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Dwyer GE, Johnsen E, Hugdahl K. NMDAR dysfunction and the regulation of dopaminergic transmission in schizophrenia. Schizophr Res 2024; 271:19-27. [PMID: 39002526 DOI: 10.1016/j.schres.2024.07.025] [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] [Received: 11/27/2023] [Revised: 02/27/2024] [Accepted: 07/07/2024] [Indexed: 07/15/2024]
Abstract
A substantial body of evidence implicates dysfunction in N-methyl-d-aspartate receptors (NMDARs) in the pathophysiology of schizophrenia. This article illustrates how NMDAR dysfunction may give rise to many of the neurobiological phenomena frequently associated with schizophrenia with a particular focus on how NMDAR dysfunction affects the thalamic reticular nucleus (nRT) and pedunculopontine tegmental nucleus (PPTg). Furthermore, this article presents a model for schizophrenia illustrating how dysfunction in the nRT may interrupt prefrontal regulation of midbrain dopaminergic neurons, and how dysfunction in the PPTg may drive increased, irregular burst firing.
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Affiliation(s)
- Gerard Eric Dwyer
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway; NORMENT Centre of Excellence, Haukeland University Hospital, Bergen, Norway.
| | - Erik Johnsen
- NORMENT Centre of Excellence, Haukeland University Hospital, Bergen, Norway; Division of Psychiatry, Haukeland University Hospital, Bergen, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Kenneth Hugdahl
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway; Division of Psychiatry, Haukeland University Hospital, Bergen, Norway; Department of Radiology, Haukeland University Hospital, Bergen, Norway
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Ding Y, Hou W, Wang C, Sha S, Dong F, Li X, Wang N, Lam ST, Zhou F, Wang C. Longitudinal changes in cognitive function in early psychosis: a meta-analysis with the MATRICS consensus cognitive battery (MCCB). Schizophr Res 2024; 270:349-357. [PMID: 38968806 DOI: 10.1016/j.schres.2024.06.048] [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] [Received: 11/30/2023] [Revised: 05/14/2024] [Accepted: 06/25/2024] [Indexed: 07/07/2024]
Abstract
INTRODUCTION A previous meta-analysis indicated stable progress in cognitive functions in early psychosis, assessed through various tools. To avoid assessment-related heterogeneity, this study aims to examine the longitudinal cognitive function changes in early psychosis utilizing the MATRICS Consensus Cognitive Battery (MCCB). METHODS Embase, PubMed, and Scopus were systematically searched from their inception to September 26th 2023. The inclusion criteria were longitudinal studies that presented follow-up MCCB data for individuals experiencing first-episode psychosis (FEP) and those with ultra-high risk for psychosis (UHR). RESULTS Twelve studies with 791 participants (566 FEP patients and 225 healthy controls) were subjected to analysis. Suitable UHR studies were absent. Over time, both FEP patients and healthy controls showed significant improvements in MCCB total scores. Furthermore, FEP patients demonstrated improvements across all MCCB domains, while healthy controls only showed augmentations in specific domains such as speed of processing, attention, working memory, and reasoning and problem-solving. Visuospatial learning improvements were significantly greater in FEP patients compared to healthy controls. Subgroup analyses suggested that neither diagnostic type nor follow-up duration influenced the magnitude of cognitive improvement in FEP patients. CONCLUSION The magnitude of cognitive improvement for MCCB domains was not significantly different between FEP and healthy controls other than visuospatial learning. This underscores visuospatial learning as a potentially sensitive cognitive marker for early pathologic state changes in psychotic disorders.
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Affiliation(s)
- Yushen Ding
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| | - Wenpeng Hou
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| | - Chenxi Wang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| | - Sha Sha
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| | - Fang Dong
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| | - Xianbin Li
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| | - Nan Wang
- Institute of Mental Health, Buangkok Green Medical Park, 10 Buangkok View, Singapore 539747, Singapore.
| | - Sze Tung Lam
- Institute of Mental Health, Buangkok Green Medical Park, 10 Buangkok View, Singapore 539747, Singapore; Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, 12 Science Drive 2, Singapore 117549, Singapore.
| | - Fuchun Zhou
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| | - Chuanyue Wang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
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Yao Y, Zhang S, Wang B, Lin X, Zhao G, Deng H, Chen Y. Neural dysfunction underlying working memory processing at different stages of the illness course in schizophrenia: a comparative meta-analysis. Cereb Cortex 2024; 34:bhae267. [PMID: 38960703 DOI: 10.1093/cercor/bhae267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 06/04/2024] [Accepted: 06/06/2024] [Indexed: 07/05/2024] Open
Abstract
Schizophrenia, as a chronic and persistent disorder, exhibits working memory deficits across various stages of the disorder, yet the neural mechanisms underlying these deficits remain elusive with inconsistent neuroimaging findings. We aimed to compare the brain functional changes of working memory in patients at different stages: clinical high risk, first-episode psychosis, and long-term schizophrenia, using meta-analyses of functional magnetic resonance imaging studies. Following a systematic literature search, 56 whole-brain task-based functional magnetic resonance imaging studies (15 for clinical high risk, 16 for first-episode psychosis, and 25 for long-term schizophrenia) were included. The separate and pooled neurofunctional mechanisms among clinical high risk, first-episode psychosis, and long-term schizophrenia were generated by Seed-based d Mapping toolbox. The clinical high risk and first-episode psychosis groups exhibited overlapping hypoactivation in the right inferior parietal lobule, right middle frontal gyrus, and left superior parietal lobule, indicating key lesion sites in the early phase of schizophrenia. Individuals with first-episode psychosis showed lower activation in left inferior parietal lobule than those with long-term schizophrenia, reflecting a possible recovery process or more neural inefficiency. We concluded that SCZ represent as a continuum in the early stage of illness progression, while the neural bases are inversely changed with the development of illness course to long-term course.
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Affiliation(s)
- Yuhao Yao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Shufang Zhang
- Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
- West China School of Nursing, Sichuan University, Chengdu, China
| | - Boyao Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Xiaoyong Lin
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Gaofeng Zhao
- Department of Psychiatry, Shandong Daizhuang Hospital, Jinning, China
| | - Hong Deng
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Ying Chen
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
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Liu Y, Xiao X, Yang Y, Yao R, Yang Q, Zhu Y, Yang X, Zhang S, Shen L, Jiao B. The risk of Alzheimer's disease and cognitive impairment characteristics in eight mental disorders: A UK Biobank observational study and Mendelian randomization analysis. Alzheimers Dement 2024; 20:4841-4853. [PMID: 38860751 PMCID: PMC11247675 DOI: 10.1002/alz.14049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 05/14/2024] [Accepted: 05/14/2024] [Indexed: 06/12/2024]
Abstract
INTRODUCTION The cognitive impairment patterns and the association with Alzheimer's disease (AD) in mental disorders remain poorly understood. METHODS We analyzed data from 486,297 UK Biobank participants, categorizing them by mental disorder history to identify the risk of AD and the cognitive impairment characteristics. Causation was further assessed using Mendelian randomization (MR). RESULTS AD risk was higher in individuals with bipolar disorder (BD; hazard ratio [HR] = 2.37, P < 0.01) and major depressive disorder (MDD; HR = 1.63, P < 0.001). MR confirmed a causal link between BD and AD (ORIVW = 1.098), as well as obsessive-compulsive disorder (OCD) and AD (ORIVW = 1.050). Cognitive impairments varied, with BD and schizophrenia showing widespread deficits, and OCD affecting complex task performance. DISCUSSION Observational study and MR provide consistent evidence that mental disorders are independent risk factors for AD. Mental disorders exhibit distinct cognitive impairment prior to dementia, indicating the potential different mechanisms in AD pathogenesis. Early detection of these impairments in mental disorders is crucial for AD prevention. HIGHLIGHTS This is the most comprehensive study that investigates the risk and causal relationships between a history of mental disorders and the development of Alzheimer's disease (AD), alongside exploring the cognitive impairment characteristics associated with different mental disorders. Individuals with bipolar disorder (BD) exhibited the highest risk of developing AD (hazard ratio [HR] = 2.37, P < 0.01), followed by those with major depressive disorder (MDD; HR = 1.63, P < 0.001). Individuals with schizophrenia (SCZ) showed a borderline higher risk of AD (HR = 2.36, P = 0.056). Two-sample Mendelian randomization (MR) confirmed a causal association between BD and AD (ORIVW = 1.098, P < 0.05), as well as AD family history (proxy-AD, ORIVW = 1.098, P < 0.001), and kept significant after false discovery rate correction. MR also identified a nominal significant causal relationship between the obsessive-compulsive disorder (OCD) spectrum and AD (ORIVW = 1.050, P < 0.05). Individuals with SCZ, BD, and MDD exhibited impairments in multiple cognitive domains with distinct patterns, whereas those with OCD showed only slight declines in complex tasks.
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Affiliation(s)
- Yiliang Liu
- Department of NeurologyXiangya HospitalCentral South UniversityChangshaChina
| | - Xuewen Xiao
- Department of NeurologyXiangya HospitalCentral South UniversityChangshaChina
| | - Yang Yang
- Department of NeurologyXiangya HospitalCentral South UniversityChangshaChina
- National Clinical Research Center for Geriatric DisordersCentral South UniversityChangshaChina
- Engineering Research Center of Hunan Province in Cognitive Impairment DisordersCentral South UniversityChangshaChina
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic DiseasesXiangya HospitalChangshaChina
- Key Laboratory of Hunan Province in Neurodegenerative DisordersCentral South UniversityChangshaChina
| | - Rui Yao
- Department of NeurologyXiangya HospitalCentral South UniversityChangshaChina
| | - Qijie Yang
- Department of NeurologyXiangya HospitalCentral South UniversityChangshaChina
| | - Yuan Zhu
- Department of NeurologyXiangya HospitalCentral South UniversityChangshaChina
| | - Xuan Yang
- Department of NeurologyXiangya HospitalCentral South UniversityChangshaChina
| | - Sizhe Zhang
- Department of NeurologyXiangya HospitalCentral South UniversityChangshaChina
| | - Lu Shen
- Department of NeurologyXiangya HospitalCentral South UniversityChangshaChina
- National Clinical Research Center for Geriatric DisordersCentral South UniversityChangshaChina
- Engineering Research Center of Hunan Province in Cognitive Impairment DisordersCentral South UniversityChangshaChina
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic DiseasesXiangya HospitalChangshaChina
- Key Laboratory of Hunan Province in Neurodegenerative DisordersCentral South UniversityChangshaChina
| | - Bin Jiao
- Department of NeurologyXiangya HospitalCentral South UniversityChangshaChina
- National Clinical Research Center for Geriatric DisordersCentral South UniversityChangshaChina
- Engineering Research Center of Hunan Province in Cognitive Impairment DisordersCentral South UniversityChangshaChina
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic DiseasesXiangya HospitalChangshaChina
- Key Laboratory of Hunan Province in Neurodegenerative DisordersCentral South UniversityChangshaChina
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Sun H, Liu N, Qiu C, Tao B, Yang C, Tang B, Li H, Zhan K, Cai C, Zhang W, Lui S. Applications of MRI in Schizophrenia: Current Progress in Establishing Clinical Utility. J Magn Reson Imaging 2024. [PMID: 38946400 DOI: 10.1002/jmri.29470] [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: 05/20/2024] [Accepted: 05/20/2024] [Indexed: 07/02/2024] Open
Abstract
Schizophrenia is a severe mental illness that significantly impacts the lives of affected individuals and with increasing mortality rates. Early detection and intervention are crucial for improving outcomes but the lack of validated biomarkers poses great challenges in such efforts. The use of magnetic resonance imaging (MRI) in schizophrenia enables the investigation of the disorder's etiological and neuropathological substrates in vivo. After decades of research, promising findings of MRI have been shown to aid in screening high-risk individuals and predicting illness onset, and predicting symptoms and treatment outcomes of schizophrenia. The integration of machine learning and deep learning techniques makes it possible to develop intelligent diagnostic and prognostic tools with extracted or selected imaging features. In this review, we aimed to provide an overview of current progress and prospects in establishing clinical utility of MRI in schizophrenia. We first provided an overview of MRI findings of brain abnormalities that might underpin the symptoms or treatment response process in schizophrenia patients. Then, we summarized the ongoing efforts in the computer-aided utility of MRI in schizophrenia and discussed the gap between MRI research findings and real-world applications. Finally, promising pathways to promote clinical translation were provided. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Hui Sun
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Naici Liu
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Changjian Qiu
- Mental Health Center, West China Hospital of Sichuan University, Sichuan Clinical Medical Research Center for Mental Disorders, Chengdu, China
| | - Bo Tao
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Chengmin Yang
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Biqiu Tang
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Hongwei Li
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Department of Radiology, The Third Hospital of Mianyang/Sichuan Mental Health Center, Mianyang, China
| | - Kongcai Zhan
- Department of Radiology, Zigong Affiliated Hospital of Southwest Medical University, Zigong Psychiatric Research Center, Zigong, China
| | - Chunxian Cai
- Department of Radiology, the Second People's Hospital of Neijiang, Neijiang, China
| | - Wenjing Zhang
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Su Lui
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
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Oliver D, Chesney E, Cullen AE, Davies C, Englund A, Gifford G, Kerins S, Lalousis PA, Logeswaran Y, Merritt K, Zahid U, Crossley NA, McCutcheon RA, McGuire P, Fusar-Poli P. Exploring causal mechanisms of psychosis risk. Neurosci Biobehav Rev 2024; 162:105699. [PMID: 38710421 PMCID: PMC11250118 DOI: 10.1016/j.neubiorev.2024.105699] [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: 11/01/2023] [Revised: 02/17/2024] [Accepted: 04/28/2024] [Indexed: 05/08/2024]
Abstract
Robust epidemiological evidence of risk and protective factors for psychosis is essential to inform preventive interventions. Previous evidence syntheses have classified these risk and protective factors according to their strength of association with psychosis. In this critical review we appraise the distinct and overlapping mechanisms of 25 key environmental risk factors for psychosis, and link these to mechanistic pathways that may contribute to neurochemical alterations hypothesised to underlie psychotic symptoms. We then discuss the implications of our findings for future research, specifically considering interactions between factors, exploring universal and subgroup-specific factors, improving understanding of temporality and risk dynamics, standardising operationalisation and measurement of risk and protective factors, and developing preventive interventions targeting risk and protective factors.
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Affiliation(s)
- Dominic Oliver
- Department of Psychiatry, University of Oxford, Oxford, UK; NIHR Oxford Health Biomedical Research Centre, Oxford, UK; OPEN Early Detection Service, Oxford Health NHS Foundation Trust, Oxford, UK; Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
| | - Edward Chesney
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; Addictions Department, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 4 Windsor Walk, London SE5 8AF, UK
| | - Alexis E Cullen
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; Department of Clinical Neuroscience, Karolinska Institutet, Sweden
| | - Cathy Davies
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Amir Englund
- Addictions Department, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 4 Windsor Walk, London SE5 8AF, UK
| | - George Gifford
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Sarah Kerins
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Paris Alexandros Lalousis
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University Munich, Munich, Germany
| | - Yanakan Logeswaran
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; Department of Biostatistics & Health Informatics, King's College London, London, UK
| | - Kate Merritt
- Division of Psychiatry, Institute of Mental Health, UCL, London, UK
| | - Uzma Zahid
- Department of Psychology, King's College London, London, UK
| | - Nicolas A Crossley
- Department of Psychiatry, University of Oxford, Oxford, UK; Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Chile
| | - Robert A McCutcheon
- Department of Psychiatry, University of Oxford, Oxford, UK; Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; Oxford Health NHS Foundation Trust, Oxford, UK
| | - Philip McGuire
- Department of Psychiatry, University of Oxford, Oxford, UK; NIHR Oxford Health Biomedical Research Centre, Oxford, UK; OPEN Early Detection Service, Oxford Health NHS Foundation Trust, Oxford, UK
| | - Paolo Fusar-Poli
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University Munich, Munich, Germany; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy; OASIS Service, South London and Maudsley NHS Foundation Trust, London SE11 5DL, UK
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7
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Wang Y, Fan L, He Y, Yuan L, Li Z, Zheng W, Tang J, Li C, Jin K, Liu W, Chen X, Ouyang L, Ma X. Compensatory thickening of cortical thickness in early stage of schizophrenia. Cereb Cortex 2024; 34:bhae255. [PMID: 38897816 DOI: 10.1093/cercor/bhae255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 05/28/2024] [Accepted: 05/29/2024] [Indexed: 06/21/2024] Open
Abstract
Brain structural abnormality has been observed in the prodromal and early stages of schizophrenia, but the mechanism behind it is not clear. In this study, to explore the association between cortical abnormalities, metabolite levels, inflammation levels and clinical symptoms of schizophrenia, 51 drug-naive first-episode schizophrenia (FES) patients, 51 ultra-high risk for psychosis (UHR), and 51 healthy controls (HC) were recruited. We estimated gray matter volume (GMV), cortical thickness (CT), concentrations of different metabolites, and inflammatory marks among four groups (UHR converted to psychosis [UHR-C], UHR unconverted to psychosis [UHR-NC], FES, HC). UHR-C group had more CT in the right lateral occipital cortex and the right medial orbito-frontal cortex (rMOF), while a significant reduction in CT of the right fusiform cortex was observed in FES group. UHR-C group had significantly higher concentration of IL-6, while IL-17 could significantly predict CT of the right fusiform and IL-4 and IL-17 were significant predictors of CT in the rMOF. To conclude, it is reasonable to speculate that the increased CT in UHR-C group is related to the inflammatory response, and may participate in some compensatory mechanism, but might become exhaustive with the progress of the disease due to potential neurotoxic effects.
