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Ramos Benitez J, Kannan S, Hastings WL, Parker BJ, Willbrand EH, Weiner KS. Ventral temporal and posteromedial sulcal morphology in autism spectrum disorder. Neuropsychologia 2024; 195:108786. [PMID: 38181845 DOI: 10.1016/j.neuropsychologia.2024.108786] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 12/14/2023] [Accepted: 01/02/2024] [Indexed: 01/07/2024]
Abstract
Two parallel research tracks link the morphology of small and shallow indentations, or sulci, of the cerebral cortex with functional features of the cortex and human cognition, respectively. The first track identified a relationship between the mid-fusiform sulcus (MFS) in ventral temporal cortex (VTC) and cognition in individuals with Autism Spectrum Disorder (ASD). The second track identified a new sulcus, the inframarginal sulcus (IFRMS), that serves as a tripartite landmark within the posteromedial cortex (PMC). As VTC and PMC are structurally and functionally different in ASD, here, we integrated these two tracks and tested if there are morphological differences in VTC and PMC sulci in a sample of young (5-17 years old) male participants (50 participants with ASD and 50 neurotypical controls). Our approach replicates and extends recent findings in four ways. First, regarding replication, the standard deviation (STD) of MFS cortical thickness (CT) was increased in ASD. Second, MFS length was shorter in ASD. Third, the CT STD effect extended to other VTC and to PMC sulci. Fourth, additional morphological features of VTC sulci (depth, surface area, gray matter volume) and PMC sulci (mean CT) were decreased in ASD, including putative tertiary sulci, which emerge last in gestation and continue to develop after birth. To our knowledge, this study is the most extensive comparison of the sulcal landscape (including putative tertiary sulci) in multiple cortical expanses between individuals with ASD and NTs based on manually defined sulci at the level of individual hemispheres, providing novel targets for future studies of neurodevelopmental disorders more broadly.
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Affiliation(s)
- Javier Ramos Benitez
- Neuroscience Graduate Program, University of Washington School of Medicine, Seattle, WA, USA
| | - Sandhya Kannan
- Department of Radiology, University of California San Francisco, San Francisco, CA, USA
| | - William L Hastings
- Department of Psychology, University of California, Berkeley, Berkeley, CA, USA
| | - Benjamin J Parker
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - Ethan H Willbrand
- Medical Scientist Training Program, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Kevin S Weiner
- Department of Psychology, University of California, Berkeley, Berkeley, CA, USA; Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA.
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2
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Marrus N, Botteron KN, Hawks Z, Pruett JR, Elison JT, Jackson JJ, Markson L, Eggebrecht AT, Burrows CA, Zwaigenbaum L, Dager S, Estes A, Hazlett H, Schultz RT, Piven J, Constantino JN. Social motivation in infancy is associated with familial recurrence of ASD. Dev Psychopathol 2024; 36:101-111. [PMID: 36189644 PMCID: PMC10067534 DOI: 10.1017/s0954579422001006] [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] [Indexed: 11/07/2022]
Abstract
Pre-diagnostic deficits in social motivation are hypothesized to contribute to autism spectrum disorder (ASD), a heritable neurodevelopmental condition. We evaluated psychometric properties of a social motivation index (SMI) using parent-report item-level data from 597 participants in a prospective cohort of infant siblings at high and low familial risk for ASD. We tested whether lower SMI scores at 6, 12, and 24 months were associated with a 24-month ASD diagnosis and whether social motivation's course differed relative to familial ASD liability. The SMI displayed good internal consistency and temporal stability. Children diagnosed with ASD displayed lower mean SMI T-scores at all ages and a decrease in mean T-scores across age. Lower group-level 6-month scores corresponded with higher familial ASD liability. Among high-risk infants, strong decline in SMI T-scores was associated with 10-fold odds of diagnosis. Infant social motivation is quantifiable by parental report, differentiates children with versus without later ASD by age 6 months, and tracks with familial ASD liability, consistent with a diagnostic and susceptibility marker of ASD. Early decrements and decline in social motivation indicate increased likelihood of ASD, highlighting social motivation's importance to risk assessment and clarification of the ontogeny of ASD.
