for 3D action. Such development normally occurs via a transfer of thalamic drive from the inferior pulvinar → MT to the anatomically stronger but later-developing LGN → V1 → MT connection. We propose that autistic variation arises from a slowing in the normal developmental attenuation of the pulvinar → MT pathway. We suggest that this is caused by a hyperactive amygdala → thalamic reticular nucleus circuit increasing activity in the PIm → MT via response gain modulation of the pulvinar and hence altering synaptic competition in area MT. We explore the probable timing of transfer in dominance of human MT from pulvinar to LGN/V1 driving circuitry and discuss the implications of the main hypothesis.
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Affiliation(s)
- Samuel Spiteri
- Centre for Human Psychopharmacology, Swinburne University of Technology, Melbourne, VIC, Australia
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11
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Wiring of higher-order cortical areas: Spatiotemporal development of cortical hierarchy. Semin Cell Dev Biol 2021; 118:35-49. [PMID: 34034988 DOI: 10.1016/j.semcdb.2021.05.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 04/27/2021] [Accepted: 05/08/2021] [Indexed: 01/04/2023]
Abstract
A hierarchical development of cortical areas was suggested over a century ago, but the diversity and complexity of cortical hierarchy properties have so far prevented a formal demonstration. The aim of this review is to clarify the similarities and differences in the developmental processes underlying cortical development of primary and higher-order areas. We start by recapitulating the historical and recent advances underlying the biological principle of cortical hierarchy in adults. We then revisit the arguments for a hierarchical maturation of cortical areas, and further integrate the principles of cortical areas specification during embryonic and postnatal development. We highlight how the dramatic expansion in cortical size might have contributed to the increased number of association areas sustaining cognitive complexification in evolution. Finally, we summarize the recent observations of an alteration of cortical hierarchy in neuropsychiatric disorders and discuss their potential developmental origins.
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12
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Mo Y, Wei Q, Bai T, Zhang T, Lv H, Zhang L, Ji G, Yu F, Tian Y, Wang K. Bifrontal electroconvulsive therapy changed regional homogeneity and functional connectivity of left angular gyrus in major depressive disorder. Psychiatry Res 2020; 294:113461. [PMID: 33038791 DOI: 10.1016/j.psychres.2020.113461] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Accepted: 09/15/2020] [Indexed: 12/11/2022]
Abstract
Electroconvulsive therapy (ECT) is a rapid and effective treatment for MDD. However, the mechanism of ECT for MDD has not been clarified. In this study, we used resting-state functional magnetic resonance imaging (rs-fMRI) to explore the mechanism of ECT. Two groups of subjects were recruited: healthy controls (HCs) and MDD patients who received bifrontal ECT. MDD patients and HCs underwent rs-fMRI scans and clinical assessments (Hamilton Depression Rating Scale, Rey-Auditory Verbal Learning Test (RAVLT), and the verbal fluency test). Regional homogeneity (ReHo) and functional connectivity were evaluated for the analysis of rs-fMRI data. The results showed that ReHo values in the left angular gyrus (LAG) significantly increased in MDD patients after ECT, and the functional connectivity of the LAG with bilateral inferior temporal gyrus, bilateral middle frontal gyrus, left superior frontal gyrus, left middle temporal gyrus, left precuneus, left posterior cingulate gyrus, and right angular gyrus was found to be strengthened after ECT. The scores of delayed recall trial in the RAVLT of MDD patients were related to the functional connectivity of the LAG with the left inferior temporal gyrus and the left posterior cingulate gyrus. It indicated LAG palyed an important role in the mechanism of ECT in MDD.
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Affiliation(s)
- Yuting Mo
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
| | - Qiang Wei
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
| | - Tongjian Bai
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
| | - Ting Zhang
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
| | - Huaming Lv
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China; Anhui Mental Health Center, Hefei, Anhui, China
| | - Li Zhang
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China; Anhui Mental Health Center, Hefei, Anhui, China
| | - Gongjun Ji
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China; Department of medical psychology, Anhui Medical University, Hefei, China
| | - Fengqiong Yu
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China; Department of medical psychology, Anhui Medical University, Hefei, China
| | - Yanghua Tian
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China.
| | - Kai Wang
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China; Department of medical psychology, Anhui Medical University, Hefei, China.