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Affiliation(s)
- Yujue Wang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Furong District No. 139 Renmin Road, Changsha 410011, Hunan, China
| | - Lejia Fan
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Furong District No. 139 Renmin Road, Changsha 410011, Hunan, China
- Department of Psychiatry, Douglas Mental Health University Institute, McGill University, 6875 Bd LaSalle, Verdun, Montreal, QC H4H 1R3, Canada
| | - Ying He
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Furong District No. 139 Renmin Road, Changsha 410011, Hunan, China
- China National Technology Institute on Mental Disorders, Furong District No. 139 Renmin Road, Changsha 410011, Hunan, China
- Hunan Key Laboratory of Psychiatry and Mental Health, Furong District No. 139 Renmin Road, Changsha 410011, Hunan, China
- Institute of Mental Health, Furong District No. 139 Renmin Road, Changsha 410011, Hunan, China
- Hunan Medical Center for Mental Health, Furong District No. 139 Renmin Road, Changsha 410011, Hunan, China
| | - Liu Yuan
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Furong District No. 139 Renmin Road, Changsha 410011, Hunan, China
| | - Zongchang Li
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Furong District No. 139 Renmin Road, Changsha 410011, Hunan, China
| | - Wenxiao Zheng
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Furong District No. 139 Renmin Road, Changsha 410011, Hunan, China
| | - Jinsong Tang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Furong District No. 139 Renmin Road, Changsha 410011, Hunan, China
| | - Chunwang Li
- Department of Radiology, Hunan Children's Hospital, Yuhua District catalpa garden road 86, Changsha 410007, Hunan, China
| | - Ke Jin
- Department of Radiology, Hunan Children's Hospital, Yuhua District catalpa garden road 86, Changsha 410007, Hunan, China
| | - Weiqing Liu
- Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, #165 Sanlin road, Pudong New Area,Shanghai 200124, China
- Laboratory for Molecular Mechanisms of Brain Development, Center for Brain Science (CBS), 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Xiaogang Chen
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Furong District No. 139 Renmin Road, Changsha 410011, Hunan, China
- China National Technology Institute on Mental Disorders, Furong District No. 139 Renmin Road, Changsha 410011, Hunan, China
- Hunan Key Laboratory of Psychiatry and Mental Health, Furong District No. 139 Renmin Road, Changsha 410011, Hunan, China
- Institute of Mental Health, Furong District No. 139 Renmin Road, Changsha 410011, Hunan, China
- Hunan Medical Center for Mental Health, Furong District No. 139 Renmin Road, Changsha 410011, Hunan, China
| | - Lijun Ouyang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Furong District No. 139 Renmin Road, Changsha 410011, Hunan, China
| | - Xiaoqian Ma
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Furong District No. 139 Renmin Road, Changsha 410011, Hunan, China
- China National Technology Institute on Mental Disorders, Furong District No. 139 Renmin Road, Changsha 410011, Hunan, China
- Hunan Key Laboratory of Psychiatry and Mental Health, Furong District No. 139 Renmin Road, Changsha 410011, Hunan, China
- Institute of Mental Health, Furong District No. 139 Renmin Road, Changsha 410011, Hunan, China
- Hunan Medical Center for Mental Health, Furong District No. 139 Renmin Road, Changsha 410011, Hunan, China
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8
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Hua JPY, Abram SV, Loewy RL, Stuart B, Fryer SL, Vinogradov S, Mathalon DH. Brain Age Gap in Early Illness Schizophrenia and the Clinical High-Risk Syndrome: Associations With Experiential Negative Symptoms and Conversion to Psychosis. Schizophr Bull 2024:sbae074. [PMID: 38815987 DOI: 10.1093/schbul/sbae074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/01/2024]
Abstract
BACKGROUND AND HYPOTHESIS Brain development/aging is not uniform across individuals,spawning efforts to characterize brain age from a biological perspective to model the effects of disease and maladaptive life processes on the brain. The brain age gap represents the discrepancy between estimated brain biological age and chronological age (in this case, based on structural magnetic resonance imaging, MRI). Structural MRI studies report an increased brain age gap (biological age > chronological age) in schizophrenia, with a greater brain age gap related to greater negative symptom severity. Less is known regarding the nature of this gap early in schizophrenia (ESZ), if this gap represents a psychosis conversion biomarker in clinical high-risk (CHR-P) individuals, and how altered brain development and/or agingmap onto specific symptom facets. STUDY DESIGN Using structural MRI, we compared the brain age gap among CHR-P (n = 51), ESZ (n = 78), and unaffected comparison participants (UCP; n = 90), and examined associations with CHR-P psychosis conversion (CHR-P converters n = 10; CHR-P non-converters; n = 23) and positive and negative symptoms. STUDY RESULTS ESZ showed a greater brain age gap relative to UCP and CHR-P (Ps < .010). CHR-P individuals who converted to psychosis showed a greater brain age gap (P = .043) relative to CHR-P non-converters. A larger brain age gap in ESZ was associated with increased experiential (P = .008), but not expressive negative symptom severity. CONCLUSIONS Consistent with schizophrenia pathophysiological models positing abnormal brain maturation, results suggest abnormal brain development is present early in psychosis. An increased brain age gap may be especially relevant to motivational and functional deficits in schizophrenia.
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Affiliation(s)
- Jessica P Y Hua
- Sierra Pacific Mental Illness Research Education and Clinical Centers, San Francisco VA Medical Center, University of California, San Francisco, CA, USA
- Mental Health Service, San Francisco VA Health Care System, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Samantha V Abram
- Mental Health Service, San Francisco VA Health Care System, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Rachel L Loewy
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Barbara Stuart
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Susanna L Fryer
- Mental Health Service, San Francisco VA Health Care System, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Sophia Vinogradov
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Daniel H Mathalon
- Mental Health Service, San Francisco VA Health Care System, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
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9
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Saglam Y, Ermis C, Takir S, Oz A, Hamid R, Kose H, Bas A, Karacetin G. The Contribution of Explainable Machine Learning Algorithms Using ROI-based Brain Surface Morphology Parameters in Distinguishing Early-onset Schizophrenia From Bipolar Disorder. Acad Radiol 2024:S1076-6332(24)00222-8. [PMID: 38704285 DOI: 10.1016/j.acra.2024.04.013] [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: 12/10/2023] [Revised: 02/25/2024] [Accepted: 04/11/2024] [Indexed: 05/06/2024]
Abstract
RATIONALE AND OBJECTIVES To differentiate early-onset schizophrenia (EOS) from early-onset bipolar disorder (EBD) using surface-based morphometry measurements and brain volumes using machine learning (ML) algorithms. METHOD High-resolution T1-weighted images were obtained to measure cortical thickness (CT), gyrification, gyrification index (GI), sulcal depth (SD), fractal dimension (FD), and brain volumes. After the feature selection step, ML classifiers were applied for each feature set and the combination of them. The SHapley Additive exPlanations (SHAP) technique was implemented to interpret the contribution of each feature. FINDINGS 144 adolescents (16.2 ± 1.4 years, female=39%) with EOS (n = 81) and EBD (n = 63) were included. The Adaptive Boosting (AdaBoost) algorithm had the highest accuracy (82.75%) in the whole dataset that includes all variables from Destrieux atlas. The best-performing algorithms were K-nearest neighbors (KNN) for FD subset, support vector machine (SVM) for SD subset, and AdaBoost for GI subset. The KNN algorithm had the highest accuracy (accuracy=79.31%) in the whole dataset from the Desikan-Killiany-Tourville atlas. CONCLUSION This study demonstrates the use of ML in the differential diagnosis of EOS and EBD using surface-based morphometry measurements. Future studies could focus on multicenter data for the validation of these results.
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Affiliation(s)
- Yesim Saglam
- Department of Child and Adolescent Psychiatry, University of Health Sciences, Bakirkoy Prof Dr Mazhar Osman Research and Training Hospital for Psychiatry, Neurology and Neurosurgery, Istanbul, Turkey.
| | - Cagatay Ermis
- Queen Silvia Children's Hospital, Department of Child Psychiatry, Gothenburg, Sweden
| | - Seyma Takir
- Department of Artificial Intelligence and Data Engineering, Istanbul Technical University, Istanbul, Turkey
| | - Ahmet Oz
- Department of Radiology, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Rauf Hamid
- Department of Radiology, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Hatice Kose
- Department of Artificial Intelligence and Data Engineering, Istanbul Technical University, Istanbul, Turkey
| | - Ahmet Bas
- Department of Radiology, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Gul Karacetin
- Department of Child and Adolescent Psychiatry, University of Health Sciences, Bakirkoy Prof Dr Mazhar Osman Research and Training Hospital for Psychiatry, Neurology and Neurosurgery, Istanbul, Turkey
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10
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Shi L, Liu X, Zhang S, Zhou A. Association of gut microbiota with cerebral cortical thickness: A Mendelian randomization study. J Affect Disord 2024; 352:312-320. [PMID: 38382814 DOI: 10.1016/j.jad.2024.02.063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 02/11/2024] [Accepted: 02/16/2024] [Indexed: 02/23/2024]
Abstract
BACKGROUND The causal relationship between gut microbiota and cerebral cortex development remains unclear. We aimed to scrutinize the plausible causal impact of gut microbiota on cortical thickness via Mendelian randomization (MR) study. METHODS Genome-wide association study (GWAS) data for 196 gut microbiota phenotypes (N = 18,340) were obtained as exposures, and GWAS data for cortical thickness-related traits (N = 51,665) were selected as outcomes. Inverse variance weighted was used as the main estimate method. A series of sensitivity analyses was used to test the robustness of the estimates including Cochran's Q test, MR-Egger intercept analysis, Steiger filtering, scatter plot funnel plot and leave-one-out analysis. RESULTS Genetic prediction of high Bacillales (β = 0.005, P = 0.032) and Lactobacillales (β = 0.010, P = 0.012) abundance was associated with a potential increase in global cortical thickness. For specific functional brain subdivisions, genetically predicted order Lactobacillales would potentially increase the thickness of the fusiform (β = 0.014, P = 0.016) and supramarginal (β = 0.017, P = 0.003). Meanwhile, order Bacillales would increase the thickness of fusiform (β = 0.007, P = 0.039), insula (β = 0.011, P = 0.003), rostralanteriorcingulate (β = 0.014, P = 0.002) and supramarginal (β = 0.006, P = 0.043). No significant estimates of heterogeneity or pleiotropy were found. CONCLUSIONS Through MR studies, we discovered genetic prediction of the Lactobacillales and Bacillales orders potentially linked to cortical thickness, affirming gut microbiota may enhance brain structure. Genetically predicted supramarginal and fusiform may be potential targets.
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Affiliation(s)
- Lubo Shi
- Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Diseases, Beijing Digestive Disease Center Beijing, China
| | - Xiaoduo Liu
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - Shutian Zhang
- Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Diseases, Beijing Digestive Disease Center Beijing, China.
| | - Anni Zhou
- Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Diseases, Beijing Digestive Disease Center Beijing, China.
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11
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Shah JL, Paquin V, McIlwaine SV, Malla AK, Joober R, Pruessner M. Examining the psychobiological response to acute social stress across clinical stages and symptom trajectories in the early psychosis continuum. Dev Psychopathol 2024; 36:774-786. [PMID: 36852607 DOI: 10.1017/s0954579423000056] [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] [Indexed: 03/01/2023]
Abstract
The stress-vulnerability model has been repeatedly highlighted in relation to the risk, onset and course of psychosis, and has been independently studied in clinical high-risk (CHR) and first-episode psychosis (FEP) populations. Notable in this literature, however, is that there are few studies directly comparing markers of stress response across progressive stages of illness. Here we examined the psychobiological response to the Trier Social Stress Test in 28 CHR (mean age 19.1) and 61 FEP (age 23.0) patients, in order to understand the stage(s) or trajectories in which differences in subjective stress or physiological response occur. The overall clinical sample had greater perceived stress and blunted cortisol (FEP + CHR, n = 89, age 21.7) compared with healthy controls (n = 45, age 22.9). Additional analyses demonstrated elevated heart rate and systolic blood pressure in FEP compared with CHR, but there were no further differences in physiological parameters (cortisol, heart rate, or blood pressure) between stage- or trajectory-based groups. Together, this suggests that individual stress response markers may differentially emerge at particular stages en route to psychosis - and demonstrates how stage-based analyses can shed light on the emergence and evolution of neurobiological changes in mental illness.
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Affiliation(s)
- Jai L Shah
- Department of Psychiatry, McGill University, Montreal, Canada
| | - Vincent Paquin
- Department of Psychiatry, McGill University, Montreal, Canada
| | - Sarah V McIlwaine
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada
| | - Ashok K Malla
- Department of Psychiatry, McGill University, Montreal, Canada
| | - Ridha Joober
- Department of Psychiatry, McGill University, Montreal, Canada
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12
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Zhu Y, Maikusa N, Radua J, Sämann PG, Fusar-Poli P, Agartz I, Andreassen OA, Bachman P, Baeza I, Chen X, Choi S, Corcoran CM, Ebdrup BH, Fortea A, Garani RR, Glenthøj BY, Glenthøj LB, Haas SS, Hamilton HK, Hayes RA, He Y, Heekeren K, Kasai K, Katagiri N, Kim M, Kristensen TD, Kwon JS, Lawrie SM, Lebedeva I, Lee J, Loewy RL, Mathalon DH, McGuire P, Mizrahi R, Mizuno M, Møller P, Nemoto T, Nordholm D, Omelchenko MA, Raghava JM, Røssberg JI, Rössler W, Salisbury DF, Sasabayashi D, Smigielski L, Sugranyes G, Takahashi T, Tamnes CK, Tang J, Theodoridou A, Tomyshev AS, Uhlhaas PJ, Værnes TG, van Amelsvoort TAMJ, Waltz JA, Westlye LT, Zhou JH, Thompson PM, Hernaus D, Jalbrzikowski M, Koike S. Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk. Mol Psychiatry 2024; 29:1465-1477. [PMID: 38332374 PMCID: PMC11189817 DOI: 10.1038/s41380-024-02426-7] [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] [Received: 08/16/2023] [Revised: 12/22/2023] [Accepted: 01/08/2024] [Indexed: 02/10/2024]
Abstract
Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psychosis later (CHR-PS+) from healthy controls (HCs) that can differentiate each other. We also evaluated whether we could distinguish CHR-PS+ individuals from those who did not develop psychosis later (CHR-PS-) and those with uncertain follow-up status (CHR-UNK). T1-weighted structural brain MRI scans from 1165 individuals at CHR (CHR-PS+, n = 144; CHR-PS-, n = 793; and CHR-UNK, n = 228), and 1029 HCs, were obtained from 21 sites. We used ComBat to harmonize measures of subcortical volume, cortical thickness and surface area data and corrected for non-linear effects of age and sex using a general additive model. CHR-PS+ (n = 120) and HC (n = 799) data from 20 sites served as a training dataset, which we used to build a classifier. The remaining samples were used external validation datasets to evaluate classifier performance (test, independent confirmatory, and independent group [CHR-PS- and CHR-UNK] datasets). The accuracy of the classifier on the training and independent confirmatory datasets was 85% and 73% respectively. Regional cortical surface area measures-including those from the right superior frontal, right superior temporal, and bilateral insular cortices strongly contributed to classifying CHR-PS+ from HC. CHR-PS- and CHR-UNK individuals were more likely to be classified as HC compared to CHR-PS+ (classification rate to HC: CHR-PS+, 30%; CHR-PS-, 73%; CHR-UNK, 80%). We used multisite sMRI to train a classifier to predict psychosis onset in CHR individuals, and it showed promise predicting CHR-PS+ in an independent sample. The results suggest that when considering adolescent brain development, baseline MRI scans for CHR individuals may be helpful to identify their prognosis. Future prospective studies are required about whether the classifier could be actually helpful in the clinical settings.
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Affiliation(s)
- Yinghan Zhu
- Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan
| | - Norihide Maikusa
- Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan
| | - Joaquim Radua
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), CIBERSAM, Instituto de Salud Carlos III, Universitat de Barcelona, Barcelona, Spain
| | | | - Paolo Fusar-Poli
- Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Ingrid Agartz
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
- KG Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- KG Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Peter Bachman
- Department of Psychiatry and Behavioral Sciences, Boston Children's Hospital, Boston, MA, USA
| | - Inmaculada Baeza
- Department of Child and Adolescent Psychiatry and Psychology, Institute of Neuroscience, 2017SGR-881, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Universitat de Barcelona, Barcelona, Spain
| | - Xiaogang Chen
- National Clinical Research Center for Mental Disorders and Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Sunah Choi
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, South Korea
| | - Cheryl M Corcoran
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Mental Illness Research, Education, and Clinical Center, James J Peters VA Medical Center, New York City, NY, USA
| | - Bjørn H Ebdrup
- Centre for Neuropsychiatric Schizophrenia Research (CNSR), Mental Health Centre Glostrup, Copenhagen University Hospital, Glostrup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Adriana Fortea
- Department of Child and Adolescent Psychiatry and Psychology, Institute of Neuroscience, Hospital Clinic Barcelona, Fundació Clínic Recerca Biomèdica, Universitat de Barcelona, Barcelona, Spain
| | - Ranjini Rg Garani
- Douglas Research Center; Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada
| | - Birte Yding Glenthøj
- Centre for Neuropsychiatric Schizophrenia Research (CNSR), Mental Health Centre Glostrup, Copenhagen University Hospital, Glostrup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Louise Birkedal Glenthøj
- Copenhagen Research Center for Mental Health, Mental Health Center Copenhagen, University of Copenhagen Copenhagen, Copenhagen, Denmark
| | - Shalaila S Haas
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Holly K Hamilton
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
- San Francisco Veterans Affairs Health Care System, San Francisco, CA, USA
| | - Rebecca A Hayes
- Department of Psychiatry and Behavioral Sciences, Boston Children's Hospital, Boston, MA, USA
| | - Ying He
- National Clinical Research Center for Mental Disorders and Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Karsten Heekeren
- Department of Psychiatry and Psychotherapy I, LVR-Hospital Cologne, Cologne, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Kiyoto Kasai
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- The University of Tokyo Institute for Diversity and Adaptation of Human Mind, The University of Tokyo, Tokyo, Japan
- The International Research Center for Neurointelligence at The University of Tokyo Institutes for Advanced Study (WPI-IRCN), The University of Tokyo, Tokyo, Japan
| | - Naoyuki Katagiri
- Department of Neuropsychiatry, Toho University School of Medicine, Tokyok, Japan
| | - Minah Kim
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea
| | - Tina D Kristensen
- Centre for Neuropsychiatric Schizophrenia Research (CNSR), Mental Health Centre Glostrup, Copenhagen University Hospital, Glostrup, Denmark
| | - Jun Soo Kwon
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, South Korea
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea
| | | | - Irina Lebedeva
- Laboratory of Neuroimaging and Multimodal Analysis, Mental Health Research Center, Moscow, Russian Federation
| | - Jimmy Lee
- Department of Psychosis, Institute of Mental Health, Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Rachel L Loewy
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Daniel H Mathalon
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
- San Francisco Veterans Affairs Health Care System, San Francisco, CA, USA
| | - Philip McGuire
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Romina Mizrahi
- Douglas Research Center; Department of Psychiatry, McGill University, Montreal, QC, Canada
| | | | - Paul Møller
- Department for Mental Health Research and Development, Division of Mental Health and Addiction, Vestre Viken Hospital Trust, Drammen, Norway
| | - Takahiro Nemoto
- Department of Neuropsychiatry, Toho University School of Medicine, Tokyok, Japan
| | - Dorte Nordholm
- Copenhagen Research Center for Mental Health, Mental Health Center Copenhagen, University of Copenhagen Copenhagen, Copenhagen, Denmark
| | - Maria A Omelchenko
- Department of Youth Psychiatry, Mental Health Research Center, Moscow, Russian Federation
| | - Jayachandra M Raghava
- Centre for Neuropsychiatric Schizophrenia Research (CNSR), Mental Health Centre Glostrup, Copenhagen University Hospital, Glostrup, Denmark
- Department of Clinical Physiology, Nuclear Medicine and PET, Functional Imaging, University of Copenhagen Copenhagen, Copenhagen, Denmark
| | - Jan I Røssberg
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Wulf Rössler
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Dean F Salisbury
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Daiki Sasabayashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan
- Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Lukasz Smigielski
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Child and Adolescent Psychiatry, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Gisela Sugranyes
- Department of Child and Adolescent Psychiatry and Psychology, Institute of Neuroscience, 2017SGR-881, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Universitat de Barcelona, Barcelona, Spain
| | - Tsutomu Takahashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan
- Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Christian K Tamnes
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Jinsong Tang
- Department of Psychiatry, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Zhejiang, China
- Key Laboratory of Medical Neurobiology of Zhejiang Province, School of Medicine, Zhejiang University, Zhejiang, China
| | - Anastasia Theodoridou
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Alexander S Tomyshev
- Laboratory of Neuroimaging and Multimodal Analysis, Mental Health Research Center, Moscow, Russian Federation
| | - Peter J Uhlhaas
- Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin Berlin, Berlin, Germany
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, UK
| | - Tor G Værnes
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Early Intervention in Psychosis Advisory Unit for South-East Norway, TIPS Sør-Øst, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Therese A M J van Amelsvoort
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Faculty of Health Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - James A Waltz
- Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore County, Baltimore, MD, USA
| | - Lars T Westlye
- KG Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Juan H Zhou
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Dennis Hernaus
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Faculty of Health Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Maria Jalbrzikowski
- Department of Psychiatry and Behavioral Sciences, Boston Children's Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Cambridge, MA, USA
| | - Shinsuke Koike
- Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan.