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Affiliation(s)
- Natasha Marrus
- Department of Psychiatry, Washington University School of Medicine
| | | | - Zoë Hawks
- Department of Psychological & Brain Sciences, Washington University in St. Louis
| | - John R. Pruett
- Department of Psychiatry, Washington University School of Medicine
| | - Jed T. Elison
- Institute of Child Development, University of Minnesota
| | - Joshua J. Jackson
- Department of Psychological & Brain Sciences, Washington University in St. Louis
| | - Lori Markson
- Department of Psychological & Brain Sciences, Washington University in St. Louis
| | - Adam T. Eggebrecht
- Mallinckrodt Institute of Radiology, Washington University School of Medicine
| | | | | | | | - Annette Estes
- Department of Speech and Hearing Sciences, University of Washington
| | - Heather Hazlett
- Department of Psychiatry, University of North Carolina at Chapel Hill
| | | | - Joseph Piven
- Department of Psychiatry, University of North Carolina at Chapel Hill
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3
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Zhao G, Zhang H, Ma L, Wang Y, Chen R, Liu N, Men W, Tan S, Gao JH, Qin S, He Y, Dong Q, Tao S. Reduced volume of the left cerebellar lobule VIIb and its increased connectivity within the cerebellum predict more general psychopathology one year later via worse cognitive flexibility in children. Dev Cogn Neurosci 2023; 63:101296. [PMID: 37690374 PMCID: PMC10507200 DOI: 10.1016/j.dcn.2023.101296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 08/30/2023] [Accepted: 09/05/2023] [Indexed: 09/12/2023] Open
Abstract
Predicting the risk for general psychopathology (the p factor) requires the examination of multiple factors ranging from brain to cognitive skills. While an increasing number of findings have reported the roles of the cerebral cortex and executive functions, it is much less clear whether and how the cerebellum and cognitive flexibility (a core component of executive function) may be associated with the risk for general psychopathology. Based on the data from more than 400 children aged 6-12 in the Children School Functions and Brain Development (CBD) Project, this study examined whether the left cerebellar lobule VIIb and its connectivity within the cerebellum may prospectively predict the risk for general psychopathology one year later and whether cognitive flexibility may mediate such predictions in school-age children. The reduced gray matter volume in the left cerebellar lobule VIIb and the increased connectivity of this region to the left cerebellar lobule VI prospectively predicted the risk for general psychopathology and was partially mediated by worse cognitive flexibility. Deficits in cognitive flexibility may play an important role in linking cerebellar structure and function to the risk for general psychopathology.
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Affiliation(s)
- Gai Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Haibo Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Leilei Ma
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Rui Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Ningyu Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Shuping Tan
- Psychiatry Research Center, Beijing Huilongguan Hospital, Peking University, Beijing 100096, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
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4
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Wang M, Xu D, Zhang L, Jiang H. Application of Multimodal MRI in the Early Diagnosis of Autism Spectrum Disorders: A Review. Diagnostics (Basel) 2023; 13:3027. [PMID: 37835770 PMCID: PMC10571992 DOI: 10.3390/diagnostics13193027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 09/13/2023] [Accepted: 09/20/2023] [Indexed: 10/15/2023] Open
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder in children. Early diagnosis and intervention can remodel the neural structure of the brain and improve quality of life but may be inaccurate if based solely on clinical symptoms and assessment scales. Therefore, we aimed to analyze multimodal magnetic resonance imaging (MRI) data from the existing literature and review the abnormal changes in brain structural-functional networks, perfusion, neuronal metabolism, and the glymphatic system in children with ASD, which could help in early diagnosis and precise intervention. Structural MRI revealed morphological differences, abnormal developmental trajectories, and network connectivity changes in the brain at different ages. Functional MRI revealed disruption of functional networks, abnormal perfusion, and neurovascular decoupling associated with core ASD symptoms. Proton magnetic resonance spectroscopy revealed abnormal changes in the neuronal metabolites during different periods. Decreased diffusion tensor imaging signals along the perivascular space index reflected impaired glymphatic system function in children with ASD. Differences in age, subtype, degree of brain damage, and remodeling in children with ASD led to heterogeneity in research results. Multimodal MRI is expected to further assist in early and accurate clinical diagnosis of ASD through deep learning combined with genomics and artificial intelligence.
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Affiliation(s)
- Miaoyan Wang
- Department of Radiology, Affiliated Children’s Hospital of Jiangnan University, Wuxi 214000, China; (M.W.); (D.X.)
| | - Dandan Xu
- Department of Radiology, Affiliated Children’s Hospital of Jiangnan University, Wuxi 214000, China; (M.W.); (D.X.)
| | - Lili Zhang
- Department of Child Health Care, Affiliated Children’s Hospital of Jiangnan University, Wuxi 214000, China
| | - Haoxiang Jiang
- Department of Radiology, Affiliated Children’s Hospital of Jiangnan University, Wuxi 214000, China; (M.W.); (D.X.)