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13
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Murray SO, Kolodny T, Schallmo MP, Gerdts J, Bernier RA. Late fMRI Response Components Are Altered in Autism Spectrum Disorder. Front Hum Neurosci 2020; 14:241. [PMID: 32694986 PMCID: PMC7338757 DOI: 10.3389/fnhum.2020.00241] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 06/02/2020] [Indexed: 12/01/2022] Open
Abstract
Disrupted cortical neural inhibition has been hypothesized to be a primary contributor to the pathophysiology of autism spectrum disorder (ASD). This hypothesis predicts that ASD will be associated with an increase in neural responses. We tested this prediction by comparing fMRI response magnitudes to simultaneous visual, auditory, and motor stimulation in ASD and neurotypical (NT) individuals. No increases in the initial transient response in any brain region were observed in ASD, suggesting that there is no increase in overall cortical neural excitability. Most notably, there were widespread fMRI magnitude increases in the ASD response following stimulation offset, approximately 6–8 s after the termination of sensory and motor stimulation. In some regions, the higher fMRI offset response in ASD could be attributed to a lack of an “undershoot”—an often observed feature of fMRI responses believed to reflect inhibitory processing. Offset response magnitude was associated with reaction times (RT) in the NT group and may explain an overall reduced RT in the ASD group. Overall, our results suggest that increases in neural responsiveness are present in ASD but are confined to specific components of the neural response, are particularly strong following stimulation offset, and are linked to differences in RT.
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Affiliation(s)
- Scott O Murray
- Department of Psychology, University of Washington, Seattle, WA, United States
| | - Tamar Kolodny
- Department of Psychology, University of Washington, Seattle, WA, United States
| | - Michael-Paul Schallmo
- Department of Psychiatry and Behavioral Science, University of Minnesota, Minneapolis, MN, United States
| | - Jennifer Gerdts
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, United States
| | - Raphael A Bernier
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, United States
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14
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Abstract
Abnormal sensory processing has been observed in autism, including superior visual motion discrimination, but the neural basis for these sensory changes remains unknown. Leveraging well-characterized suppressive neural circuits in the visual system, we used behavioral and fMRI tasks to demonstrate a significant reduction in neural suppression in young adults with autism spectrum disorder (ASD) compared to neurotypical controls. MR spectroscopy measurements revealed no group differences in neurotransmitter signals. We show how a computational model that incorporates divisive normalization, as well as narrower top-down gain (that could result, for example, from a narrower window of attention), can explain our observations and divergent previous findings. Thus, weaker neural suppression is reflected in visual task performance and fMRI measures in ASD, and may be attributable to differences in top-down processing. Sensory hypersensitivity is common in autism spectrum disorders. Using functional MRI, psychophysics, and computational modeling, Schallmo et al. show that differences in visual motion perception in ASD are accompanied by weaker neural suppression in visual cortex.
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15
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Kolodny T, Schallmo MP, Gerdts J, Edden RAE, Bernier RA, Murray SO. Concentrations of Cortical GABA and Glutamate in Young Adults With Autism Spectrum Disorder. Autism Res 2020; 13:1111-1129. [PMID: 32297709 DOI: 10.1002/aur.2300] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2020] [Revised: 03/02/2020] [Accepted: 03/18/2020] [Indexed: 12/19/2022]
Abstract
The balance of excitation and inhibition in neural circuits is hypothesized to be increased in autism spectrum disorder, possibly mediated by altered signaling of the inhibitory neurotransmitter γ-aminobutyric acid (GABA), yet empirical evidence in humans is inconsistent. We used edited magnetic resonance spectroscopy (MRS) to quantify signals associated with both GABA and the excitatory neurotransmitter glutamate in multiple regions of the sensory and sensorimotor cortex, including primary visual, auditory, and motor areas in adult individuals with autism and in neurotypical controls. Despite the strong a priori hypothesis of reduced GABA in autism spectrum disorder, we found no group differences in neurometabolite concentrations in any of the examined regions and no correlations of MRS measure with psychophysical visual sensitivity or autism symptomatology. We demonstrate high data quality that is comparable across groups, with a relatively large sample of well-characterized participants, and use Bayesian statistics to corroborate the lack of any group differences. We conclude that levels of GABA and Glx (glutamate, glutamine, and glutathione) in the sensory and sensorimotor cortex, as measured with MRS at 3T, are comparable in adults with autism and neurotypical individuals. Autism Res 2020, 13: 1111-1129. © 2020 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: γ-Aminobutyric acid (GABA) and glutamate are the main inhibitory and excitatory neurotransmitters in the human brain, respectively, and their balanced interaction is necessary for neural function. Previous research suggests that the GABA and glutamate systems might be altered in autism. In this study, we used magnetic resonance spectroscopy to measure concentrations of these neurotransmitters in the sensory areas in the brains of young adults with autism. In contradiction to the common hypothesis of reduced GABA in autism, we demonstrate that concentrations of both GABA and glutamate, in all the brain regions examined, are comparable in individuals with autism and in neurotypical adults. © 2020 International Society for Autism Research, Wiley Periodicals, Inc.
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Affiliation(s)
- Tamar Kolodny
- Department of Psychology, University of Washington, Seattle, Washington, USA
| | - Michael-Paul Schallmo
- Department of Psychology, University of Washington, Seattle, Washington, USA.,Department of Psychiatry and Behavioral Science, University of Minnesota, Minneapolis, Minnesota, USA
| | - Jennifer Gerdts
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, Washington, USA
| | - Richard A E Edden
- Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, Maryland, USA
| | - Raphael A Bernier
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, Washington, USA
| | - Scott O Murray
- Department of Psychology, University of Washington, Seattle, Washington, USA
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