- The University of Tokyo Institute for Diversity and Adaptation of Human Mind, The University of Tokyo, Tokyo, Japan.
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13
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Wang Y, Yang Y, Xu W, Yao X, Xie X, Zhang L, Sun J, Wang L, Hua Q, He K, Tian Y, Wang K, Ji GJ. Heterogeneous Brain Abnormalities in Schizophrenia Converge on a Common Network Associated With Symptom Remission. Schizophr Bull 2024; 50:545-556. [PMID: 38253437 PMCID: PMC11059819 DOI: 10.1093/schbul/sbae003] [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: 01/24/2024]
Abstract
BACKGROUND AND HYPOTHESIS There is a huge heterogeneity of magnetic resonance imaging findings in schizophrenia studies. Here, we hypothesized that brain regions identified by structural and functional imaging studies of schizophrenia could be reconciled in a common network. STUDY DESIGN We systematically reviewed the case-control studies that estimated the brain morphology or resting-state local function for schizophrenia patients in the literature. Using the healthy human connectome (n = 652) and a validated technique "coordinate network mapping" to identify a common brain network affected in schizophrenia. Then, the specificity of this schizophrenia network was examined by independent data collected from 13 meta-analyses. The clinical relevance of this schizophrenia network was tested on independent data of medication, neuromodulation, and brain lesions. STUDY RESULTS We identified 83 morphological and 60 functional studies comprising 7389 patients with schizophrenia and 7408 control subjects. The "coordinate network mapping" showed that the atrophy and dysfunction coordinates were functionally connected to a common network although they were spatially distant from each other. Taking all 143 studies together, we identified the schizophrenia network with hub regions in the bilateral anterior cingulate cortex, insula, temporal lobe, and subcortical structures. Based on independent data from 13 meta-analyses, we showed that these hub regions were specifically connected with regions of cortical thickness changes in schizophrenia. More importantly, this schizophrenia network was remarkably aligned with regions involving psychotic symptom remission. CONCLUSIONS Neuroimaging abnormalities in cross-sectional schizophrenia studies converged into a common brain network that provided testable targets for developing precise therapies.
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Affiliation(s)
- Yingru Wang
- Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Yinian Yang
- Department of Clinical Psychiatry, School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
| | - Wenqiang Xu
- Department of Clinical Psychiatry, School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
| | - Xiaoqing Yao
- Department of Clinical Psychiatry, School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
| | - Xiaohui Xie
- Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Long Zhang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Jinmei Sun
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Lu Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Qiang Hua
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Kongliang He
- Department of Psychiatry, Fourth People’s Hospital of Hefei, Anhui Mental Health Center, Hefei, China
| | - Yanghua Tian
- Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Kai Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders,Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China
- Anhui Institute of Translational Medicine, Hefei, China
| | - Gong-Jun Ji
- Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
- Department of Clinical Psychiatry, School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders,Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China
- Anhui Institute of Translational Medicine, Hefei, China
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Li X, Wei W, Wang Q, Deng W, Li M, Ma X, Zeng J, Zhao L, Guo W, Hall MH, Li T. Identify Potential Causal Relationships Between Cortical Thickness, Mismatch Negativity, Neurocognition, and Psychosocial Functioning in Drug-Naïve First-Episode Psychosis Patients. Schizophr Bull 2024:sbae026. [PMID: 38635296 DOI: 10.1093/schbul/sbae026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
BACKGROUND Cortical thickness (CT) alterations, mismatch negativity (MMN) reductions, and cognitive deficits are robust findings in first-episode psychosis (FEP). However, most studies focused on medicated patients, leaving gaps in our understanding of the interrelationships between CT, MMN, neurocognition, and psychosocial functioning in unmedicated FEP. This study aimed to employ multiple mediation analysis to investigate potential pathways among these variables in unmedicated drug-naïve FEP. METHODS We enrolled 28 drug-naïve FEP and 34 age and sex-matched healthy controls. Clinical symptoms, neurocognition, psychosocial functioning, auditory duration MMN, and T1 structural magnetic resonance imaging data were collected. We measured CT in the superior temporal gyrus (STG), a primary MMN-generating region. RESULTS We found a significant negative correlation between MMN amplitude and bilateral CT of STG (CT_STG) in FEP (left: r = -.709, P < .001; right: r = -.612, P = .008). Multiple mediation models revealed that a thinner left STG cortex affected functioning through both direct (24.66%) and indirect effects (75.34%). In contrast, the effects of the right CT_STG on functioning were mainly mediated through MMN and neurocognitive pathways. CONCLUSIONS Bilateral CT_STG showed significant association with MMN, and MMN plays a mediating role between CT and cognition. Both MMN alone and its interaction with cognition mediated the effects of structural alterations on psychosocial function. The decline in overall function in FEP may stem from decreased CT_STG, leading to subsequent MMN deficits and neurocognitive dysfunction. These findings underline the crucial role of MMN in elucidating how subtle structural alterations can impact neurocognition and psychosocial function in FEP.
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Affiliation(s)
- Xiaojing Li
- Affiliated Mental Health Center and Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
- Nanhu Brain-Computer Interface Institute, Hangzhou 311100, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-Machine Integration, State Key Laboratory of Brain-Machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Wei Wei
- Affiliated Mental Health Center and Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
- Nanhu Brain-Computer Interface Institute, Hangzhou 311100, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-Machine Integration, State Key Laboratory of Brain-Machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Qiang Wang
- Mental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, China
| | - Wei Deng
- Affiliated Mental Health Center and Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
- Nanhu Brain-Computer Interface Institute, Hangzhou 311100, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-Machine Integration, State Key Laboratory of Brain-Machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Mingli Li
- Mental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, China
| | - Xiaohong Ma
- Mental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, China
| | - Jinkun Zeng
- Affiliated Mental Health Center and Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Liansheng Zhao
- Mental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, China
| | - Wanjun Guo
- Affiliated Mental Health Center and Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
- Nanhu Brain-Computer Interface Institute, Hangzhou 311100, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-Machine Integration, State Key Laboratory of Brain-Machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Mei-Hua Hall
- Psychosis Neurobiology Laboratory, McLean Hospital, Harvard Medical School, Belmont, MA, USA
| | - Tao Li
- Affiliated Mental Health Center and Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
- Nanhu Brain-Computer Interface Institute, Hangzhou 311100, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-Machine Integration, State Key Laboratory of Brain-Machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
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Yan Y, He X, Xu Y, Peng J, Zhao F, Shao Y. Comparison between morphometry and radiomics: detecting normal brain aging based on grey matter. Front Aging Neurosci 2024; 16:1366780. [PMID: 38685908 PMCID: PMC11056505 DOI: 10.3389/fnagi.2024.1366780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Accepted: 04/04/2024] [Indexed: 05/02/2024] Open
Abstract
Objective Voxel-based morphometry (VBM), surface-based morphometry (SBM), and radiomics are widely used in the field of neuroimage analysis, while it is still unclear that the performance comparison between traditional morphometry and emerging radiomics methods in diagnosing brain aging. In this study, we aimed to develop a VBM-SBM model and a radiomics model for brain aging based on cognitively normal (CN) individuals and compare their performance to explore both methods' strengths, weaknesses, and relationships. Methods 967 CN participants were included in this study. Subjects were classified into the middle-aged group (n = 302) and the old-aged group (n = 665) according to the age of 66. The data of 360 subjects from the Alzheimer's Disease Neuroimaging Initiative were used for training and internal test of the VBM-SBM and radiomics models, and the data of 607 subjects from the Australian Imaging, Biomarker and Lifestyle, the National Alzheimer's Coordinating Center, and the Parkinson's Progression Markers Initiative databases were used for the external tests. Logistics regression participated in the construction of both models. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were used to evaluate the two model performances. The DeLong test was used to compare the differences in AUCs between models. The Spearman correlation analysis was used to observe the correlations between age, VBM-SBM parameters, and radiomics features. Results The AUCs of the VBM-SBM model and radiomics model were 0.697 and 0.778 in the training set (p = 0.018), 0.640 and 0.789 in the internal test set (p = 0.007), 0.736 and 0.737 in the AIBL test set (p = 0.972), 0.746 and 0.838 in the NACC test set (p < 0.001), and 0.701 and 0.830 in the PPMI test set (p = 0.036). Weak correlations were observed between VBM-SBM parameters and radiomics features (p < 0.05). Conclusion The radiomics model achieved better performance than the VBM-SBM model. Radiomics provides a good option for researchers who prioritize performance and generalization, whereas VBM-SBM is more suitable for those who emphasize interpretability and clinical practice.
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Affiliation(s)
| | | | | | | | | | - Yuan Shao
- Center for Rehabilitation Medicine, Department of Radiology, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
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Cilia BJ, Eratne D, Wannan C, Malpas C, Janelidze S, Hansson O, Everall I, Bousman C, Thomas N, Santillo AF, Velakoulis D, Pantelis C. Associations between structural brain changes and blood neurofilament light chain protein in treatment-resistant schizophrenia. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.07.24305362. [PMID: 38645076 PMCID: PMC11030485 DOI: 10.1101/2024.04.07.24305362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Background and Hypothesis Around 30% of people with schizophrenia are refractory to antipsychotic treatment (treatment-resistant schizophrenia; TRS). While abnormal structural neuroimaging findings, in particular volume and thickness reductions, are often observed in schizophrenia, it is anticipated that biomarkers of neuronal injury like neurofilament light chain protein (NfL) can improve our understanding of the pathological basis underlying schizophrenia. The current study aimed to determine whether people with TRS demonstrate different associations between plasma NfL levels and regional cortical thickness reductions compared with controls. Study Design Measurements of plasma NfL and cortical thickness were obtained from 39 individuals with TRS, and 43 healthy controls. T1-weighted magnetic resonance imaging sequences were obtained and processed via FreeSurfer. General linear mixed models adjusting for age and weight were estimated to determine whether the interaction between diagnostic group and plasma NfL level predicted lower cortical thickness across frontotemporal structures and the insula. Study Results Significant (false discovery rate corrected) cortical thinning of the left ( p = 0.001, η 2p = 0.104) and right ( p < 0.001, η 2 = 0.167) insula was associated with higher levels of plasma NfL in TRS, but not in healthy controls. Conclusions The association between regional thickness reduction of the insula bilaterally and plasma NfL may reflect a neurodegenerative process during the course of TRS. The findings of the present study suggest that some level of cortical degeneration localised to the bilateral insula may exist in people with TRS, which is not observed in the normal population.
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Wang M, Wang Z, Zhao D, Yu Y, Wei F. Periodontitis causally affects the brain cortical structure: A Mendelian randomization study. J Periodontal Res 2024; 59:381-386. [PMID: 38059384 DOI: 10.1111/jre.13222] [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: 09/12/2023] [Revised: 11/15/2023] [Accepted: 11/28/2023] [Indexed: 12/08/2023]
Abstract
OBJECTIVE To estimate whether genetically proxied periodontitis causally impacts the brain cortical structure using Mendelian randomization (MR). BACKGROUND Periodontitis is one of the most prevalent inflammatory conditions globally, and emerging evidence has indicated its influences on distal organs, including the brain, whose disorders are always accompanied by magnetic resonance imaging (MRI)-identified brain cortical changes. However, to date, no available evidence has revealed the association between periodontitis and brain cortical structures. METHODS The instrumental variables (IVs) were adopted from previous genome-wide association study (GWAS) studies and meta-analyses of GWAS studies of periodontitis from 1844 to 5266 cases and 8255 to 12 515 controls. IVs were linked to GWAS summary data of 51 665 patients from the ENIGMA Consortium, assessing the impacts of genetically proxied periodontitis on the surficial area (SA) or the cortical thickness (TH) of the global and 34 MRI-identified functional regions of the brain. Inverse-variance weighted was used as the primary estimate; the MR pleiotropy residual sum and outlier (MR-PRESSO), the MR-Egger intercept test, and leave-one-out analyses were used to examine the potential horizontal pleiotropy. RESULTS Genetically proxied periodontitis affects the SA of the medial orbitofrontal cortex, the lateral orbitofrontal cortex, the inferior temporal cortex, the entorhinal cortex, and the temporal pole, as well as the TH of the entorhinal. No pleiotropy was detected. CONCLUSIONS Periodontitis causally influences the brain cortical structures, implying the existence of a periodontal tissue-brain axis.
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Affiliation(s)
- Mengqiao Wang
- Department of Orthodontics, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University & Shandong Key Laboratory of Oral Tissue Regeneration & Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration, Jinan, China
| | - Ziyao Wang
- Department of Orthodontics, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University & Shandong Key Laboratory of Oral Tissue Regeneration & Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration, Jinan, China
| | - Delu Zhao
- Department of Orthodontics, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University & Shandong Key Laboratory of Oral Tissue Regeneration & Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration, Jinan, China
| | - Yajie Yu
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital/Institute of Mental Health, The Key Laboratory of Mental Health, Ministry of Health, Peking University, Beijing, China
| | - Fulan Wei
- Department of Orthodontics, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University & Shandong Key Laboratory of Oral Tissue Regeneration & Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration, Jinan, China
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Yin Y, He S, He N, Zhang W, Luo L, Chen L, Liu T, Tian M, Xu J, Chen S, Li F. Brain alterations in sensorimotor and emotional regions associated with temporomandibular disorders. Oral Dis 2024; 30:1367-1378. [PMID: 36516329 DOI: 10.1111/odi.14466] [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: 07/02/2022] [Revised: 11/21/2022] [Accepted: 12/11/2022] [Indexed: 12/15/2022]
Abstract
OBJECTIVES Temporomandibular disorders (TMD) are characterized by sensorimotor and psychological dysfunction, with evidence revealing the implication of a dysfunctional central nervous system. Previous magnetic resonance imaging (MRI) studies have reported brain alterations in TMD, but most studies focused on either structure or function by a single modality of MRI and investigated static functional connectivity (FC) in TMD. By combining structural and functional MRI data, the present study aimed to identify brain regions with structural abnormalities in TMD patients and examine static and dynamic FC seeded by these regions to investigate structural brain alterations and related disrupted FC underlying the pathophysiology of TMD. METHODS We recruited 30 TMD patients and 20 healthy controls who underwent 3.0 T MRI scanning with T1-weighted images using a three-dimensional magnetization-prepared rapid gradient-echo sequence and resting state functional images using a gradient-echo echo-planar imaging sequence. Cortical thickness, volume, surface area, and subcortical volume were calculated, where brain areas with significant structural between-group differences were treated as seeds for static and dynamic FC analyses. RESULTS In this preliminary study, we found between-group alterations in sensorimotor regions including decreased cortical thickness in the right sensorimotor cortex as well as decreased volume in the left putamen and associated reduced dynamic FC with the anterior midcingulate cortex; and alterations in emotion processing and regulation regions including decreased volume/surface area in the left posterior superior temporal gyrus and associated increased dynamic FC with the precuneus in TMD patients than controls, having all p < 0.05 with corrections for multiple comparisons. CONCLUSION Our findings of structural and functional abnormalities in brain regions implicated in sensorimotor and emotional functions provided evidence for the biopsychosocial model of TMD and facilitated our understanding of the pathophysiological mechanism underlying TMD. The associations between neuroimaging results and clinical measurements of TMD warrant further exploration.
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Affiliation(s)
- Yuanyuan Yin
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Department of Orthodontics, State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
- Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Stomatological Hospital of Chongqing Medical University, Chongqing, China
| | - Shushu He
- Department of Orthodontics, State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Ning He
- Department of Psychiatry, West China Hospital of Sichuan University, Chengdu, China
| | - Wenjing Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Lekai Luo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Lizhou Chen
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Ting Liu
- Department of Orthodontics, State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Mi Tian
- Department of Orthodontics, State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Jingchen Xu
- Department of Orthodontics, State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Song Chen
- Department of Orthodontics, State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Fei Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
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Zhou Z, Gao Y, Bao W, Liang K, Cao L, Tang M, Li H, Hu X, Zhang L, Sun H, Roberts N, Gong Q, Huang X. Distinctive intrinsic functional connectivity alterations of anterior cingulate cortex subdivisions in major depressive disorder: A systematic review and meta-analysis. Neurosci Biobehav Rev 2024; 159:105583. [PMID: 38365137 DOI: 10.1016/j.neubiorev.2024.105583] [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: 09/30/2023] [Revised: 01/22/2024] [Accepted: 02/11/2024] [Indexed: 02/18/2024]
Abstract
Evidence of whether the intrinsic functional connectivity of anterior cingulate cortex (ACC) and its subregions is altered in major depressive disorder (MDD) remains inconclusive. A systematic review and meta-analysis were therefore performed on the whole-brain resting-state functional connectivity (rsFC) studies using the ACC and its subregions as seed regions in MDD, in order to draw more reliable conclusions. Forty-four ACC-based rsFC studies were included, comprising 25 subgenual ACC-based studies, 11 pregenual ACC-based studies, and 17 dorsal ACC-based studies. Specific alterations of rsFC were identified for each ACC subregion in patients with MDD, with altered rsFC of subgenual ACC in emotion-related brain regions, of pregenual ACC in sensorimotor-related regions, and of dorsal ACC in cognition-related regions. Furthermore, meta-regression analysis revealed a significant negative correlation between the pgACC-caudate hypoconnectivity and percentage of female patients in the study cohort. This meta-analysis provides robust evidence of altered intrinsic functional connectivity of the ACC subregions in MDD, which may hold relevance to understanding the origin of, and treating, the emotional, sensorimotor and cognitive dysfunctions that are often observed in these patients.
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Affiliation(s)
- Zilin Zhou
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Yingxue Gao
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Weijie Bao
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Kaili Liang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Lingxiao Cao
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Mengyue Tang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Hailong Li
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Xinyue Hu
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Lianqing Zhang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Huaiqiang Sun
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Science, Chengdu, China
| | - Neil Roberts
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China; Centre for Reproductive Health (CRH), School of Clinical Sciences, University of Edinburgh, Edinburgh, UK
| | - Qiyong Gong
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Science, Chengdu, China; The Xiaman Key Lab of psychoradiology and neuromodulation, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China
| | - Xiaoqi Huang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Science, Chengdu, China; The Xiaman Key Lab of psychoradiology and neuromodulation, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China.