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Borges MS, Hoffmann MS, Simioni A, Axelrud LK, Teixeira DS, Zugman A, Jackowski A, Pan PM, Bressan RA, Parker N, Germann J, Bado PP, Satterthwaite TD, Milham MP, Chakravarty MM, Paim Rohde LA, Constantino Miguel E, Paus T, Salum GA. Deviations from a typical development of the cerebellum in youth are associated with psychopathology, executive functions and educational outcomes. Psychol Med 2023; 53:5698-5708. [PMID: 36226568 DOI: 10.1017/s0033291722002926] [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: 11/06/2022]
Abstract
BACKGROUND Understanding deviations from typical brain development is a promising approach to comprehend pathophysiology in childhood and adolescence. We investigated if cerebellar volumes different than expected for age and sex could predict psychopathology, executive functions and academic achievement. METHODS Children and adolescents aged 6-17 years from the Brazilian High-Risk Cohort Study for Mental Conditions had their cerebellar volume estimated using Multiple Automatically Generated Templates from T1-weighted images at baseline (n = 677) and at 3-year follow-up (n = 447). Outcomes were assessed using the Child Behavior Checklist and standardized measures of executive functions and school achievement. Models of typically developing cerebellum were based on a subsample not exposed to risk factors and without mental-health conditions (n = 216). Deviations from this model were constructed for the remaining individuals (n = 461) and standardized variation from age and sex trajectory model was used to predict outcomes in cross-sectional, longitudinal and mediation analyses. RESULTS Cerebellar volumes higher than expected for age and sex were associated with lower externalizing specific factor and higher executive functions. In a longitudinal analysis, deviations from typical development at baseline predicted inhibitory control at follow-up, and cerebellar deviation changes from baseline to follow-up predicted changes in reading and writing abilities. The association between deviations in cerebellar volume and academic achievement was mediated by inhibitory control. CONCLUSIONS Deviations in the cerebellar typical development are associated with outcomes in youth that have long-lasting consequences. This study highlights both the potential of typical developing models and the important role of the cerebellum in mental health, cognition and education.
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Affiliation(s)
- Marina S Borges
- Graduate Program in Psychiatry and Behavioral Sciences, Universidade Federal do Rio Grande do Sul, rua Ramiro Barcelos 2350, Porto Alegre, 90035-003, Brazil
| | - Maurício S Hoffmann
- Graduate Program in Psychiatry and Behavioral Sciences, Universidade Federal do Rio Grande do Sul, rua Ramiro Barcelos 2350, Porto Alegre, 90035-003, Brazil
- Section on Negative Affect and Social Processes, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
- Department of Neuropsychiatry, Universidade Federal de Santa Maria, Avenida Roraima 1000, Santa Maria, 97105-900, Brazil
| | - André Simioni
- Graduate Program in Psychiatry and Behavioral Sciences, Universidade Federal do Rio Grande do Sul, rua Ramiro Barcelos 2350, Porto Alegre, 90035-003, Brazil
- Section on Negative Affect and Social Processes, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Luiza K Axelrud
- Graduate Program in Psychiatry and Behavioral Sciences, Universidade Federal do Rio Grande do Sul, rua Ramiro Barcelos 2350, Porto Alegre, 90035-003, Brazil
- Section on Negative Affect and Social Processes, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Danielle S Teixeira
- Graduate Program in Psychiatry and Behavioral Sciences, Universidade Federal do Rio Grande do Sul, rua Ramiro Barcelos 2350, Porto Alegre, 90035-003, Brazil
- Section on Negative Affect and Social Processes, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - André Zugman
- National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
- Laboratório Interdisciplinar de Neurociências Integrativas (LiNC), Departamento de Psiquiatria, Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil
| | - Andrea Jackowski
- Laboratório Interdisciplinar de Neurociências Integrativas (LiNC), Departamento de Psiquiatria, Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil
| | - Pedro M Pan
- Laboratório Interdisciplinar de Neurociências Integrativas (LiNC), Departamento de Psiquiatria, Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil
| | - Rodrigo A Bressan
- Laboratório Interdisciplinar de Neurociências Integrativas (LiNC), Departamento de Psiquiatria, Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil
| | - Nadine Parker
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Jurgen Germann
- University Health Network, Toronto, ON, Canada
- Cerebral Imaging Centre, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | - Patrícia P Bado
- Graduate Program in Psychiatry and Behavioral Sciences, Universidade Federal do Rio Grande do Sul, rua Ramiro Barcelos 2350, Porto Alegre, 90035-003, Brazil
- Section on Negative Affect and Social Processes, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | | | - Michael P Milham
- Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022, USA
| | - M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
- Department of Biological and Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - Luis Augusto Paim Rohde
- Graduate Program in Psychiatry and Behavioral Sciences, Universidade Federal do Rio Grande do Sul, rua Ramiro Barcelos 2350, Porto Alegre, 90035-003, Brazil
- National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil
| | - Eurípedes Constantino Miguel
- National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil
- Universidade de São Paulo (USP), São Paulo, Brazil
| | - Tomas Paus
- Departments of Psychology and Psychiatry, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada
- Centre hospitalier universitaire Sainte-Justine, University of Montreal, Montreal, Quebec, Canada
| | - Giovanni A Salum
- Graduate Program in Psychiatry and Behavioral Sciences, Universidade Federal do Rio Grande do Sul, rua Ramiro Barcelos 2350, Porto Alegre, 90035-003, Brazil
- Section on Negative Affect and Social Processes, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
- National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil
- Department of Psychiatry and Legal Medicine, Universidade Federal do Rio Grande do Sul, Rua Ramiro Barcelos 2350, Porto Alegre, 90035-003, Brazil
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6
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Elandaloussi Y, Floris DL, Coupé P, Duchesnay E, Mihailov A, Grigis A, Bègue I, Victor J, Frouin V, Leboyer M, Houenou J, Laidi C. Understanding the relationship between cerebellar structure and social abilities. Mol Autism 2023; 14:18. [PMID: 37189195 DOI: 10.1186/s13229-023-00551-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 05/03/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND The cerebellum contains more than 50% of all neurons in the brain and is involved in a broad range of cognitive functions, including social communication and social cognition. Inconsistent atypicalities in the cerebellum have been reported in individuals with autism compared to controls suggesting the limits of categorical case control comparisons. Alternatively, investigating how clinical dimensions are related to neuroanatomical features, in line with the Research Domain Criteria approach, might be more relevant. We hypothesized that the volume of the "cognitive" lobules of the cerebellum would be associated with social difficulties. METHODS We analyzed structural MRI data from a large pediatric and transdiagnostic sample (Healthy Brain Network). We performed cerebellar parcellation with a well-validated automated segmentation pipeline (CERES). We studied how social communication abilities-assessed with the social component of the Social Responsiveness Scale (SRS)-were associated with the cerebellar structure, using linear mixed models and canonical correlation analysis. RESULTS In 850 children and teenagers (mean age 10.8 ± 3 years; range 5-18 years), we found a significant association between the cerebellum, IQ and social communication performance in our canonical correlation model. LIMITATIONS Cerebellar parcellation relies on anatomical boundaries, which does not overlap with functional anatomy. The SRS was originally designed to identify social impairments associated with autism spectrum disorders. CONCLUSION Our results unravel a complex relationship between cerebellar structure, social performance and IQ and provide support for the involvement of the cerebellum in social and cognitive processes.
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Affiliation(s)
- Yannis Elandaloussi
- Sorbonne Université, UFR Médecine, 75005, Paris, France
- Département Médico-Universitaire de Psychiatrie et d'Addictologie (DMU IMPACT), Fédération Hospitalo-Universitaire de Médecine de Précision en Psychiatrie (FHU ADAPT), AP-HP, Hôpitaux Universitaires Henri Mondor, 94010, Créteil, France
- Fondation FondaMental, 94010, Créteil, France
- CEA, Neurospin, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Dorothea L Floris
- Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland
- Donders Institute for Brain, Cognition, and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands
| | - Pierrick Coupé
- Pictura Research Group, Unité Mixte de Recherche Centre National de la Recherche Scientifique (UMR 5800), Laboratoire Bordelais de Recherche en Informatique, Centre National de la Recherche Scientifique, Talence, France
| | | | | | - Antoine Grigis
- CEA, Neurospin, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Indrit Bègue
- Laboratory for Clinical and Experimental Psychopathology, Department of Psychiatry, University of Geneva, Geneva, Switzerland
- University Hospital of Geneva, Geneva, Switzerland
- Laboratory of Applied Neuroscience, Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, USA
| | - Julie Victor
- CEA, Neurospin, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Vincent Frouin
- CEA, Neurospin, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Marion Leboyer
- Département Médico-Universitaire de Psychiatrie et d'Addictologie (DMU IMPACT), Fédération Hospitalo-Universitaire de Médecine de Précision en Psychiatrie (FHU ADAPT), AP-HP, Hôpitaux Universitaires Henri Mondor, 94010, Créteil, France
- Fondation FondaMental, 94010, Créteil, France
- Univ Paris Est Créteil, INSERM U955, IMRB, Translational Neuro-Psychiatry, 94010, Créteil, France
| | - Josselin Houenou
- Département Médico-Universitaire de Psychiatrie et d'Addictologie (DMU IMPACT), Fédération Hospitalo-Universitaire de Médecine de Précision en Psychiatrie (FHU ADAPT), AP-HP, Hôpitaux Universitaires Henri Mondor, 94010, Créteil, France
- Fondation FondaMental, 94010, Créteil, France
- CEA, Neurospin, Université Paris-Saclay, Gif-sur-Yvette, France
- Univ Paris Est Créteil, INSERM U955, IMRB, Translational Neuro-Psychiatry, 94010, Créteil, France
| | - Charles Laidi
- Département Médico-Universitaire de Psychiatrie et d'Addictologie (DMU IMPACT), Fédération Hospitalo-Universitaire de Médecine de Précision en Psychiatrie (FHU ADAPT), AP-HP, Hôpitaux Universitaires Henri Mondor, 94010, Créteil, France.