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20
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Liu N, Sun H, Yang C, Li X, Gao Z, Gong Q, Zhang W, Lui S. The difference in volumetric alternations of the orbitofrontal-limbic-striatal system between major depressive disorder and anxiety disorders: A systematic review and voxel-based meta-analysis. J Affect Disord 2024; 350:65-77. [PMID: 38199394 DOI: 10.1016/j.jad.2024.01.043] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 12/12/2023] [Accepted: 01/03/2024] [Indexed: 01/12/2024]
Abstract
BACKGROUND Major depressive disorder (MDD) and anxiety disorders (ANX) are psychiatric disorders with high mutual comorbidity rates that might indicate some shared neurobiological pathways between them, but they retain diverse phenotypes that characterize themselves specifically. However, no consistent evidence exists for common and disorder-specific gray matter volume (GMV) alternations between them. METHODS A systematic review and meta-analysis on voxel-based morphometry studies of patients with MDD and ANX were performed. The effect of comorbidity was explicitly controlled during disorder-specific analysis and particularly investigated in patient with comorbidity. RESULTS A total of 45 studies with 54 datasets comprising 2196 patients and 2055 healthy participants met the inclusion criteria. Deficits in the orbitofrontal cortex, striatum, and limbic regions were found in MDD and ANX. The disorder-specific analyses showed decreased GMV in the bilateral anterior cingulate cortex, right striatum, hippocampus, and cerebellum in MDD, while decreased GMV in the left striatum, amygdala, insula, and increased cerebellar volume in ANX. A totally different GMV alternation pattern was shown involving bilateral temporal and parietal gyri and left fusiform gyrus in patients with comorbidity. LIMITATIONS Owing to the design of included studies, only partial patients in the comorbid group had a secondary comorbidity diagnosis. CONCLUSION Patients with MDD and ANX shared a structural disruption in the orbitofrontal-limbic-striatal system. The disorder-specific effects manifested their greatest severity in distinct lateralization and directionality of these changes that differentiate MDD from ANX. The comorbid group showed a totally different GMV alternation pattern, possibly suggesting another illness subtype that requires further investigation.
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Affiliation(s)
- Naici Liu
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Hui Sun
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Chengmin Yang
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Xing Li
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Ziyang Gao
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Qiyong Gong
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China; Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China
| | - Wenjing Zhang
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China.
| | - Su Lui
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China.
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González-Peñas J, Alloza C, Brouwer R, Díaz-Caneja CM, Costas J, González-Lois N, Gallego AG, de Hoyos L, Gurriarán X, Andreu-Bernabeu Á, Romero-García R, Fañanás L, Bobes J, González-Pinto A, Crespo-Facorro B, Martorell L, Arrojo M, Vilella E, Gutiérrez-Zotes A, Perez-Rando M, Moltó MD, Buimer E, van Haren N, Cahn W, O'Donovan M, Kahn RS, Arango C, Pol HH, Janssen J, Schnack H. Accelerated Cortical Thinning in Schizophrenia Is Associated With Rare and Common Predisposing Variation to Schizophrenia and Neurodevelopmental Disorders. Biol Psychiatry 2024:S0006-3223(24)01170-3. [PMID: 38521159 DOI: 10.1016/j.biopsych.2024.03.011] [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] [Received: 08/17/2023] [Revised: 02/22/2024] [Accepted: 03/05/2024] [Indexed: 03/25/2024]
Abstract
BACKGROUND Schizophrenia is a highly heritable disorder characterized by increased cortical thinning throughout the life span. Studies have reported a shared genetic basis between schizophrenia and cortical thickness. However, no genes whose expression is related to abnormal cortical thinning in schizophrenia have been identified. METHODS We conducted linear mixed models to estimate the rates of accelerated cortical thinning across 68 regions from the Desikan-Killiany atlas in individuals with schizophrenia compared with healthy control participants from a large longitudinal sample (ncases = 169 and ncontrols = 298, ages 16-70 years). We studied the correlation between gene expression data from the Allen Human Brain Atlas and accelerated thinning estimates across cortical regions. Finally, we explored the functional and genetic underpinnings of the genes that contribute most to accelerated thinning. RESULTS We found a global pattern of accelerated cortical thinning in individuals with schizophrenia compared with healthy control participants. Genes underexpressed in cortical regions that exhibit this accelerated thinning were downregulated in several psychiatric disorders and were enriched for both common and rare disrupting variation for schizophrenia and neurodevelopmental disorders. In contrast, none of these enrichments were observed for baseline cross-sectional cortical thickness differences. CONCLUSIONS Our findings suggest that accelerated cortical thinning, rather than cortical thickness alone, serves as an informative phenotype for neurodevelopmental disruptions in schizophrenia. We highlight the genetic and transcriptomic correlates of this accelerated cortical thinning, emphasizing the need for future longitudinal studies to elucidate the role of genetic variation and the temporal-spatial dynamics of gene expression in brain development and aging in schizophrenia.
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Affiliation(s)
- Javier González-Peñas
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain; CIBERSAM, Madrid, Spain.
| | - Clara Alloza
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain; CIBERSAM, Madrid, Spain
| | - Rachel Brouwer
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands; Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Neuroscience Campus, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Covadonga M Díaz-Caneja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain; CIBERSAM, Madrid, Spain; School of Medicine, Universidad Complutense, Madrid, Spain
| | - Javier Costas
- Instituto de Investigación Sanitària de Santiago de Compostela, Complexo Hospitalario Universitario de Santiago de Compostela, Servizo Galego de Saúde, Santiago de Compostela, Galicia, Spain
| | - Noemí González-Lois
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain
| | - Ana Guil Gallego
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain
| | - Lucía de Hoyos
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain
| | - Xaquín Gurriarán
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain; CIBERSAM, Madrid, Spain
| | - Álvaro Andreu-Bernabeu
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain; CIBERSAM, Madrid, Spain
| | - Rafael Romero-García
- Department of Medical Physiology and Biophysics, Instituto de Biomedicina de Sevilla, HUVR/CSIC/Universidad de Sevilla/CIBERSAM, Instituto de Salud Carlos III, Sevilla, Spain; Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Lourdes Fañanás
- CIBERSAM, Madrid, Spain; Department of Evolutionary Biology, Ecology and Environmental Sciences, Faculty of Biology, University of Barcelona, Barcelona, Spain
| | - Julio Bobes
- CIBERSAM, Madrid, Spain; Faculty of Medicine and Health Sciences-Psychiatry, Universidad de Oviedo, Instituto de Investigación Sanitaria del Principado de Asturias, Instituto de Neurociencias del Principado de Asturias, Oviedo, Spain
| | - Ana González-Pinto
- CIBERSAM, Madrid, Spain; BIOARABA Health Research Institute, Organización Sanitaria Integrada Araba, University Hospital, University of the Basque Country, Vitoria, Spain
| | - Benedicto Crespo-Facorro
- CIBERSAM, Madrid, Spain; Hospital Universitario Virgen del Rocío, Department of Psychiatry, Universidad de Sevilla, Sevilla, Spain
| | - Lourdes Martorell
- CIBERSAM, Madrid, Spain; Hospital Universitari Institut Pere Mata, Institut d'Investigació Sanitària Pere Virgili-Centres de Recerca de Catalunya, Universitat Rovira i Virgili, Reus, Spain
| | - Manuel Arrojo
- Instituto de Investigación Sanitària de Santiago de Compostela, Complexo Hospitalario Universitario de Santiago de Compostela, Servizo Galego de Saúde, Santiago de Compostela, Galicia, Spain
| | - Elisabet Vilella
- CIBERSAM, Madrid, Spain; Hospital Universitari Institut Pere Mata, Institut d'Investigació Sanitària Pere Virgili-Centres de Recerca de Catalunya, Universitat Rovira i Virgili, Reus, Spain
| | - Alfonso Gutiérrez-Zotes
- CIBERSAM, Madrid, Spain; Hospital Universitari Institut Pere Mata, Institut d'Investigació Sanitària Pere Virgili-Centres de Recerca de Catalunya, Universitat Rovira i Virgili, Reus, Spain
| | - Marta Perez-Rando
- Fundación Investigación Hospital Clínico de València, Fundación Investigación Hospital Clínico de Valencia, València, Spain; Unidad de Neurobiología, Instituto de Biotecnología y Biomedicina, Universitat de València, València, Spain
| | - María Dolores Moltó
- CIBERSAM, Madrid, Spain; Unidad de Neurobiología, Instituto de Biotecnología y Biomedicina, Universitat de València, València, Spain; Department of Genetics, Universitat de València, Campus of Burjassot, València, Spain
| | - Elizabeth Buimer
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Neeltje van Haren
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands; Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Centre, Rotterdam, the Netherlands
| | - Wiepke Cahn
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands; Altrecht Mental Health Institute, Altrecht Science, Utrecht, the Netherlands
| | - Michael O'Donovan
- Medical Research Council for Neuropsychiatric Genetics and Genomics and Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - René S Kahn
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain; CIBERSAM, Madrid, Spain; School of Medicine, Universidad Complutense, Madrid, Spain
| | - Hilleke Hulshoff Pol
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Joost Janssen
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain; CIBERSAM, Madrid, Spain; Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Hugo Schnack
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
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22
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Tsugawa S, Honda S, Noda Y, Wannan C, Zalesky A, Tarumi R, Iwata Y, Ogyu K, Plitman E, Ueno F, Mimura M, Uchida H, Chakravarty M, Graff-Guerrero A, Nakajima S. Associations Between Structural Covariance Network and Antipsychotic Treatment Response in Schizophrenia. Schizophr Bull 2024; 50:382-392. [PMID: 37978044 PMCID: PMC10919786 DOI: 10.1093/schbul/sbad160] [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: 11/19/2023]
Abstract
BACKGROUND AND HYPOTHESIS Schizophrenia is associated with widespread cortical thinning and abnormality in the structural covariance network, which may reflect connectome alterations due to treatment effect or disease progression. Notably, patients with treatment-resistant schizophrenia (TRS) have stronger and more widespread cortical thinning, but it remains unclear whether structural covariance is associated with treatment response in schizophrenia. STUDY DESIGN We organized a multicenter magnetic resonance imaging study to assess structural covariance in a large population of TRS and non-TRS, who had been resistant and responsive to non-clozapine antipsychotics, respectively. Whole-brain structural covariance for cortical thickness was assessed in 102 patients with TRS, 77 patients with non-TRS, and 79 healthy controls (HC). Network-based statistics were used to examine the difference in structural covariance networks among the 3 groups. Moreover, the relationship between altered individual differentiated structural covariance and clinico-demographics was also explored. STUDY RESULTS Patients with non-TRS exhibited greater structural covariance compared with HC, mainly in the fronto-temporal and fronto-occipital regions, while there were no significant differences in structural covariance between TRS and non-TRS or HC. Higher individual differentiated structural covariance was associated with lower general scores of the Positive and Negative Syndrome Scale in the non-TRS group, but not in the TRS group. CONCLUSIONS These findings suggest that reconfiguration of brain networks via coordinated cortical thinning is related to treatment response in schizophrenia. Further longitudinal studies are warranted to confirm if greater structural covariance could serve as a marker for treatment response in this disease.
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Affiliation(s)
- Sakiko Tsugawa
- Department of Neuropsychiatry, Keio University, Tokyo, Japan
| | - Shiori Honda
- Department of Neuropsychiatry, Keio University, Tokyo, Japan
| | - Yoshihiro Noda
- Department of Neuropsychiatry, Keio University, Tokyo, Japan
| | - Cassandra Wannan
- Department of Psychiatry, University of Melbourne, Melbourne, Australia
| | - Andrew Zalesky
- Department of Biomedical Engineering, Melbourne School of Engineering, the University of Melbourne, Melbourne, Australia
| | - Ryosuke Tarumi
- Department of Neuropsychiatry, Keio University, Tokyo, Japan
- Department of Psychiatry, Komagino Hospital, Tokyo, Japan
| | - Yusuke Iwata
- Department of Neuropsychiatry, University of Yamanashi, Yamanashi, Japan
| | - Kamiyu Ogyu
- Department of Neuropsychiatry, Keio University, Tokyo, Japan
- Department of Psychiatry, National Hospital Organization Shimofusa Psychiatric Medical Center, Chiba, Japan
| | - Eric Plitman
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Fumihiko Ueno
- Department of Neuropsychiatry, Keio University, Tokyo, Japan
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Masaru Mimura
- Department of Neuropsychiatry, Keio University, Tokyo, Japan
| | - Hiroyuki Uchida
- Department of Neuropsychiatry, Keio University, Tokyo, Japan
| | - Mallar Chakravarty
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, QC, Canada
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23
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Cao T, Pang JC, Segal A, Chen Y, Aquino KM, Breakspear M, Fornito A. Mode-based morphometry: A multiscale approach to mapping human neuroanatomy. Hum Brain Mapp 2024; 45:e26640. [PMID: 38445545 PMCID: PMC10915742 DOI: 10.1002/hbm.26640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 02/06/2024] [Accepted: 02/18/2024] [Indexed: 03/07/2024] Open
Abstract
Voxel-based morphometry (VBM) and surface-based morphometry (SBM) are two widely used neuroimaging techniques for investigating brain anatomy. These techniques rely on statistical inferences at individual points (voxels or vertices), clusters of points, or a priori regions-of-interest. They are powerful tools for describing brain anatomy, but offer little insights into the generative processes that shape a particular set of findings. Moreover, they are restricted to a single spatial resolution scale, precluding the opportunity to distinguish anatomical variations that are expressed across multiple scales. Drawing on concepts from classical physics, here we develop an approach, called mode-based morphometry (MBM), that can describe any empirical map of anatomical variations in terms of the fundamental, resonant modes-eigenmodes-of brain anatomy, each tied to a specific spatial scale. Hence, MBM naturally yields a multiscale characterization of the empirical map, affording new opportunities for investigating the spatial frequency content of neuroanatomical variability. Using simulated and empirical data, we show that the validity and reliability of MBM are either comparable or superior to classical vertex-based SBM for capturing differences in cortical thickness maps between two experimental groups. Our approach thus offers a robust, accurate, and informative method for characterizing empirical maps of neuroanatomical variability that can be directly linked to a generative physical process.
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Affiliation(s)
- Trang Cao
- The Turner Institute for Brain and Mental HealthSchool of Psychological Sciences, and Monash Biomedical Imaging, Monash UniversityClaytonVictoriaAustralia
| | - James C. Pang
- The Turner Institute for Brain and Mental HealthSchool of Psychological Sciences, and Monash Biomedical Imaging, Monash UniversityClaytonVictoriaAustralia
| | - Ashlea Segal
- The Turner Institute for Brain and Mental HealthSchool of Psychological Sciences, and Monash Biomedical Imaging, Monash UniversityClaytonVictoriaAustralia
| | - Yu‐Chi Chen
- The Turner Institute for Brain and Mental HealthSchool of Psychological Sciences, and Monash Biomedical Imaging, Monash UniversityClaytonVictoriaAustralia
| | - Kevin M. Aquino
- School of PhysicsUniversity of SydneyCamperdownNew South WalesAustralia
| | - Michael Breakspear
- School of Psychological SciencesUniversity of NewcastleCallaghanNew South WalesAustralia
| | - Alex Fornito
- The Turner Institute for Brain and Mental HealthSchool of Psychological Sciences, and Monash Biomedical Imaging, Monash UniversityClaytonVictoriaAustralia
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24
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Wang Y, Li Q, Yao L, He N, Tang Y, Chen L, Long F, Chen Y, Kemp GJ, Lui S, Li F. Shared and differing functional connectivity abnormalities of the default mode network in mild cognitive impairment and Alzheimer's disease. Cereb Cortex 2024; 34:bhae094. [PMID: 38521993 DOI: 10.1093/cercor/bhae094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 02/19/2024] [Accepted: 02/21/2024] [Indexed: 03/25/2024] Open
Abstract
Alzheimer's disease (AD) and mild cognitive impairment (MCI) both show abnormal resting-state functional connectivity (rsFC) of default mode network (DMN), but it is unclear to what extent these abnormalities are shared. Therefore, we performed a comprehensive meta-analysis, including 31 MCI studies and 20 AD studies. MCI patients, compared to controls, showed decreased within-DMN rsFC in bilateral medial prefrontal cortex/anterior cingulate cortex (mPFC/ACC), precuneus/posterior cingulate cortex (PCC), right temporal lobes, and left angular gyrus and increased rsFC between DMN and left inferior temporal gyrus. AD patients, compared to controls, showed decreased rsFC within DMN in bilateral mPFC/ACC and precuneus/PCC and between DMN and left inferior occipital gyrus and increased rsFC between DMN and right dorsolateral prefrontal cortex. Conjunction analysis showed shared decreased rsFC in mPFC/ACC and precuneus/PCC. Compared to MCI, AD had decreased rsFC in left precuneus/PCC and between DMN and left inferior occipital gyrus and increased rsFC in right temporal lobes. MCI and AD share a decreased within-DMN rsFC likely underpinning episodic memory deficits and neuropsychiatric symptoms, but differ in DMN rsFC alterations likely related to impairments in other cognitive domains such as language, vision, and execution. This may throw light on neuropathological mechanisms in these two stages of dementia.
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Affiliation(s)
- Yaxuan Wang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular lmaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wuhou District, Chengdu 610041, Sichuan Province, P.R. China
| | - Qian Li
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular lmaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wuhou District, Chengdu 610041, Sichuan Province, P.R. China
| | - Li Yao
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular lmaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wuhou District, Chengdu 610041, Sichuan Province, P.R. China
| | - Ning He
- Department of Psychiatry, West China Hospital of Sichuan University, No. 37 Guo Xue Alley, Wuhou District, Chengdu 610041, Sichuan, P.R. China
| | - Yingying Tang
- Department of Neurology, West China Hospital of Sichuan University, No. 37 Guo Xue Alley, Wuhou District, Chengdu 610041, Sichuan, P.R. China
| | - Lizhou Chen
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular lmaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wuhou District, Chengdu 610041, Sichuan Province, P.R. China
| | - Fenghua Long
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular lmaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wuhou District, Chengdu 610041, Sichuan Province, P.R. China
| | - Yufei Chen
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular lmaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wuhou District, Chengdu 610041, Sichuan Province, P.R. China
| | - Graham J Kemp
- Institute of Life Course and Medical Sciences, University of Liverpool, 6 West Derby Street, Liverpool L7 8TX, United Kingdom
| | - Su Lui
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular lmaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wuhou District, Chengdu 610041, Sichuan Province, P.R. China
| | - Fei Li
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular lmaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wuhou District, Chengdu 610041, Sichuan Province, P.R. China
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25
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Mehta UM, Ithal D, Roy N, Shekhar S, Govindaraj R, Ramachandraiah CT, Bolo NR, Bharath RD, Thirthalli J, Venkatasubramanian G, Gangadhar BN, Keshavan MS. Posterior Cerebellar Resting-State Functional Hypoconnectivity: A Neural Marker of Schizophrenia Across Different Stages of Treatment Response. Biol Psychiatry 2024:S0006-3223(24)00076-3. [PMID: 38336217 DOI: 10.1016/j.biopsych.2024.01.027] [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] [Received: 08/02/2023] [Revised: 01/11/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024]
Abstract
BACKGROUND Identifying stable and consistent resting-state functional connectivity patterns across illness trajectories has the potential to be considered fundamental to the pathophysiology of schizophrenia. We aimed to identify consistent resting-state functional connectivity patterns across heterogeneous schizophrenia groups defined based on treatment response. METHODS In phase 1, we used a cross-sectional case-control design to characterize and compare stable independent component networks from resting-state functional magnetic resonance imaging scans of antipsychotic-naïve participants with first-episode schizophrenia (n = 54) and healthy participants (n = 43); we also examined associations with symptoms, cognition, and disability. In phase 2, we examined the stability (and replicability) of our phase 1 results in 4 groups (N = 105) representing a cross-sequential gradation of schizophrenia based on treatment response: risperidone responders, clozapine responders, clozapine nonresponders, and clozapine nonresponders following electroconvulsive therapy. Hypothesis-free whole-brain within- and between-network connectivity were examined. RESULTS Phase 1 identified posterior and anterior cerebellar hypoconnectivity and limbic hyperconnectivity in schizophrenia at a familywise error rate-corrected cluster significance threshold of p < .01. These network aberrations had unique associations with positive symptoms, cognition, and disability. During phase 2, we replicated the phase 1 results while comparing each of the 4 schizophrenia groups to the healthy participants. The participants in 2 longitudinal subdatasets did not demonstrate a significant change in these network aberrations following risperidone or electroconvulsive therapy. Posterior cerebellar hypoconnectivity (with thalamus and cingulate) emerged as the most consistent finding; it was replicated across different stages of treatment response (Cohen's d range -0.95 to -1.44), reproduced using different preprocessing techniques, and not confounded by educational attainment. CONCLUSIONS Posterior cerebellar-thalamo-cingulate hypoconnectivity is a consistent and stable state-independent neural marker of schizophrenia.