- Fondation FondaMental, 94010, Créteil, France.
- CEA, Neurospin, Université Paris-Saclay, Gif-sur-Yvette, France.
- Univ Paris Est Créteil, INSERM U955, IMRB, Translational Neuro-Psychiatry, 94010, Créteil, France.
- Child Mind Institute, Center for the Developing Brain, New York, NY, USA.
- Hôpital Albert Chenevier, 40 rue de Mesly, 94000, Créteil, France.
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7
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Fan YS, Xu Y, Li Q, Chen Y, Guo X, Yang S, Guo J, Sheng W, Wang C, Gao Q, Chen H. Systematically mapping gray matter abnormal patterns in drug-naïve first-episode schizophrenia from childhood to adolescence. Cereb Cortex 2023; 33:1452-1461. [PMID: 35396845 DOI: 10.1093/cercor/bhac148] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Revised: 03/23/2022] [Accepted: 03/24/2022] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Schizophrenia originates early in neurodevelopment, underscoring the need to elaborate on anomalies in the still maturing brain of early-onset schizophrenia (EOS). METHODS Gray matter (GM) volumes were evaluated in 94 antipsychotic-naïve first-episode EOS patients and 100 typically developing (TD) controls. The anatomical profiles of changing GM deficits in EOS were detected using 2-way analyses of variance with diagnosis and age as factors, and its timing was further charted using stage-specific group comparisons. Interregional relationships of GM alterations were established using structural covariance network analyses. RESULTS Antagonistic interaction results suggested dynamic GM abnormalities of the left fusiform gyrus, inferior occipital gyrus, and lingual gyrus in EOS. These regions comprise a dominating part of the ventral stream, a ventral occipitotemporal (vOT) network engaged in early social information processing. GM abnormalities were mainly located in the vOT regions in childhood-onset patients, whereas in the rostral prefrontal cortex (rPFC) in adolescent-onset patients. Moreover, compared with TD controls, patients' GM synchronization with the ventral stream was disrupted in widespread high-order social perception regions including the rPFC and salience network. CONCLUSIONS The current findings reveal age-related anatomical abnormalities of the social perception system in pediatric patients with schizophrenia.
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Affiliation(s)
- Yun-Shuang Fan
- Sichuan Provincial Center for Mental Health, The Center of Psychosomatic Medicine of Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yong Xu
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan 030000, China
| | - Qiang Li
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan 030000, China
| | - Yuyan Chen
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.,MOE Key Lab for Neuroinformation; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Xiaonan Guo
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
| | - Siqi Yang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.,MOE Key Lab for Neuroinformation; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Jing Guo
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.,MOE Key Lab for Neuroinformation; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Wei Sheng
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.,MOE Key Lab for Neuroinformation; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Chong Wang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.,MOE Key Lab for Neuroinformation; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Qing Gao
- MOE Key Lab for Neuroinformation; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Huafu Chen
- Sichuan Provincial Center for Mental Health, The Center of Psychosomatic Medicine of Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.,MOE Key Lab for Neuroinformation; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, China
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Lu F, Cui Q, Chen Y, He Z, Sheng W, Tang Q, Yang Y, Luo W, Yu Y, Chen J, Li D, Deng J, Zeng Y, Chen H. Insular-associated causal network of structural covariance evaluating progressive gray matter changes in major depressive disorder. Cereb Cortex 2023; 33:831-843. [PMID: 35357431 DOI: 10.1093/cercor/bhac105] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 01/17/2022] [Accepted: 02/15/2022] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Morphometric studies demonstrated wide-ranging distribution of brain structural abnormalities in major depressive disorder (MDD). OBJECTIVE This study explored the progressive gray matter volume (GMV) changes pattern of structural network in 108 MDD patients throughout the illness duration by using voxel-based morphometric analysis. METHODS The causal structural covariance network method was applied to map the causal effects of GMV alterations between the original source of structural changes and other brain regions as the illness duration prolonged in MDD. This was carried out by utilizing the Granger causality analysis to T1-weighted data ranked based on the disease progression information. RESULTS With greater illness duration, the GMV reduction was originated from the right insula and progressed to the frontal lobe, and then expanded to the occipital lobe, temporal lobe, dorsal striatum (putamen and caudate) and the cerebellum. Importantly, results revealed that the right insula was the prominent node projecting positive causal influences (i.e., GMV decrease) to frontal lobe, temporal lobe, postcentral gyrus, putamen, and precuneus. While opposite causal effects were detected from the right insula to the angular, parahippocampus, supramarginal gyrus and cerebellum. CONCLUSIONS This work may provide further information and vital evidence showing that MDD is associated with progressive brain structural alterations.
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Affiliation(s)
- Fengmei Lu
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Qian Cui
- School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Yuyan Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Zongling He
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Wei Sheng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Qin Tang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Yang Yang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Wei Luo
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Yue Yu
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Jiajia Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Di Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Jiaxin Deng
- School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Yuhong Zeng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, PR China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, PR China
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Simhal AK, Carpenter KLH, Kurtzberg J, Song A, Tannenbaum A, Zhang L, Sapiro G, Dawson G. Changes in the geometry and robustness of diffusion tensor imaging networks: Secondary analysis from a randomized controlled trial of young autistic children receiving an umbilical cord blood infusion. Front Psychiatry 2022; 13:1026279. [PMID: 36353577 PMCID: PMC9637553 DOI: 10.3389/fpsyt.2022.1026279] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 09/22/2022] [Indexed: 11/04/2022] Open
Abstract
Diffusion tensor imaging (DTI) has been used as an outcome measure in clinical trials for several psychiatric disorders but has rarely been explored in autism clinical trials. This is despite a large body of research suggesting altered white matter structure in autistic individuals. The current study is a secondary analysis of changes in white matter connectivity from a double-blind placebo-control trial of a single intravenous cord blood infusion in 2-7-year-old autistic children (1). Both clinical assessments and DTI were collected at baseline and 6 months after infusion. This study used two measures of white matter connectivity: change in node-to-node connectivity as measured through DTI streamlines and a novel measure of feedback network connectivity, Ollivier-Ricci curvature (ORC). ORC is a network measure which considers both local and global connectivity to assess the robustness of any given pathway. Using both the streamline and ORC analyses, we found reorganization of white matter pathways in predominantly frontal and temporal brain networks in autistic children who received umbilical cord blood treatment versus those who received a placebo. By looking at changes in network robustness, this study examined not only the direct, physical changes in connectivity, but changes with respect to the whole brain network. Together, these results suggest the use of DTI and ORC should be further explored as a potential biomarker in future autism clinical trials. These results, however, should not be interpreted as evidence for the efficacy of cord blood for improving clinical outcomes in autism. This paper presents a secondary analysis using data from a clinical trial that was prospectively registered with ClinicalTrials.gov(NCT02847182).
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Affiliation(s)
- Anish K. Simhal
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Kimberly L. H. Carpenter
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, United States
| | - Joanne Kurtzberg
- Marcus Center for Cellular Cures, Duke University Medical Center, Durham, NC, United States
| | - Allen Song
- Brain Imaging and Analysis Center, Duke University, Durham, NC, United States
| | - Allen Tannenbaum
- Department of Computer Science, Stony Brook University, Stony Brook, NY, United States
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, United States
| | - Lijia Zhang
- Brain Imaging and Analysis Center, Duke University, Durham, NC, United States
| | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, United States
- Department of Biomedical Engineering, Computer Science, and Mathematics, Duke University, Durham, NC, United States
| | - Geraldine Dawson
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, United States
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10
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Duan X, Chen H. Mapping brain functional and structural abnormities in autism spectrum disorder: moving toward precision treatment. PSYCHORADIOLOGY 2022; 2:78-85. [PMID: 38665600 PMCID: PMC10917159 DOI: 10.1093/psyrad/kkac013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 10/09/2022] [Accepted: 10/12/2022] [Indexed: 04/28/2024]
Abstract
Autism spectrum disorder (ASD) is a formidable challenge for psychiatry and neuroscience because of its high prevalence, lifelong nature, complexity, and substantial heterogeneity. A major goal of neuroimaging studies of ASD is to understand the neurobiological underpinnings of this disorder from multi-dimensional and multi-level perspectives, by investigating how brain anatomy, function, and connectivity are altered in ASD, and how they vary across the population. However, ongoing debate exists within those studies, and neuroimaging findings in ASD are often contradictory. Over the past decade, we have dedicated to delineate a comprehensive and consistent mapping of the abnormal structure and function of the autistic brain, and this review synthesizes the findings across our studies reaching a consensus that the "social brain" are the most affected regions in the autistic brain at different levels and modalities. We suggest that the social brain network can serve as a plausible biomarker and potential target for effective intervention in individuals with ASD.