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Affiliation(s)
- Urvakhsh Meherwan Mehta
- Department of Psychiatry, National Institute of Mental Health & Neurosciences, Bangalore, India.
| | - Dhruva Ithal
- Department of Psychiatry, National Institute of Mental Health & Neurosciences, Bangalore, India
| | - Neelabja Roy
- Department of Psychiatry, National Institute of Mental Health & Neurosciences, Bangalore, India
| | - Shreshth Shekhar
- Department of Psychiatry, National Institute of Mental Health & Neurosciences, Bangalore, India
| | - Ramajayam Govindaraj
- Department of Psychiatry, National Institute of Mental Health & Neurosciences, Bangalore, India
| | | | - Nicolas R Bolo
- Beth Israel Deaconess Medical Center and Massachusetts Mental Health Center, Harvard Medical School, Boston, Massachusetts
| | - Rose Dawn Bharath
- Department of Neuroimaging & Interventional Radiology, National Institute of Mental Health & Neurosciences, Bangalore, India
| | - Jagadisha Thirthalli
- Department of Psychiatry, National Institute of Mental Health & Neurosciences, Bangalore, India
| | | | - Bangalore N Gangadhar
- Department of Psychiatry, National Institute of Mental Health & Neurosciences, Bangalore, India
| | - Matcheri S Keshavan
- Beth Israel Deaconess Medical Center and Massachusetts Mental Health Center, Harvard Medical School, Boston, Massachusetts
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26
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Gaiser C, Berthet P, Kia SM, Frens MA, Beckmann CF, Muetzel RL, Marquand AF. Estimating cortical thickness trajectories in children across different scanners using transfer learning from normative models. Hum Brain Mapp 2024; 45:e26565. [PMID: 38339954 PMCID: PMC10839740 DOI: 10.1002/hbm.26565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 10/28/2023] [Accepted: 11/30/2023] [Indexed: 02/12/2024] Open
Abstract
This work illustrates the use of normative models in a longitudinal neuroimaging study of children aged 6-17 years and demonstrates how such models can be used to make meaningful comparisons in longitudinal studies, even when individuals are scanned with different scanners across successive study waves. More specifically, we first estimated a large-scale reference normative model using Hierarchical Bayesian Regression from N = 42,993 individuals across the lifespan and from dozens of sites. We then transfer these models to a longitudinal developmental cohort (N = 6285) with three measurement waves acquired on two different scanners that were unseen during estimation of the reference models. We show that the use of normative models provides individual deviation scores that are independent of scanner effects and efficiently accommodate inter-site variations. Moreover, we provide empirical evidence to guide the optimization of sample size for the transfer of prior knowledge about the distribution of regional cortical thicknesses. We show that a transfer set containing as few as 25 samples per site can lead to good performance metrics on the test set. Finally, we demonstrate the clinical utility of this approach by showing that deviation scores obtained from the transferred normative models are able to detect and chart morphological heterogeneity in individuals born preterm.
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Affiliation(s)
- C. Gaiser
- Department of Neuroscience, Erasmus MCUniversity Medical Centre RotterdamRotterdamThe Netherlands
- The Generation R Study Group, Erasmus MC—Sophia Children's HospitalUniversity Medical Centre RotterdamRotterdamThe Netherlands
| | - P. Berthet
- Department of PsychologyUniversity of OsloOsloNorway
- Norwegian Center for Mental Disorders Research (NORMENT)University of Oslo, and Oslo University HospitalOsloNorway
| | - S. M. Kia
- Donders Institute for Brain, Cognition, and BehaviorRadboud UniversityNijmegenThe Netherlands
- Department of PsychiatryUtrecht University Medical CenterUtrechtThe Netherlands
- Department of Cognitive Science and Artificial IntelligenceTilburg UniversityTilburgThe Netherlands
| | - M. A. Frens
- Department of Neuroscience, Erasmus MCUniversity Medical Centre RotterdamRotterdamThe Netherlands
| | - C. F. Beckmann
- Donders Institute for Brain, Cognition, and BehaviorRadboud UniversityNijmegenThe Netherlands
- Department of Cognitive NeuroscienceRadboud University Medical CenterNijmegenThe Netherlands
- Centre for Functional MRI of the BrainUniversity of OxfordOxfordUK
| | - R. L. Muetzel
- Department of Child and Adolescent Psychiatry, Erasmus MC—Sophia Children's HospitalUniversity Medical Centre RotterdamRotterdamThe Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC—Sophia Children's HospitalUniversity Medical Centre RotterdamRotterdamThe Netherlands
| | - Andre F. Marquand
- Donders Institute for Brain, Cognition, and BehaviorRadboud UniversityNijmegenThe Netherlands
- Department of Cognitive NeuroscienceRadboud University Medical CenterNijmegenThe Netherlands
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27
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Tandon R, Nasrallah H, Akbarian S, Carpenter WT, DeLisi LE, Gaebel W, Green MF, Gur RE, Heckers S, Kane JM, Malaspina D, Meyer-Lindenberg A, Murray R, Owen M, Smoller JW, Yassin W, Keshavan M. The schizophrenia syndrome, circa 2024: What we know and how that informs its nature. Schizophr Res 2024; 264:1-28. [PMID: 38086109 DOI: 10.1016/j.schres.2023.11.015] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 11/23/2023] [Accepted: 11/29/2023] [Indexed: 03/01/2024]
Abstract
With new data about different aspects of schizophrenia being continually generated, it becomes necessary to periodically revisit exactly what we know. Along with a need to review what we currently know about schizophrenia, there is an equal imperative to evaluate the construct itself. With these objectives, we undertook an iterative, multi-phase process involving fifty international experts in the field, with each step building on learnings from the prior one. This review assembles currently established findings about schizophrenia (construct, etiology, pathophysiology, clinical expression, treatment) and posits what they reveal about its nature. Schizophrenia is a heritable, complex, multi-dimensional syndrome with varying degrees of psychotic, negative, cognitive, mood, and motor manifestations. The illness exhibits a remitting and relapsing course, with varying degrees of recovery among affected individuals with most experiencing significant social and functional impairment. Genetic risk factors likely include thousands of common genetic variants that each have a small impact on an individual's risk and a plethora of rare gene variants that have a larger individual impact on risk. Their biological effects are concentrated in the brain and many of the same variants also increase the risk of other psychiatric disorders such as bipolar disorder, autism, and other neurodevelopmental conditions. Environmental risk factors include but are not limited to urban residence in childhood, migration, older paternal age at birth, cannabis use, childhood trauma, antenatal maternal infection, and perinatal hypoxia. Structural, functional, and neurochemical brain alterations implicate multiple regions and functional circuits. Dopamine D-2 receptor antagonists and partial agonists improve psychotic symptoms and reduce risk of relapse. Certain psychological and psychosocial interventions are beneficial. Early intervention can reduce treatment delay and improve outcomes. Schizophrenia is increasingly considered to be a heterogeneous syndrome and not a singular disease entity. There is no necessary or sufficient etiology, pathology, set of clinical features, or treatment that fully circumscribes this syndrome. A single, common pathophysiological pathway appears unlikely. The boundaries of schizophrenia remain fuzzy, suggesting the absence of a categorical fit and need to reconceptualize it as a broader, multi-dimensional and/or spectrum construct.
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Affiliation(s)
- Rajiv Tandon
- Department of Psychiatry, WMU Homer Stryker School of Medicine, Kalamazoo, MI 49008, United States of America.
| | - Henry Nasrallah
- Department of Psychiatry, University of Cincinnati College of Medicine Cincinnati, OH 45267, United States of America
| | - Schahram Akbarian
- Department of Psychiatry, Icahn School of Medicine at Mt. Sinai, New York, NY 10029, United States of America
| | - William T Carpenter
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD 21201, United States of America
| | - Lynn E DeLisi
- Department of Psychiatry, Cambridge Health Alliance and Harvard Medical School, Cambridge, MA 02139, United States of America
| | - Wolfgang Gaebel
- Department of Psychiatry and Psychotherapy, LVR-Klinikum Dusseldorf, Heinrich-Heine University, Dusseldorf, Germany
| | - Michael F Green
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute of Neuroscience and Human Behavior, UCLA, Los Angeles, CA 90024, United States of America; Greater Los Angeles Veterans' Administration Healthcare System, United States of America
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States of America
| | - Stephan Heckers
- Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN 37232, United States of America
| | - John M Kane
- Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Glen Oaks, NY 11004, United States of America
| | - Dolores Malaspina
- Department of Psychiatry, Neuroscience, Genetics, and Genomics, Icahn School of Medicine at Mt. Sinai, New York, NY 10029, United States of America
| | - Andreas Meyer-Lindenberg
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannhein/Heidelberg University, Mannheim, Germany
| | - Robin Murray
- Institute of Psychiatry, Psychology, and Neuroscience, Kings College, London, UK
| | - Michael Owen
- Centre for Neuropsychiatric Genetics and Genomics, and Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Jordan W Smoller
- Center for Precision Psychiatry, Department of Psychiatry, Psychiatric and Neurodevelopmental Unit, Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, United States of America
| | - Walid Yassin
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02115, United States of America
| | - Matcheri Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02115, United States of America
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Neher P, Hirjak D, Maier-Hein K. Radiomic tractometry reveals tract-specific imaging biomarkers in white matter. Nat Commun 2024; 15:303. [PMID: 38182594 PMCID: PMC10770385 DOI: 10.1038/s41467-023-44591-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 12/21/2023] [Indexed: 01/07/2024] Open
Abstract
Tract-specific microstructural analysis of the brain's white matter (WM) using diffusion MRI has been a driver for neuroscientific discovery with a wide range of applications. Tractometry enables localized tissue analysis along tracts but relies on bare summary statistics and reduces complex image information along a tract to few scalar values, and so may miss valuable information. This hampers the applicability of tractometry for predictive modelling. Radiomics is a promising method based on the analysis of numerous quantitative image features beyond what can be visually perceived, but has not yet been used for tract-specific analysis of white matter. Here we introduce radiomic tractometry (RadTract) and show that introducing rich radiomics-based feature sets into the world of tractometry enables improved predictive modelling while retaining the localization capability of tractometry. We demonstrate its value in a series of clinical populations, showcasing its performance in diagnosing disease subgroups in different datasets, as well as estimation of demographic and clinical parameters. We propose that RadTract could spark the establishment of a new generation of tract-specific imaging biomarkers with benefits for a range of applications from basic neuroscience to medical research.
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Affiliation(s)
- Peter Neher
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Im Neuenheimer Feld 223, 69120, Heidelberg, Germany.
- German Cancer Consortium (DKTK), partner site Heidelberg, Heidelberg, Germany.
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany.
| | - Dusan Hirjak
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, J5, 68159, Mannheim, Germany
| | - Klaus Maier-Hein
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Im Neuenheimer Feld 223, 69120, Heidelberg, Germany
- German Cancer Consortium (DKTK), partner site Heidelberg, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and the university medical center Heidelberg, Heidelberg, Germany
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29
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Haas SS, Ge R, Agartz I, Amminger GP, Andreassen OA, Bachman P, Baeza I, Choi S, Colibazzi T, Cropley VL, de la Fuente-Sandoval C, Ebdrup BH, Fortea A, Fusar-Poli P, Glenthøj BY, Glenthøj LB, Haut KM, Hayes RA, Heekeren K, Hooker CI, Hwang WJ, Jahanshad N, Kaess M, Kasai K, Katagiri N, Kim M, Kindler J, Koike S, Kristensen TD, Kwon JS, Lawrie SM, Lebedeva I, Lee J, Lemmers-Jansen ILJ, Lin A, Ma X, Mathalon DH, McGuire P, Michel C, Mizrahi R, Mizuno M, Møller P, Mora-Durán R, Nelson B, Nemoto T, Nordentoft M, Nordholm D, Omelchenko MA, Pantelis C, Pariente JC, Raghava JM, Reyes-Madrigal F, Røssberg JI, Rössler W, Salisbury DF, Sasabayashi D, Schall U, Smigielski L, Sugranyes G, Suzuki M, Takahashi T, Tamnes CK, Theodoridou A, Thomopoulos SI, Thompson PM, Tomyshev AS, Uhlhaas PJ, Værnes TG, van Amelsvoort TAMJ, van Erp TGM, Waltz JA, Wenneberg C, Westlye LT, Wood SJ, Zhou JH, Hernaus D, Jalbrzikowski M, Kahn RS, Corcoran CM, Frangou S. Normative Modeling of Brain Morphometry in Clinical High Risk for Psychosis. JAMA Psychiatry 2024; 81:77-88. [PMID: 37819650 PMCID: PMC10568447 DOI: 10.1001/jamapsychiatry.2023.3850] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 07/25/2023] [Indexed: 10/13/2023]
Abstract
Importance The lack of robust neuroanatomical markers of psychosis risk has been traditionally attributed to heterogeneity. A complementary hypothesis is that variation in neuroanatomical measures in individuals at psychosis risk may be nested within the range observed in healthy individuals. Objective To quantify deviations from the normative range of neuroanatomical variation in individuals at clinical high risk for psychosis (CHR-P) and evaluate their overlap with healthy variation and their association with positive symptoms, cognition, and conversion to a psychotic disorder. Design, Setting, and Participants This case-control study used clinical-, IQ-, and neuroimaging software (FreeSurfer)-derived regional measures of cortical thickness (CT), cortical surface area (SA), and subcortical volume (SV) from 1340 individuals with CHR-P and 1237 healthy individuals pooled from 29 international sites participating in the Enhancing Neuroimaging Genetics Through Meta-analysis (ENIGMA) Clinical High Risk for Psychosis Working Group. Healthy individuals and individuals with CHR-P were matched on age and sex within each recruitment site. Data were analyzed between September 1, 2021, and November 30, 2022. Main Outcomes and Measures For each regional morphometric measure, deviation scores were computed as z scores indexing the degree of deviation from their normative means from a healthy reference population. Average deviation scores (ADS) were also calculated for regional CT, SA, and SV measures and globally across all measures. Regression analyses quantified the association of deviation scores with clinical severity and cognition, and 2-proportion z tests identified case-control differences in the proportion of individuals with infranormal (z < -1.96) or supranormal (z > 1.96) scores. Results Among 1340 individuals with CHR-P, 709 (52.91%) were male, and the mean (SD) age was 20.75 (4.74) years. Among 1237 healthy individuals, 684 (55.30%) were male, and the mean (SD) age was 22.32 (4.95) years. Individuals with CHR-P and healthy individuals overlapped in the distributions of the observed values, regional z scores, and all ADS values. For any given region, the proportion of individuals with CHR-P who had infranormal or supranormal values was low (up to 153 individuals [<11.42%]) and similar to that of healthy individuals (<115 individuals [<9.30%]). Individuals with CHR-P who converted to a psychotic disorder had a higher percentage of infranormal values in temporal regions compared with those who did not convert (7.01% vs 1.38%) and healthy individuals (5.10% vs 0.89%). In the CHR-P group, only the ADS SA was associated with positive symptoms (β = -0.08; 95% CI, -0.13 to -0.02; P = .02 for false discovery rate) and IQ (β = 0.09; 95% CI, 0.02-0.15; P = .02 for false discovery rate). Conclusions and Relevance In this case-control study, findings suggest that macroscale neuromorphometric measures may not provide an adequate explanation of psychosis risk.