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Affiliation(s)
- Xujun Duan
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, PR China
- MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, PR China
| | - Huafu Chen
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, PR China
- MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, PR China
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11
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Zhang J, Liu Y, Guo X, Guo J, Du Z, He M, Liu Q, Xu D, Liu T, Zhang J, Yuan H, Wang M, Li S. Causal Structural Covariance Network Suggesting Structural Alterations Progression in Type 2 Diabetes Patients. Front Hum Neurosci 2022; 16:936943. [PMID: 35911591 PMCID: PMC9336220 DOI: 10.3389/fnhum.2022.936943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 06/16/2022] [Indexed: 12/02/2022] Open
Abstract
Background and Purpose According to reports, type 2 diabetes (T2D) is a progressive disease. However, no known research has examined the progressive brain structural changes associated with T2D. The purpose of this study was to determine whether T2D patients exhibit progressive brain structural alterations and, if so, how the alterations progress. Materials and Methods Structural magnetic resonance imaging scans were collected for 81 T2D patients and 48 sex-and age-matched healthy controls (HCs). Voxel-based morphometry (VBM) and causal structural covariance network (CaSCN) analyses were applied to investigate gray matter volume (GMV) alterations and the likely chronological processes underlying them in T2D. Two sample t-tests were performed to compare group differences, and the differences were corrected using Gaussian random field (GRF) correction (voxel-level p < 0.001, cluster-level p < 0.01). Results Our findings demonstrated that GMV alterations progressed in T2D patients as disease duration increased. In the early stages of the disease, the right temporal pole of T2D patients had GMV atrophy. As the diseases duration prolonged, the limbic system, cerebellum, subcortical structures, parietal cortex, frontal cortex, and occipital cortex progressively exhibited GMV alterations. The patients also exhibited a GMV alterations sequence exerting from the right temporal pole to the limbic-cerebellum-striatal-cortical network areas. Conclusion Our results indicate that the progressive GMV alterations of T2D patients manifested a limbic-cerebellum-striatal-cortical sequence. These findings may contribute to a better understanding of the progression and an improvement of current diagnosis and intervention strategies for T2D.
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Affiliation(s)
- Jiang Zhang
- College of Electrical Engineering, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Yuyan Liu
- College of Electrical Engineering, Sichuan University, Chengdu, China
| | - Xiaonan Guo
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Jing Guo
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhengcong Du
- School of Information Science and Technology, Xichang University, Xichang, China
| | - Muyuan He
- College of Electrical Engineering, Sichuan University, Chengdu, China
| | - Qihong Liu
- College of Biomedical Engineering, Sichuan University, Chengdu, China
| | - Dundi Xu
- College of Electrical Engineering, Sichuan University, Chengdu, China
| | - Taiyuan Liu
- Department of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou, China
| | - Junran Zhang
- College of Electrical Engineering, Sichuan University, Chengdu, China
- *Correspondence: Junran Zhang
| | - Huijuan Yuan
- Department of Endocrinology, Henan Provincial People's Hospital, Zhengzhou, China
- Huijuan Yuan
| | - Meiyun Wang
- Department of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou, China
- Meiyun Wang
| | - Shasha Li
- The Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
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12
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Shan X, Uddin LQ, Xiao J, He C, Ling Z, Li L, Huang X, Chen H, Duan X. Mapping the Heterogeneous Brain Structural Phenotype of Autism Spectrum Disorder Using the Normative Model. Biol Psychiatry 2022; 91:967-976. [PMID: 35367047 DOI: 10.1016/j.biopsych.2022.01.011] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 12/28/2021] [Accepted: 01/14/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by substantial clinical and biological heterogeneity. Quantitative and individualized metrics for delineating the heterogeneity of brain structure in ASD are still lacking. Likewise, the extent to which brain structural metrics of ASD deviate from typical development (TD) and whether deviations can be used for parsing brain structural phenotypes of ASD is unclear. METHODS T1-weighted magnetic resonance imaging data from the Autism Brain Imaging Data Exchange (ABIDE) II (nTD = 564) were used to generate a normative model to map brain structure deviations of ABIDE I subjects (nTD = 560, nASD = 496). Voxel-based morphometry was used to compute gray matter volume. Non-negative matrix factorization was employed to decompose the gray matter matrix into 6 factors and weights. These weights were used for normative modeling to estimate the factor deviations. Then, clustering analysis was used to identify ASD subtypes. RESULTS Compared with TD, ASD showed increased weights and deviations in 5 factors. Three subtypes with distinct neuroanatomical deviation patterns were identified. ASD subtype 1 and subtype 3 showed positive deviations, whereas ASD subtype 2 showed negative deviations. Distinct clinical manifestations in social communication deficits were identified among the three subtypes. CONCLUSIONS Our findings suggest that individuals with ASD have heterogeneous deviation patterns in brain structure. The results highlight the need to test for subtypes in neuroimaging studies of ASD. This study also presents a framework for understanding neuroanatomical heterogeneity in this increasingly prevalent neurodevelopmental disorder.