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Affiliation(s)
- Shalaila S Haas
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Ruiyang Ge
- Djavad Mowafaghian Centre for Brain Health, Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Ingrid Agartz
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
- KG Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - G Paul Amminger
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
- Orygen, Melbourne, Victoria, Australia
| | - Ole A Andreassen
- KG Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Peter Bachman
- Department of Psychiatry and Behavioral Sciences, Boston Children's Hospital, Boston, Massachusetts
| | - Inmaculada Baeza
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM)-ISCIII, Madrid Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (FCRB-IDIBAPS), Barcelona, Spain
- Child and Adolescent Psychiatry and Psychology Department, 2021SGR01319, Institute of Neuroscience, Hospital Clínic de Barcelona, Barcelona, Spain
- Department of Medicine, University of Barcelona, Barcelona, Spain
| | - Sunah Choi
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Tiziano Colibazzi
- Department of Psychiatry, Columbia University, New York, New York
- New York State Psychiatric Institute, New York
| | - Vanessa L Cropley
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Western Health, Carlton South, VIC, Australia
| | | | - Bjørn H Ebdrup
- Centre for Neuropsychiatric Schizophrenia Research and Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup, University of Copenhagen, Glostrup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Adriana Fortea
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM)-ISCIII, Madrid Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (FCRB-IDIBAPS), Barcelona, Spain
- Department of Medicine, University of Barcelona, Barcelona, Spain
- Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Barcelona, Spain
- Fundació Clínic Recerca Biomèdica, Universitat de Barcelona, Barcelona, Spain
| | - Paolo Fusar-Poli
- Department of Psychosis Studies, Early Psychosis: Interventions and Clinical-Detection Lab, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Birte Yding Glenthøj
- Centre for Neuropsychiatric Schizophrenia Research and Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup, University of Copenhagen, Glostrup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Louise Birkedal Glenthøj
- Copenhagen Research Center for Mental Health, Mental Health Center Copenhagen, University of Copenhagen, Copenhagen, Denmark
| | - Kristen M Haut
- Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, Chicago, Illinois
| | - Rebecca A Hayes
- Department of Psychiatry and Behavioral Sciences, Boston Children's Hospital, Boston, Massachusetts
| | - Karsten Heekeren
- Department of Psychiatry and Psychotherapy, LVR-Hospital Cologne, Cologne, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Christine I Hooker
- Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, Chicago, Illinois
| | - Wu Jeong Hwang
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
- Catholic Kwandong University College of Medicine, Gangneung, Republic of Korea
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey
| | - Michael Kaess
- Department of Child and Adolescent Psychiatry, University of Heidelberg, Heidelberg, Germany
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Kiyoto Kasai
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- The University of Tokyo Institute for Diversity and Adaptation of Human Mind, The University of Tokyo, Tokyo, Japan
- The International Research Center for Neurointelligence at The University of Tokyo Institutes for Advanced Study, The University of Tokyo, Tokyo, Japan
| | - Naoyuki Katagiri
- Department of Neuropsychiatry, Toho University School of Medicine, Tokyo, Japan
| | - Minah Kim
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jochen Kindler
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Shinsuke Koike
- The University of Tokyo Institute for Diversity and Adaptation of Human Mind, The University of Tokyo, Tokyo, Japan
- Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo, Tokyo, Japan
| | - Tina D Kristensen
- Centre for Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup, Copenhagen University Hospital, Glostrup, Denmark
| | - Jun Soo Kwon
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Stephen M Lawrie
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Irina Lebedeva
- Laboratory of Neuroimaging and Multimodal Analysis, Mental Health Research Center, Moscow, Russian Federation
| | - Jimmy Lee
- Department of Psychosis, Institute of Mental Health, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Imke L J Lemmers-Jansen
- Faculty of Behavioural and Movement Sciences, Department of Clinical, Neuro and Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Ashleigh Lin
- Telethon Kids Institute, The University of Western Australia, Perth, Western Australia, Australia
| | - Xiaoqian Ma
- National Clinical Research Center for Mental Disorders and Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Daniel H Mathalon
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco
- San Francisco Veterans Affairs Health Care System, San Francisco, California
| | - Philip McGuire
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Chantal Michel
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Romina Mizrahi
- Douglas Research Center, McGill Univesity, Montreal, Quebec, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | | | - Paul Møller
- Department for Mental Health Research and Development, Division of Mental Health and Addiction, Vestre Viken Hospital Trust, Drammen, Norway
| | - Ricardo Mora-Durán
- Emergency Department, Hospital Fray Bernardino Álvarez, Mexico City, Mexico
| | - Barnaby Nelson
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
- Orygen, Melbourne, Victoria, Australia
| | - Takahiro Nemoto
- Department of Neuropsychiatry, Toho University School of Medicine, Tokyo, Japan
| | - Merete Nordentoft
- Copenhagen Research Center for Mental Health, Mental Health Center Copenhagen, University of Copenhagen, Copenhagen, Denmark
| | - Dorte Nordholm
- Copenhagen Research Center for Mental Health, Mental Health Center Copenhagen, University of Copenhagen, Copenhagen, Denmark
| | - Maria A Omelchenko
- Department of Youth Psychiatry, Mental Health Research Center, Moscow, Russian Federation
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Western Health, Carlton South, VIC, Australia
- Florey Institute of Neuroscience and Mental Health, Center for Mental Health, Parkville, Victoria, Australia
| | - Jose C Pariente
- Magnetic Resonance Imaging Core Facility, Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
| | - Jayachandra M Raghava
- Centre for Neuropsychiatric Schizophrenia Research and Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup, University of Copenhagen, Glostrup, Denmark
- Functional Imaging Unit, Department of Clinical Physiology, Nuclear Medicine and PET, University of Copenhagen, Glostrup, Denmark
| | - Francisco Reyes-Madrigal
- Laboratory of Experimental Psychiatry, Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico
| | - Jan I Røssberg
- Oslo University Hospital and University of Oslo, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Wulf Rössler
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin, Berlin, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zurich, Zurich, Switzerland
| | - Dean F Salisbury
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Daiki Sasabayashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan
- Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Ulrich Schall
- Priority Centre for Brain and Mental Health Research, The University of Newcastle, Newcastle, New South Wales, Australia
- Priority Research Centre Grow Up Well, The University of Newcastle, Newcastle, New South Wales, Australia
| | - Lukasz Smigielski
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Child and Adolescent Psychiatry, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Gisela Sugranyes
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM)-ISCIII, Madrid Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (FCRB-IDIBAPS), Barcelona, Spain
- Child and Adolescent Psychiatry and Psychology Department, 2021SGR01319, Institute of Neuroscience, Hospital Clínic de Barcelona, Barcelona, Spain
- Department of Medicine, University of Barcelona, Barcelona, Spain
| | - Michio Suzuki
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan
- Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Tsutomu Takahashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan
- Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Christian K Tamnes
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Anastasia Theodoridou
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey
| | - Alexander S Tomyshev
- Laboratory of Neuroimaging and Multimodal Analysis, Mental Health Research Center, Moscow, Russian Federation
| | - Peter J Uhlhaas
- Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin, Berlin, Germany
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom
| | - Tor G Værnes
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Early Intervention in Psychosis Advisory Unit for South-East Norway, TIPS Sør-Øst, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Therese A M J van Amelsvoort
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Faculty of Health Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Theo G M van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine
| | - James A Waltz
- Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore
| | - Christina Wenneberg
- Copenhagen Research Center for Mental Health, Mental Health Center Copenhagen, University of Copenhagen, Copenhagen, Denmark
| | - Lars T Westlye
- KG Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Stephen J Wood
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
- Orygen, Melbourne, Victoria, Australia
- School of Psychology, University of Birmingham, Birmingham, United Kingdom
| | - Juan H Zhou
- Center for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Center for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Dennis Hernaus
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Faculty of Health Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Maria Jalbrzikowski
- Department of Psychiatry and Behavioral Sciences, Boston Children's Hospital, Boston, Massachusetts
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - René S Kahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Cheryl M Corcoran
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
- Mental Illness Research, Education and Clinical Center, James J. Peters VA Medical Center, New York, New York
| | - Sophia Frangou
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
- Djavad Mowafaghian Centre for Brain Health, Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
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30
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Walther S, Nadesalingam N, Nuoffer M, Kyrou A, Wüthrich F, Lefebvre S. Structural alterations of the motor cortex and higher order cortical areas suggest early neurodevelopmental origin of catatonia in schizophrenia. Schizophr Res 2024; 263:131-138. [PMID: 36272843 DOI: 10.1016/j.schres.2022.10.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 10/05/2022] [Accepted: 10/06/2022] [Indexed: 11/07/2022]
Abstract
The neurobiology of catatonia is still poorly understood. Particularly structural MRI studies yielded conflicting results. Heterogeneity of findings was suggested to stem from specifics of different rating scales. This study sought to test grey matter differences between patients with catatonia, patients without catatonia, and healthy controls using the two main instruments of catatonia rating. We included 98 patients with schizophrenia spectrum disorders and 42 healthy controls. Catatonia was measured using the Bush Francis Catatonia Rating Scale and the Northoff Catatonia Rating Scale. According to these scales, patients were classified into those with and those without catatonia. We tested whole brain grey matter volume, cortical thickness, and local gyrification across groups. Both catatonia rating scales correlated at tau = 0.65 but failed to classify identical subjects as catatonia patients. However, group differences in grey matter parameters were broadly similar with either rating scale to identify catatonia cases. Catatonia patients had reduced grey matter volume compared to controls in a large network including orbitofrontal cortex, cingulate, thalamus, and amygdala. While there was no group difference in cortical thickness, catatonia patients had increased local gyrification in premotor, motor, and parietal cortices compared to controls. Hypergyrification of the motor cortex and higher order cortical areas was found in catatonia patients compared to patients without catatonia. Both catatonia rating scales find similar symptom severity and group differences in grey matter indices. Catatonia is linked to reduced grey matter volume and increased local gyrification, suggesting some impact of early neurodevelopmental insults.
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Affiliation(s)
- Sebastian Walther
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland.
| | - Niluja Nadesalingam
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland; Graduate School for Health Sciences, University of Bern, Switzerland
| | - Melanie Nuoffer
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland; Graduate School for Health Sciences, University of Bern, Switzerland
| | - Alexandra Kyrou
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Florian Wüthrich
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland; Graduate School for Health Sciences, University of Bern, Switzerland
| | - Stephanie Lefebvre
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
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Shen L, Zhang J, Fan S, Ping L, Yu H, Xu F, Cheng Y, Xu X, Yang C, Zhou C. Cortical thickness abnormalities in autism spectrum disorder. Eur Child Adolesc Psychiatry 2024; 33:65-77. [PMID: 36542200 DOI: 10.1007/s00787-022-02133-0] [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] [Received: 09/22/2022] [Accepted: 12/15/2022] [Indexed: 12/24/2022]
Abstract
The pathological mechanism of autism spectrum disorder (ASD) remains unclear. Nowadays, surface-based morphometry (SBM) based on structural magnetic resonance imaging (sMRI) techniques have reported cortical thickness (CT) variations in ASD. However, the findings were inconsistent and heterogeneous. This current meta-analysis conducted a whole-brain vertex-wise coordinate-based meta-analysis (CBMA) on CT studies to explore the most noticeable and robust CT changes in ASD individuals by applying the seed-based d mapping (SDM) program. A total of 26 investigations comprised 27 datasets were included, containing 1,635 subjects with ASD and 1470 HC, along with 94 coordinates. Individuals with ASD exhibited significantly altered CT in several regions compared to HC, including four clusters with thicker CT in the right superior temporal gyrus (STG.R), the left middle temporal gyrus (MTG.L), the left anterior cingulate/paracingulate gyri, the right superior frontal gyrus (SFG.R, medial orbital parts), as well as three clusters with cortical thinning including the left parahippocampal gyrus (PHG.L), the right precentral gyrus (PCG.R) and the left middle frontal gyrus (MFG.L). Adults with ASD only demonstrated CT thinning in the right parahippocampal gyrus (PHG.R), revealed by subgroup meta-analyses. Meta-regression analyses found that CT in STG.R was positively correlated with age. Meanwhile, CT in MFG.L and PHG.L had negative correlations with the age of ASD individuals. These results suggested a complicated and atypical cortical development trajectory in ASD, and would provide a deeper understanding of the neural mechanism underlying the cortical morphology in ASD.
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Affiliation(s)
- Liancheng Shen
- Department of Psychiatry, Shandong Daizhuang Hospital, Jining, China
| | - Junqing Zhang
- Department of Pharmacy, Shandong Daizhuang Hospital, Jining, China
| | - Shiran Fan
- School of Mental Health, Jining Medical University, Jining, China
| | - Liangliang Ping
- Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China
| | - Hao Yu
- School of Mental Health, Jining Medical University, Jining, China
| | - Fangfang Xu
- School of Mental Health, Jining Medical University, Jining, China
| | - Yuqi Cheng
- Department of Psychiatry, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Xiufeng Xu
- Department of Psychiatry, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Chunyan Yang
- School of Rehabilitation Medicine, Jining Medical University, Jining, China.
| | - Cong Zhou
- School of Mental Health, Jining Medical University, Jining, China.
- Department of Psychology, Affiliated Hospital of Jining Medical University, Jining, China.
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Roell L, Keeser D, Papazov B, Lembeck M, Papazova I, Greska D, Muenz S, Schneider-Axmann T, Sykorova EB, Thieme CE, Vogel BO, Mohnke S, Huppertz C, Roeh A, Keller-Varady K, Malchow B, Stoecklein S, Ertl-Wagner B, Henkel K, Wolfarth B, Tantchik W, Walter H, Hirjak D, Schmitt A, Hasan A, Meyer-Lindenberg A, Falkai P, Maurus I. Effects of Exercise on Structural and Functional Brain Patterns in Schizophrenia-Data From a Multicenter Randomized-Controlled Study. Schizophr Bull 2024; 50:145-156. [PMID: 37597507 PMCID: PMC10754172 DOI: 10.1093/schbul/sbad113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/21/2023]
Abstract
BACKGROUND AND HYPOTHESIS Aerobic exercise interventions in people with schizophrenia have been demonstrated to improve clinical outcomes, but findings regarding the underlying neural mechanisms are limited and mainly focus on the hippocampal formation. Therefore, we conducted a global exploratory analysis of structural and functional neural adaptations after exercise and explored their clinical implications. STUDY DESIGN In this randomized controlled trial, structural and functional MRI data were available for 91 patients with schizophrenia who performed either aerobic exercise on a bicycle ergometer or underwent a flexibility, strengthening, and balance training as control group. We analyzed clinical and neuroimaging data before and after 6 months of regular exercise. Bayesian linear mixed models and Bayesian logistic regressions were calculated to evaluate effects of exercise on multiple neural outcomes and their potential clinical relevance. STUDY RESULTS Our results indicated that aerobic exercise in people with schizophrenia led to structural and functional adaptations mainly within the default-mode network, the cortico-striato-pallido-thalamo-cortical loop, and the cerebello-thalamo-cortical pathway. We further observed that volume increases in the right posterior cingulate gyrus as a central node of the default-mode network were linked to improvements in disorder severity. CONCLUSIONS These exploratory findings suggest a positive impact of aerobic exercise on 3 cerebral networks that are involved in the pathophysiology of schizophrenia. CLINICAL TRIALS REGISTRATION The underlying study of this manuscript was registered in the International Clinical Trials Database, ClinicalTrials.gov (NCT number: NCT03466112, https://clinicaltrials.gov/ct2/show/NCT03466112?term=NCT03466112&draw=2&rank=1) and in the German Clinical Trials Register (DRKS-ID: DRKS00009804).
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Affiliation(s)
- Lukas Roell
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
- Neuroimaging Core Unit Munich (NICUM), University Hospital, LMU Munich, Munich, Germany
| | - Daniel Keeser
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
- Neuroimaging Core Unit Munich (NICUM), University Hospital, LMU Munich, Munich, Germany
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Boris Papazov
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Moritz Lembeck
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Irina Papazova
- Department of Psychiatry, Psychotherapy and Psychosomatics of the University Augsburg, Medical Faculty, University of Augsburg, Bezirkskrankenhaus Augsburg, Augsburg, Germany
| | - David Greska
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Susanne Muenz
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Thomas Schneider-Axmann
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Eliska B Sykorova
- Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Germany
| | - Christina E Thieme
- Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Germany
| | - Bob O Vogel
- Department of Psychiatry and Psychotherapy, University Hospital Charité Berlin, Berlin, Germany
| | - Sebastian Mohnke
- Department of Psychiatry and Psychotherapy, University Hospital Charité Berlin, Berlin, Germany
| | - Charlotte Huppertz
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
| | - Astrid Roeh
- Department of Psychiatry, Psychotherapy and Psychosomatics of the University Augsburg, Medical Faculty, University of Augsburg, Bezirkskrankenhaus Augsburg, Augsburg, Germany
| | - Katriona Keller-Varady
- Hannover Medical School, Department of Rehabilitation and Sports Medicine, Hannover, Germany
| | - Berend Malchow
- Department of Psychiatry and Psychotherapy, University Hospital Göttingen, Göttingen, Germany
| | - Sophia Stoecklein
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Birgit Ertl-Wagner
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
- Division of Neuroradiology, Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, Canada
- Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Karsten Henkel
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
| | - Bernd Wolfarth
- Department of Sports Medicine, University Hospital Charité Berlin, Berlin, Germany
| | - Wladimir Tantchik
- Department of Psychiatry and Psychotherapy, University Hospital Charité Berlin, Berlin, Germany
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy, University Hospital Charité Berlin, Berlin, Germany
| | - Dusan Hirjak
- Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Germany
| | - Andrea Schmitt
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
- Laboratory of Neuroscience (LIM27), Institute of Psychiatry, University of Sao Paulo, São Paulo, Brazil
| | - Alkomiet Hasan
- Department of Psychiatry, Psychotherapy and Psychosomatics of the University Augsburg, Medical Faculty, University of Augsburg, Bezirkskrankenhaus Augsburg, Augsburg, Germany
| | | | - Peter Falkai
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
- Max Planck Institute of Psychiatry, Munich, Germany
| | - Isabel Maurus
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
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Li L, Yang W, Wan Y, Shen H, Wang T, Ping L, Liu C, Chen M, Yu H, Jin S, Cheng Y, Xu X, Zhou C. White matter alterations in mild cognitive impairment revealed by meta-analysis of diffusion tensor imaging using tract-based spatial statistics. Brain Imaging Behav 2023; 17:639-651. [PMID: 37656372 DOI: 10.1007/s11682-023-00791-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/28/2023] [Indexed: 09/02/2023]
Abstract
The neuropathological mechanism of mild cognitive impairment (MCI) remains unclarified. Diffusion tensor imaging (DTI) studies revealed white matter (WM) microarchitecture alterations in MCI, but consistent findings and conclusions have not yet been drawn. The present coordinate-based meta-analysis (CBMA) of tract-based spatial statistics (TBSS) studies aimed to identify the most prominent and robust WM abnormalities in patients with MCI. A systematic search of relevant studies was conducted through January 2022 to identify TBSS studies comparing fractional anisotropy (FA) between MCI patients and healthy controls (HC). We used the seed-based d mapping (SDM) software to achieve the CBMA and analyze regional FA alterations in MCI. Meta-regression analysis was subsequently applied to explore the potential associations between clinical variables and FA changes. MCI patients demonstrated significantly decreased FA in widely distributed areas in the corpus callosum (CC), including the genu, body, and splenium of the CC, as well as one cluster in the left striatum. FA in the body of the CC and in three clusters in the splenium of the CC was negatively associated with the mean age. Additionally, FA in the genu of the CC and in three clusters in the splenium of the CC had negative correlations with the MMSE scores. Disrupted integrities of the CC and left striatum might play vital roles in the process of cognitive decline. These findings enhanced our understanding of the neural mechanism underlying WM neurodegeneration in MCI and provided perspectives for the early detection and intervention of dementia.Registration number: CRD42022235716.
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Affiliation(s)
- Longfei Li
- Department of Psychiatry, Shandong Daizhuang Hospital, Jining, China
| | - Wei Yang
- Department of Psychiatry, Shandong Daizhuang Hospital, Jining, China
| | - Yu Wan
- School of Mental Health, Jining Medical University, Jining, China
| | - Hailong Shen
- School of Mental Health, Jining Medical University, Jining, China
| | - Ting Wang
- Outpatient Department, Affiliated Hospital of Jining Medical University, Jining, China
| | - Liangliang Ping
- Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China
| | - Chuanxin Liu
- School of Mental Health, Jining Medical University, Jining, China
| | - Min Chen
- School of Mental Health, Jining Medical University, Jining, China
| | - Hao Yu
- School of Mental Health, Jining Medical University, Jining, China
| | - Shushu Jin
- Department of Psychology, Affiliated Hospital of Jining Medical University, Jining, China
| | - Yuqi Cheng
- Department of Psychiatry, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Xiufeng Xu
- Department of Psychiatry, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Cong Zhou
- School of Mental Health, Jining Medical University, Jining, China.
- Department of Psychology, Affiliated Hospital of Jining Medical University, Jining, China.
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Wang Y, Wu Y, Luo L, Li F. Structural and functional alterations in the brains of patients with anisometropic and strabismic amblyopia: a systematic review of magnetic resonance imaging studies. Neural Regen Res 2023; 18:2348-2356. [PMID: 37282452 PMCID: PMC10360096 DOI: 10.4103/1673-5374.371349] [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] [Indexed: 06/08/2023] Open
Abstract
Amblyopia is the most common cause of vision loss in children and can persist into adulthood in the absence of effective intervention. Previous clinical and neuroimaging studies have suggested that the neural mechanisms underlying strabismic amblyopia and anisometropic amblyopia may be different. Therefore, we performed a systematic review of magnetic resonance imaging studies investigating brain alterations in patients with these two subtypes of amblyopia; this study is registered with PROSPERO (registration ID: CRD42022349191). We searched three online databases (PubMed, EMBASE, and Web of Science) from inception to April 1, 2022; 39 studies with 633 patients (324 patients with anisometropic amblyopia and 309 patients with strabismic amblyopia) and 580 healthy controls met the inclusion criteria (e.g., case-control designed, peer-reviewed articles) and were included in this review. These studies highlighted that both strabismic amblyopia and anisometropic amblyopia patients showed reduced activation and distorted topological cortical activated maps in the striate and extrastriate cortices during task-based functional magnetic resonance imaging with spatial-frequency stimulus and retinotopic representations, respectively; these may have arisen from abnormal visual experiences. Compensations for amblyopia that are reflected in enhanced spontaneous brain function have been reported in the early visual cortices in the resting state, as well as reduced functional connectivity in the dorsal pathway and structural connections in the ventral pathway in both anisometropic amblyopia and strabismic amblyopia patients. The shared dysfunction of anisometropic amblyopia and strabismic amblyopia patients, relative to controls, is also characterized by reduced spontaneous brain activity in the oculomotor cortex, mainly involving the frontal and parietal eye fields and the cerebellum; this may underlie the neural mechanisms of fixation instability and anomalous saccades in amblyopia. With regards to specific alterations of the two forms of amblyopia, anisometropic amblyopia patients suffer more microstructural impairments in the precortical pathway than strabismic amblyopia patients, as reflected by diffusion tensor imaging, and more significant dysfunction and structural loss in the ventral pathway. Strabismic amblyopia patients experience more attenuation of activation in the extrastriate cortex than in the striate cortex when compared to anisometropic amblyopia patients. Finally, brain structural magnetic resonance imaging alterations tend to be lateralized in the adult anisometropic amblyopia patients, and the patterns of brain alterations are more limited in amblyopic adults than in children. In conclusion, magnetic resonance imaging studies provide important insights into the brain alterations underlying the pathophysiology of amblyopia and demonstrate common and specific alterations in anisometropic amblyopia and strabismic amblyopia patients; these alterations may improve our understanding of the neural mechanisms underlying amblyopia.