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Affiliation(s)
- Xiaolong Shan
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Ministry of Education Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Lucina Q Uddin
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California
| | - Jinming Xiao
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Ministry of Education Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Changchun He
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Ministry of Education Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Zihan Ling
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Ministry of Education Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Lei Li
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Ministry of Education Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Xinyue Huang
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Ministry of Education Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Huafu Chen
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Ministry of Education Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Xujun Duan
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Ministry of Education Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China.
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13
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Li R, Zou T, Wang X, Wang H, Hu X, Xie F, Meng L, Chen H. Basal ganglia atrophy-associated causal structural network degeneration in Parkinson's disease. Hum Brain Mapp 2022; 43:1145-1156. [PMID: 34792836 PMCID: PMC8764481 DOI: 10.1002/hbm.25715] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 10/09/2021] [Accepted: 11/02/2021] [Indexed: 01/18/2023] Open
Abstract
Parkinson's disease (PD) is a progressive neurodegenerative disease characterized by both motor and non-motor symptoms. A convergent pathophysiological hallmark of PD is an early selective vulnerability within the basal ganglia circuit. However, the causal interactions between basal ganglia atrophy and progressive structural network alterations in PD remain unaddressed. Here, we adopted voxel-based morphometry method to measure gray matter (GM) volume for each participant (n = 84 PD patients and n = 70 matched healthy controls). Patients were first divided into three stages according to the Hoehn and Yahr (H&Y) and the Part III of Unified Parkinson's Disease Rating Scale scores respectively to analyze the stage-specific GM atrophy patterns. Then, the modulation of early caudate atrophy over other brain structures was evaluated using the whole-brain voxel-wise and region-of-interest-wise causal structural covariance network approaches. We found that GM atrophy progressively expands from the basal ganglia to the angular gyrus, temporal areas, and eventually spreads through the subcortical-cortical networks as PD progresses. Notably, we identified a shared caudate-associated degeneration network including the basal ganglia, thalamus, cerebellum, sensorimotor cortex, and cortical association areas with the PD progressive factors. These findings suggest that the early structural vulnerability of basal ganglia in PD may play a pivotal role in the modulation of motor and non-motor circuits at the structural level. Our work provides evidence for a novel mechanism of network degeneration that underlies the pathology of PD and may have potential clinical applications in the development of early predictors of PD onset and progress.
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Affiliation(s)
- Rong Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Ting Zou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Xuyang Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Hongyu Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Xiaofei Hu
- Department of Radiology, Southwest HospitalThird Military Medical University (Army Medical University)ChongqingChina
| | - Fangfang Xie
- Department of Radiology, Xiangya HospitalCentral South UniversityChangshaChina
| | - Li Meng
- Department of Radiology, Xiangya HospitalCentral South UniversityChangshaChina
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Department of Radiology, Southwest HospitalThird Military Medical University (Army Medical University)ChongqingChina
- Sichuan Provincial Center for Mental Health, The Center of Psychosomatic Medicine of Sichuan Provincial People's HospitalUniversity of Electronic Science and Technology of ChinaChengduChina
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