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Affiliation(s)
- Yuxia Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Ye Wu
- Department of Ophthalmology, Laboratory of Optometry and Vision Sciences, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, Sichuan Province, China
| | - Lekai Luo
- Department of Radiology, West China Second Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Fei Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
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Sun S, Xiao S, Guo Z, Gong J, Tang G, Huang L, Wang Y. Meta-analysis of cortical thickness reduction in adult schizophrenia. J Psychiatry Neurosci 2023; 48:E461-E470. [PMID: 38123240 PMCID: PMC10743639 DOI: 10.1503/jpn.230081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 08/17/2023] [Accepted: 09/11/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Numerous neuroimaging studies using surface-based morphometry analyses have reported altered cortical thickness among patients with schizophrenia, but the results have been inconsistent. We sought to provide a whole-brain meta-analysis, which may help enhance the spatial accuracy of identification. METHODS We conducted a meta-analysis of whole-brain studies that explored cortical thickness alteration among adult patients with schizophrenia, including first-episode patients with schizophrenia, and patients with chronic schizophrenia, compared with healthy controls by using the seed-based d mapping with permutation of subject images (SDM-PSI) software. RESULTS A systematic literature search identified 25 studies (33 data sets) of cortical thickness, including 2008 patients with schizophrenia and 2004 healthy controls. Overall, patients with schizophrenia showed decreased cortical thickness in the right inferior frontal gyrus (IFG) and bilateral insula extending to the superior temporal gyrus (STG). Subgroup meta-analysis reported that patients with chronic schizophrenia showed decreased cortical thickness in the right insula extending to the right IFG. There was no significant cortical thickness difference between first-episode patients with schizophrenia and healthy controls. LIMITATIONS The results of meta-regression analyses should be viewed cautiously since they were driven by a small number of studies or did not overlap with the between-group differences found in the primary analyses. CONCLUSION The meta-analysis suggested robust cortical thickness reduction in the IFG, insula and STG among adult patients with schizophrenia, particularly in those with chronic schizophrenia. The results provide useful insights to understanding the underlying pathophysiology of schizophrenia.
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Affiliation(s)
- Shilin Sun
- From the Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China (Sun, Xiao, Guo, Tang, Huang, Wang); the Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, China (Sun, Xiao, Guo, Gong, Tang, Huang, Wang); the Department of Radiology, Six Affiliated Hospital of Sun Yat-sen University, Guangzhou, China (Gong)
| | - Shu Xiao
- From the Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China (Sun, Xiao, Guo, Tang, Huang, Wang); the Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, China (Sun, Xiao, Guo, Gong, Tang, Huang, Wang); the Department of Radiology, Six Affiliated Hospital of Sun Yat-sen University, Guangzhou, China (Gong)
| | - Zixuan Guo
- From the Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China (Sun, Xiao, Guo, Tang, Huang, Wang); the Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, China (Sun, Xiao, Guo, Gong, Tang, Huang, Wang); the Department of Radiology, Six Affiliated Hospital of Sun Yat-sen University, Guangzhou, China (Gong)
| | - Jiaying Gong
- From the Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China (Sun, Xiao, Guo, Tang, Huang, Wang); the Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, China (Sun, Xiao, Guo, Gong, Tang, Huang, Wang); the Department of Radiology, Six Affiliated Hospital of Sun Yat-sen University, Guangzhou, China (Gong)
| | - Guixian Tang
- From the Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China (Sun, Xiao, Guo, Tang, Huang, Wang); the Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, China (Sun, Xiao, Guo, Gong, Tang, Huang, Wang); the Department of Radiology, Six Affiliated Hospital of Sun Yat-sen University, Guangzhou, China (Gong)
| | - Li Huang
- From the Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China (Sun, Xiao, Guo, Tang, Huang, Wang); the Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, China (Sun, Xiao, Guo, Gong, Tang, Huang, Wang); the Department of Radiology, Six Affiliated Hospital of Sun Yat-sen University, Guangzhou, China (Gong)
| | - Ying Wang
- From the Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China (Sun, Xiao, Guo, Tang, Huang, Wang); the Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, China (Sun, Xiao, Guo, Gong, Tang, Huang, Wang); the Department of Radiology, Six Affiliated Hospital of Sun Yat-sen University, Guangzhou, China (Gong)
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Wu Y, Xiong L, Wang Y, Chen Q, Li F, Zhang W, Liu L. Frequencies and patterns of symptoms in Chinese adults with accommodative and binocular dysfunctions. Graefes Arch Clin Exp Ophthalmol 2023; 261:2961-2970. [PMID: 36757504 DOI: 10.1007/s00417-022-05968-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 12/16/2022] [Accepted: 12/28/2022] [Indexed: 02/10/2023] Open
Abstract
PURPOSE Recent studies have found that children with convergence insufficiency experience higher frequencies of performance-related symptoms (e.g., losing concentration), but data on performance-related symptoms among adults with accommodative dysfunctions (ADs) and/or binocular dysfunctions (BDs) are lacking, which might cause misdiagnosis, diagnostic confusion, or exacerbation of attention deficits. We aimed to describe frequencies and symptom patterns in adults with ADs and/or BDs who were treated at optometric clinics and explore any correlations between visual symptoms and clinical findings. METHODS This cross-sectional study divided 235 participants (age: 23.7 ± 2.9 years) into three groups: ADs, BDs, and normal binocular vision (NBV) groups. Convergence Insufficiency Symptom Survey (CISS), refractive examinations, and binocular tests were administered to all participants. After 1-to-1 propensity score matching, outcomes were assessed using Mann‒Whitney U test and Pearson's correlation analysis among three groups. RESULTS In this sample, the number (frequency) of individuals with ADs and/or BDs was 117 (49.8%). ADs and BDs groups experienced significantly more performance-related symptoms (feeling sleepy, losing concentration, trouble remembering, reading slowly, losing place, and having to re-read; all P < 0.05) than the NBV group. Significant correlations were observed between performance-related symptoms and clinical findings, including accommodative amplitude (r = - 0.294), accommodative facility (r = - 0.452), near phoria (r = - 0.261), near point of convergence (r = 0.482), and positive fusional vergence (r = - 0.331) (all P < 0.001). CONCLUSION ADs and/or BDs are commonly present in adults treated at optometric clinics, and adults diagnosed with ADs and/or BDs exhibit more performance-related symptoms than participants with NBV.
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Affiliation(s)
- Ye Wu
- Department of Ophthalmology, Laboratory of Optometry and Vision Science, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Ling Xiong
- Department of Ophthalmology, Laboratory of Optometry and Vision Science, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Yuxia Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Qian Chen
- Department of Clinical Research Management, Center of Biostatistics, Design, Measurement and Evaluation, West China Hospital of Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Fei Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, People's Republic of China.
| | - Wenqiu Zhang
- Department of Ophthalmology, Laboratory of Optometry and Vision Science, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, Sichuan, People's Republic of China.
| | - Longqian Liu
- Department of Ophthalmology, Laboratory of Optometry and Vision Science, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, Sichuan, People's Republic of China
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Emsley R. Antipsychotics and structural brain changes: could treatment adherence explain the discrepant findings? Ther Adv Psychopharmacol 2023; 13:20451253231195258. [PMID: 37701891 PMCID: PMC10493054 DOI: 10.1177/20451253231195258] [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] [Received: 03/15/2023] [Accepted: 07/11/2023] [Indexed: 09/14/2023] Open
Abstract
Progressive structural brain changes are well documented in schizophrenia and have been linked to both illness progression and the extent of antipsychotic treatment exposure. Literature reporting longitudinal changes in brain structure in individuals with schizophrenia is selectively reviewed to assess the roles of illness, antipsychotic treatment, adherence and other factors in the genesis of these changes. This narrative review considers literature investigating longitudinal changes in brain structure in individuals with schizophrenia. The review focusses on structural changes in the cortex, basal ganglia and white matter. It also examines effects of medication non-adherence and relapse on the clinical course of the illness and on structural brain changes. Studies investigating structural magnetic resonance imaging changes in patients treated with long-acting injectable antipsychotics are reviewed. Temporal changes in brain structure in schizophrenia can be divided into those that are associated with antipsychotic treatment and those that are not. Changes associated with treatment include increases in basal ganglia and white matter volumes. Relapse episodes may be a critical factor in illness progression and brain volume reductions. Medication adherence may be an important factor that could explain the findings that brain volume reductions are associated with poor treatment response, higher intensity of antipsychotic treatment exposure and more time spent in relapse. Improved adherence via long-acting injectable antipsychotics and adherence focussed psychosocial interventions could maximize protective effects of antipsychotics against illness progression.
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Affiliation(s)
- Robin Emsley
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, PO Box 241, Tygerberg Campus, Cape Town 8000, South Africa
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Luo L, Li Q, Wang Y, He N, Wang Y, You W, Zhang Q, Long F, Chen L, Zhao Y, Yao L, Sweeney JA, Gong Q, Li F. Shared and Disorder-Specific Alterations of Brain Temporal Dynamics in Obsessive-Compulsive Disorder and Schizophrenia. Schizophr Bull 2023; 49:1387-1398. [PMID: 37030006 PMCID: PMC10483459 DOI: 10.1093/schbul/sbad042] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/10/2023]
Abstract
BACKGROUND Obsessive-compulsive disorder (OCD) and schizophrenia have distinct but also overlapping symptoms. Few studies have examined the shared and disorder-specific disturbances in dynamic brain function in the 2 disorders. STUDY DESIGN Resting-state functional magnetic resonance imaging data of 31 patients with OCD and 49 patients with schizophrenia, all untreated, and 45 healthy controls (HCs) were analyzed using spatial group independent component (IC) analysis. Time-varying degree centrality patterns across the whole brain were clustered into 3 reoccurring states, and state transition metrics were obtained. We further explored regional temporal variability of degree centrality for each IC across all time windows. STUDY RESULTS Patients with OCD and patients with schizophrenia both showed decreased occurrence of a state having the highest centrality in the sensorimotor and auditory networks. Additionally, patients with OCD and patients with schizophrenia both exhibited reduced dynamics of degree centrality in the superior frontal gyrus than controls, while dynamic degree centrality of the cerebellum was lower in patients with schizophrenia than with OCD and HCs. Altered dynamics of degree centrality nominally correlated with symptom severity in both patient groups. CONCLUSIONS Our study provides evidence of transdiagnostic and clinically relevant functional brain abnormalities across OCD and schizophrenia in neocortex, as well as functional dynamic alterations in the cerebellum specific to schizophrenia. These findings add to the recognition of overlap in neocortical alterations in the 2 disorders, and indicate that cerebellar alterations in schizophrenia may be specifically important in schizophrenia pathophysiology via impact on cerebellar thalamocortical circuitry.
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Affiliation(s)
- Lekai Luo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
- Department of Radiology, West China Second Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Qian Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Yaxuan Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Ning He
- Department of Psychiatry, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Yuxia Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Wanfang You
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Qian Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Fenghua Long
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Lizhou Chen
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Youjin Zhao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Li Yao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Fei Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
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39
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Dai R, Herold CJ, Wang X, Kong L, Schröder J. Structural brain networks in schizophrenia based on nonnegative matrix factorization. Psychiatry Res Neuroimaging 2023; 334:111690. [PMID: 37480705 DOI: 10.1016/j.pscychresns.2023.111690] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 06/11/2023] [Accepted: 07/18/2023] [Indexed: 07/24/2023]
Abstract
Schizophrenia is a severe mental disease with significant morphometric reductions in gray matter volume and cortical thickness in a variety of brain regions. However, most studies only focused on the voxel level alterations in specific cerebral regions and ignored the spatial relationship between voxels. In the present study, we used a novel, data-driven technique-nonnegative matrix factorization (NMF) to group voxels with similar information into a network, and studied the structural covariance at the network level in schizophrenia. Our sample included 36 patients with schizophrenia and 21 healthy controls. Compared with healthy controls, patients with schizophrenia showed significant gray matter volume reductions in six structural covariance networks (dorsal striatum, thalamus, hippocampus-parahippocampus, supplementary motor area-fusiform, middle/inferior temporal network, frontal-parietal-occipital network). Our findings confirmed the assumption of a disturbance in the cortical-subcortical circuit in schizophrenia and suggested that NMF is a useful multivariate method to identify brain networks, which provides a new perspective to study the neural mechanism in schizophrenia.
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Affiliation(s)
- Rongjie Dai
- Department of Psychology, Shanghai Normal University, Shanghai, China
| | - Christina J Herold
- Section of Geriatric Psychiatry, Department of Psychiatry, University of Heidelberg, Germany
| | - Xingsong Wang
- Department of Psychology, Shanghai Normal University, Shanghai, China
| | - Li Kong
- Department of Psychology, Shanghai Normal University, Shanghai, China.
| | - Johannes Schröder
- Section of Geriatric Psychiatry, Department of Psychiatry, University of Heidelberg, Germany
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40
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Matéos M, Hacein-Bey L, Hanafi R, Mathys L, Amad A, Pruvo JP, Krystal S. Advanced imaging in first episode psychosis: a systematic review. J Neuroradiol 2023; 50:464-469. [PMID: 37028754 DOI: 10.1016/j.neurad.2023.04.001] [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: 03/14/2023] [Revised: 03/31/2023] [Accepted: 04/04/2023] [Indexed: 04/09/2023]
Abstract
First-episode psychosis (FEP) is defined as the first occurrence of delusions, hallucinations, or psychic disorganization of significant magnitude, lasting more than 7 days. Evolution is difficult to predict since the first episode remains isolated in one third of cases, while recurrence occurs in another third, and the last third progresses to a schizo-affective disorder. It has been suggested that the longer psychosis goes unnoticed and untreated, the more severe the probability of relapse and recovery. MRI has become the gold standard for imaging psychiatric disorders, especially first episode psychosis. Besides ruling out some neurological conditions that may have psychiatric manifestations, advanced imaging techniques allow for identifying imaging biomarkers of psychiatric disorders. We performed a systematic review of the literature to determine how advanced imaging in FEP may have high diagnostic specificity and predictive value regarding the evolution of disease.
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Affiliation(s)
- Marjorie Matéos
- Lille University Hospital Center, Department of Neuroradiology, Lille, France.
| | - Lotfi Hacein-Bey
- Neuroradiology, Radiology Department, University of California Davis School of Medicine, Sacramento, CA, 95817, USA
| | - Riyad Hanafi
- Lille University Hospital Center, Department of Neuroradiology, Lille, France; Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, F-59000 Lille, France
| | - Luc Mathys
- Lille University Hospital Center, Department of Neuroradiology, Lille, France
| | - Ali Amad
- Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, F-59000 Lille, France; Department of neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Jean-Pierre Pruvo
- Lille University Hospital Center, Department of Neuroradiology, Lille, France; Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, F-59000 Lille, France
| | - Sidney Krystal
- Lille University Hospital Center, Department of Neuroradiology, Lille, France; Radiology Department, A. de Rothschild Foundation Hospital, Paris, France; Neurospin, CEA, Université Paris-Saclay, Gif-Sur-Yvette, Paris, France
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41
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Cattarinussi G, Gugliotta AA, Sambataro F. The Risk for Schizophrenia-Bipolar Spectrum: Does the Apple Fall Close to the Tree? A Narrative Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:6540. [PMID: 37569080 PMCID: PMC10418911 DOI: 10.3390/ijerph20156540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 07/24/2023] [Accepted: 08/04/2023] [Indexed: 08/13/2023]
Abstract
Schizophrenia (SCZ) and bipolar disorder (BD) are severe psychiatric disorders that share clinical features and several risk genes. Important information about their genetic underpinnings arises from intermediate phenotypes (IPs), quantifiable biological traits that are more prevalent in unaffected relatives (RELs) of patients compared to the general population and co-segregate with the disorders. Within IPs, neuropsychological functions and neuroimaging measures have the potential to provide useful insight into the pathophysiology of SCZ and BD. In this context, the present narrative review provides a comprehensive overview of the available evidence on deficits in neuropsychological functions and neuroimaging alterations in unaffected relatives of SCZ (SCZ-RELs) and BD (BD-RELs). Overall, deficits in cognitive functions including intelligence, memory, attention, executive functions, and social cognition could be considered IPs for SCZ. Although the picture for cognitive alterations in BD-RELs is less defined, BD-RELs seem to present worse performances compared to controls in executive functioning, including adaptable thinking, planning, self-monitoring, self-control, and working memory. Among neuroimaging markers, SCZ-RELs appear to be characterized by structural and functional alterations in the cortico-striatal-thalamic network, while BD risk seems to be associated with abnormalities in the prefrontal, temporal, thalamic, and limbic regions. In conclusion, SCZ-RELs and BD-RELs present a pattern of cognitive and neuroimaging alterations that lie between patients and healthy individuals. Similar abnormalities in SCZ-RELs and BD-RELs may be the phenotypic expression of the shared genetic mechanisms underlying both disorders, while the specificities in neuropsychological and neuroimaging profiles may be associated with the differential symptom expression in the two disorders.
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Affiliation(s)
- Giulia Cattarinussi
- Department of Neuroscience (DNS), University of Padova, 35131 Padova, Italy; (G.C.); (A.A.G.)
- Padova Neuroscience Center, University of Padova, 35131 Padova, Italy
| | - Alessio A. Gugliotta
- Department of Neuroscience (DNS), University of Padova, 35131 Padova, Italy; (G.C.); (A.A.G.)
| | - Fabio Sambataro
- Department of Neuroscience (DNS), University of Padova, 35131 Padova, Italy; (G.C.); (A.A.G.)
- Padova Neuroscience Center, University of Padova, 35131 Padova, Italy
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42
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Porter A, Fei S, Damme KSF, Nusslock R, Gratton C, Mittal VA. A meta-analysis and systematic review of single vs. multimodal neuroimaging techniques in the classification of psychosis. Mol Psychiatry 2023; 28:3278-3292. [PMID: 37563277 PMCID: PMC10618094 DOI: 10.1038/s41380-023-02195-9] [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] [Received: 10/03/2022] [Revised: 07/11/2023] [Accepted: 07/17/2023] [Indexed: 08/12/2023]
Abstract
BACKGROUND Psychotic disorders are characterized by structural and functional abnormalities in brain networks. Neuroimaging techniques map and characterize such abnormalities using unique features (e.g., structural integrity, coactivation). However, it is unclear if a specific method, or a combination of modalities, is particularly effective in identifying differences in brain networks of someone with a psychotic disorder. METHODS A systematic meta-analysis evaluated machine learning classification of schizophrenia spectrum disorders in comparison to healthy control participants using various neuroimaging modalities (i.e., T1-weighted imaging (T1), diffusion tensor imaging (DTI), resting state functional connectivity (rs-FC), or some combination (multimodal)). Criteria for manuscript inclusion included whole-brain analyses and cross-validation to provide a complete picture regarding the predictive ability of large-scale brain systems in psychosis. For this meta-analysis, we searched Ovid MEDLINE, PubMed, PsychInfo, Google Scholar, and Web of Science published between inception and March 13th 2023. Prediction results were averaged for studies using the same dataset, but parallel analyses were run that included studies with pooled sample across many datasets. We assessed bias through funnel plot asymmetry. A bivariate regression model determined whether differences in imaging modality, demographics, and preprocessing methods moderated classification. Separate models were run for studies with internal prediction (via cross-validation) and external prediction. RESULTS 93 studies were identified for quantitative review (30 T1, 9 DTI, 40 rs-FC, and 14 multimodal). As a whole, all modalities reliably differentiated those with schizophrenia spectrum disorders from controls (OR = 2.64 (95%CI = 2.33 to 2.95)). However, classification was relatively similar across modalities: no differences were seen across modalities in the classification of independent internal data, and a small advantage was seen for rs-FC studies relative to T1 studies in classification in external datasets. We found large amounts of heterogeneity across results resulting in significant signs of bias in funnel plots and Egger's tests. Results remained similar, however, when studies were restricted to those with less heterogeneity, with continued small advantages for rs-FC relative to structural measures. Notably, in all cases, no significant differences were seen between multimodal and unimodal approaches, with rs-FC and unimodal studies reporting largely overlapping classification performance. Differences in demographics and analysis or denoising were not associated with changes in classification scores. CONCLUSIONS The results of this study suggest that neuroimaging approaches have promise in the classification of psychosis. Interestingly, at present most modalities perform similarly in the classification of psychosis, with slight advantages for rs-FC relative to structural modalities in some specific cases. Notably, results differed substantially across studies, with suggestions of biased effect sizes, particularly highlighting the need for more studies using external prediction and large sample sizes. Adopting more rigorous and systematized standards will add significant value toward understanding and treating this critical population.
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Affiliation(s)
- Alexis Porter
- Department of Psychology, Northwestern University, Evanston, IL, USA.
| | - Sihan Fei
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Katherine S F Damme
- Department of Psychology, Northwestern University, Evanston, IL, USA
- Institute for Innovations in Developmental Sciences, Northwestern University, Evanston and Chicago, IL, USA
| | - Robin Nusslock
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Caterina Gratton
- Department of Psychology, Florida State University, Tallahassee, FL, USA
| | - Vijay A Mittal
- Department of Psychology, Northwestern University, Evanston, IL, USA
- Institute for Innovations in Developmental Sciences, Northwestern University, Evanston and Chicago, IL, USA
- Department of Psychiatry, Northwestern University, Chicago, IL, USA
- Medical Social Sciences, Northwestern University, Chicago, IL, USA
- Institute for Policy Research, Northwestern University, Chicago, IL, USA
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Hua JPY, Loewy RL, Stuart B, Fryer SL, Niendam TA, Carter CS, Vinogradov S, Mathalon DH. Cortical and subcortical brain morphometry abnormalities in youth at clinical high-risk for psychosis and individuals with early illness schizophrenia. Psychiatry Res Neuroimaging 2023; 332:111653. [PMID: 37121090 PMCID: PMC10362971 DOI: 10.1016/j.pscychresns.2023.111653] [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] [Received: 01/23/2023] [Revised: 03/27/2023] [Accepted: 04/18/2023] [Indexed: 05/02/2023]
Abstract
Neuroimaging studies have documented morphometric brain abnormalities in schizophrenia, but less is known about them in individuals at clinical high-risk for psychosis (CHR-P), including how they compare with those observed in early schizophrenia (ESZ). Accordingly, we implemented multivariate profile analysis of regional morphometric profiles in CHR-P (n = 89), ESZ (n = 93) and healthy controls (HC; n = 122). ESZ profiles differed from HC and CHR-P profiles, including 1) cortical thickness: significant level reduction and regional non-parallelism reflecting widespread thinning, except for entorhinal and pericalcarine cortex, 2) basal ganglia volume: significant level increase and regional non-parallelism reflecting larger caudate and pallidum, and 3) ventricular volume: significant level increase with parallel regional profiles. CHR-P and ESZ cerebellar profiles showed significant non-parallelism with HC profiles. Regional profiles did not significantly differ between groups for cortical surface area or subcortical volume. Compared to CHR-P followed for ≥18 months without psychosis conversion (n = 31), CHR-P converters (n = 17) showed significant non-parallel ventricular volume expansion reflecting specific enlargement of lateral and inferolateral regions. Antipsychotic dosage in ESZ was significantly correlated with frontal cortical thinning. Results suggest that morphometric abnormalities in ESZ are not present in CHR-P, except for ventricular enlargement, which was evident in CHR-P who developed psychosis.
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Affiliation(s)
- Jessica P Y Hua
- Sierra Pacific Mental Illness Research Education and Clinical Centers, San Francisco VA Medical Center, and the University of California, San Francisco, CA, United States; Mental Health Service, San Francisco VA Medical Center, San Francisco, 94121, CA, United States; Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, 94143, CA, United States; Department of Psychological Sciences, University of Missouri, Columbia, 65211, MO, United States
| | - Rachel L Loewy
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, 94143, CA, United States
| | - Barbara Stuart
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, 94143, CA, United States
| | - Susanna L Fryer
- Mental Health Service, San Francisco VA Medical Center, San Francisco, 94121, CA, United States
| | - Tara A Niendam
- Department of Psychiatry and Behavioral Sciences, University of California Davis, Davis, 95616, CA, United States
| | - Cameron S Carter
- Department of Psychiatry and Behavioral Sciences, University of California Davis, Davis, 95616, CA, United States
| | - Sophia Vinogradov
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, 55455, MN, United States
| | - Daniel H Mathalon
- Mental Health Service, San Francisco VA Medical Center, San Francisco, 94121, CA, United States; Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, 94143, CA, United States.
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44
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Yang C, Gao X, Liu N, Sun H, Gong Q, Yao L, Lui S. Convergent and distinct neural structural and functional patterns of mild cognitive impairment: a multimodal meta-analysis. Cereb Cortex 2023:7169132. [PMID: 37197764 DOI: 10.1093/cercor/bhad167] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 04/23/2023] [Accepted: 04/25/2023] [Indexed: 05/19/2023] Open
Abstract
Mild cognitive impairment (MCI) is regarded as a transitional stage between normal aging and Alzheimer's disease. Numerous voxel-based morphometry (VBM) and resting-state fMRI (rs-fMRI) studies have provided strong evidence of abnormalities in the structure and intrinsic function of brain regions in MCI. Studies have recently begun to explore their association but have not employed systematic information in this pursuit. Herein, a multimodal meta-analysis was performed, which included 43 VBM datasets (1,247 patients and 1,352 controls) of gray matter volume (GMV) and 42 rs-fMRI datasets (1,468 patients and 1,605 controls) that combined 3 metrics: amplitude of low-frequency fluctuation, the fractional amplitude of low-frequency fluctuation, and regional homogeneity. Compared to controls, patients with MCI displayed convergent reduced regional GMV and altered intrinsic activity, mainly in the default mode network and salience network. Decreased GMV alone in ventral medial prefrontal cortex and altered intrinsic function alone in bilateral dorsal anterior cingulate/paracingulate gyri, right lingual gyrus, and cerebellum were identified, respectively. This meta-analysis investigated complex patterns of convergent and distinct brain alterations impacting different neural networks in MCI patients, which contributes to a further understanding of the pathophysiology of MCI.
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Affiliation(s)
- Chengmin Yang
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Chengdu 610041, China
| | - Xin Gao
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Chengdu 610041, China
| | - Naici Liu
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Chengdu 610041, China
| | - Hui Sun
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Chengdu 610041, China
| | - Qiyong Gong
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Chengdu 610041, China
| | - Li Yao
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Chengdu 610041, China
| | - Su Lui
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Chengdu 610041, China
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Howes OD, Onwordi EC. The synaptic hypothesis of schizophrenia version III: a master mechanism. Mol Psychiatry 2023; 28:1843-1856. [PMID: 37041418 PMCID: PMC10575788 DOI: 10.1038/s41380-023-02043-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 03/16/2023] [Accepted: 03/20/2023] [Indexed: 04/13/2023]
Abstract
The synaptic hypothesis of schizophrenia has been highly influential. However, new approaches mean there has been a step-change in the evidence available, and some tenets of earlier versions are not supported by recent findings. Here, we review normal synaptic development and evidence from structural and functional imaging and post-mortem studies that this is abnormal in people at risk and with schizophrenia. We then consider the mechanism that could underlie synaptic changes and update the hypothesis. Genome-wide association studies have identified a number of schizophrenia risk variants converging on pathways regulating synaptic elimination, formation and plasticity, including complement factors and microglial-mediated synaptic pruning. Induced pluripotent stem cell studies have demonstrated that patient-derived neurons show pre- and post-synaptic deficits, synaptic signalling alterations, and elevated, complement-dependent elimination of synaptic structures compared to control-derived lines. Preclinical data show that environmental risk factors linked to schizophrenia, such as stress and immune activation, can lead to synapse loss. Longitudinal MRI studies in patients, including in the prodrome, show divergent trajectories in grey matter volume and cortical thickness compared to controls, and PET imaging shows in vivo evidence for lower synaptic density in patients with schizophrenia. Based on this evidence, we propose version III of the synaptic hypothesis. This is a multi-hit model, whereby genetic and/or environmental risk factors render synapses vulnerable to excessive glia-mediated elimination triggered by stress during later neurodevelopment. We propose the loss of synapses disrupts pyramidal neuron function in the cortex to contribute to negative and cognitive symptoms and disinhibits projections to mesostriatal regions to contribute to dopamine overactivity and psychosis. It accounts for the typical onset of schizophrenia in adolescence/early adulthood, its major risk factors, and symptoms, and identifies potential synaptic, microglial and immune targets for treatment.
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Affiliation(s)
- Oliver D Howes
- Faculty of Medicine, Institute of Clinical Sciences (ICS), Imperial College London, London, W12 0NN, UK.
- Psychiatric Imaging Group, Medical Research Council, London Institute of Medical Sciences, Hammersmith Hospital, London, W12 0NN, UK.
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK.
| | - Ellis Chika Onwordi
- Faculty of Medicine, Institute of Clinical Sciences (ICS), Imperial College London, London, W12 0NN, UK.
- Psychiatric Imaging Group, Medical Research Council, London Institute of Medical Sciences, Hammersmith Hospital, London, W12 0NN, UK.
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK.
- Centre for Psychiatry and Mental Health, Wolfson Institute of Population Health, Queen Mary University of London, London, E1 2AB, UK.
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46
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Yang W, Jin S, Duan W, Yu H, Ping L, Shen Z, Cheng Y, Xu X, Zhou C. The effects of childhood maltreatment on cortical thickness and gray matter volume: a coordinate-based meta-analysis. Psychol Med 2023; 53:1681-1699. [PMID: 36946124 DOI: 10.1017/s0033291723000661] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
Childhood maltreatment has been suggested to have an adverse impact on neurodevelopment, including microstructural brain abnormalities. Existing neuroimaging findings remain inconsistent and heterogeneous. We aim to explore the most prominent and robust cortical thickness (CTh) and gray matter volume (GMV) alterations associated with childhood maltreatment. A systematic search on relevant studies was conducted through September 2022. The whole-brain coordinate-based meta-analysis (CBMA) on CTh and GMV studies were conducted using the seed-based d mapping (SDM) software. Meta-regression analysis was subsequently applied to investigate potential associations between clinical variables and structural changes. A total of 45 studies were eligible for inclusion, including 11 datasets on CTh and 39 datasets on GMV, consisting of 2550 participants exposed to childhood maltreatment and 3739 unexposed comparison subjects. Individuals with childhood maltreatment exhibited overlapped deficits in the median cingulate/paracingulate gyri simultaneously revealed by both CTh and GM studies. Regional cortical thinning in the right anterior cingulate/paracingulate gyri and the left middle frontal gyrus, as well as GMV reductions in the left supplementary motor area (SMA) was also identified. No greater regions were found for either CTh or GMV. In addition, several neural morphology changes were associated with the average age of the maltreated individuals. The median cingulate/paracingulate gyri morphology might serve as the most robust neuroimaging feature of childhood maltreatment. The effects of early-life trauma on the human brain predominantly involved in cognitive functions, socio-affective functioning and stress regulation. This current meta-analysis enhanced the understanding of neuropathological changes induced by childhood maltreatment.
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Affiliation(s)
- Wei Yang
- Department of Psychiatry, Shandong Daizhuang Hospital, Jining, China
| | - Shushu Jin
- Department of Psychology, Affiliated Hospital of Jining Medical University, Jining, China
| | - Weiwei Duan
- School of Mental Health, Jining Medical University, Jining, China
| | - Hao Yu
- School of Mental Health, Jining Medical University, Jining, China
| | - Liangliang Ping
- Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China
| | - Zonglin Shen
- Department of Psychiatry, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yuqi Cheng
- Department of Psychiatry, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Xiufeng Xu
- Department of Psychiatry, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Cong Zhou
- Department of Psychology, Affiliated Hospital of Jining Medical University, Jining, China
- School of Mental Health, Jining Medical University, Jining, China
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47
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Investigating brain aging trajectory deviations in different brain regions of individuals with schizophrenia using multimodal magnetic resonance imaging and brain-age prediction: a multicenter study. Transl Psychiatry 2023; 13:82. [PMID: 36882419 PMCID: PMC9992684 DOI: 10.1038/s41398-023-02379-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 02/21/2023] [Accepted: 02/22/2023] [Indexed: 03/09/2023] Open
Abstract
Although many studies on brain-age prediction in patients with schizophrenia have been reported recently, none has predicted brain age based on different neuroimaging modalities and different brain regions in these patients. Here, we constructed brain-age prediction models with multimodal MRI and examined the deviations of aging trajectories in different brain regions of participants with schizophrenia recruited from multiple centers. The data of 230 healthy controls (HCs) were used for model training. Next, we investigated the differences in brain age gaps between participants with schizophrenia and HCs from two independent cohorts. A Gaussian process regression algorithm with fivefold cross-validation was used to train 90, 90, and 48 models for gray matter (GM), functional connectivity (FC), and fractional anisotropy (FA) maps in the training dataset, respectively. The brain age gaps in different brain regions for all participants were calculated, and the differences in brain age gaps between the two groups were examined. Our results showed that most GM regions in participants with schizophrenia in both cohorts exhibited accelerated aging, particularly in the frontal lobe, temporal lobe, and insula. The parts of the white matter tracts, including the cerebrum and cerebellum, indicated deviations in aging trajectories in participants with schizophrenia. However, no accelerated brain aging was noted in the FC maps. The accelerated aging in 22 GM regions and 10 white matter tracts in schizophrenia potentially exacerbates with disease progression. In individuals with schizophrenia, different brain regions demonstrate dynamic deviations of brain aging trajectories. Our findings provided more insights into schizophrenia neuropathology.
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Controlling the Impact of Helicobacter pylori-Related Hyperhomocysteinemia on Neurodegeneration. Medicina (B Aires) 2023; 59:medicina59030504. [PMID: 36984505 PMCID: PMC10056452 DOI: 10.3390/medicina59030504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 02/27/2023] [Accepted: 02/28/2023] [Indexed: 03/08/2023] Open
Abstract
Helicobacter pylori infection consists a high global burden affecting more than 50% of the world’s population. It is implicated, beyond substantiated local gastric pathologies, i.e., peptic ulcers and gastric cancer, in the pathophysiology of several neurodegenerative disorders, mainly by inducing hyperhomocysteinemia-related brain cortical thinning (BCT). BCT has been advocated as a possible biomarker associated with neurodegenerative central nervous system disorders such as Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, and/or glaucoma, termed as “ocular Alzheimer’s disease”. According to the infection hypothesis in relation to neurodegeneration, Helicobacter pylori as non-commensal gut microbiome has been advocated as trigger and/or mediator of neurodegenerative diseases, such as the development of Alzheimer’s disease. Among others, Helicobacter pylori-related inflammatory mediators, defensins, autophagy, vitamin D, dietary factors, role of probiotics, and some pathogenetic considerations including relevant involved genes are discussed within this opinion article. In conclusion, by controlling the impact of Helicobacter pylori-related hyperhomocysteinemia on neurodegenerative disorders might offer benefits, and additional research is warranted to clarify this crucial topic currently representing a major worldwide burden.
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Cao T, Pang JC, Segal A, Chen YC, Aquino KM, Breakspear M, Fornito A. Mode-based morphometry: A multiscale approach to mapping human neuroanatomy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.26.529328. [PMID: 36909539 PMCID: PMC10002616 DOI: 10.1101/2023.02.26.529328] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/02/2023]
Abstract
Voxel-based morphometry (VBM) and surface-based morphometry (SBM) are two widely used neuroimaging techniques for investigating brain anatomy. These techniques rely on statistical inferences at individual points (voxels or vertices), clusters of points, or a priori regions-of-interest. They are powerful tools for describing brain anatomy, but offer little insights into the generative processes that shape a particular set of findings. Moreover, they are restricted to a single spatial resolution scale, precluding the opportunity to distinguish anatomical variations that are expressed across multiple scales. Drawing on concepts from classical physics, here we develop an approach, called mode-based morphometry (MBM), that can describe any empirical map of anatomical variations in terms of the fundamental, resonant modes--eigenmodes--of brain anatomy, each tied to a specific spatial scale. Hence, MBM naturally yields a multiscale characterization of the empirical map, affording new opportunities for investigating the spatial frequency content of neuroanatomical variability. Using simulated and empirical data, we show that the validity and reliability of MBM are either comparable or superior to classical vertex-based SBM for capturing differences in cortical thickness maps between two experimental groups. Our approach thus offers a robust, accurate, and informative method for characterizing empirical maps of neuroanatomical variability that can be directly linked to a generative physical process.
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Affiliation(s)
- Trang Cao
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, 762-772 Blackburn Rd, Clayton VIC 3168, Australia
| | - James C Pang
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, 762-772 Blackburn Rd, Clayton VIC 3168, Australia
| | - Ashlea Segal
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, 762-772 Blackburn Rd, Clayton VIC 3168, Australia
| | - Yu-Chi Chen
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, 762-772 Blackburn Rd, Clayton VIC 3168, Australia
| | - Kevin M Aquino
- School of Physics, University of Sydney, Physics Rd, Camperdown NSW 2006, Australia
| | - Michael Breakspear
- School of Psychological Sciences, University of Newcastle, University Dr, Callaghan NSW 2308, Australia
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, 762-772 Blackburn Rd, Clayton VIC 3168, Australia
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50
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Luo L, Chen L, Wang Y, Li Q, He N, Li Y, You W, Wang Y, Long F, Guo L, Luo K, Sweeney JA, Gong Q, Li F. Patterns of brain dynamic functional connectivity are linked with attention-deficit/hyperactivity disorder-related behavioral and cognitive dimensions. Psychol Med 2023; 53:1-12. [PMID: 36748350 PMCID: PMC10600939 DOI: 10.1017/s0033291723000089] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 11/20/2022] [Accepted: 01/09/2023] [Indexed: 02/08/2023]
Abstract
BACKGROUND Attention-deficit/hyperactivity disorder (ADHD) is a clinically heterogeneous neurodevelopmental disorder defined by characteristic behavioral and cognitive features. Abnormal brain dynamic functional connectivity (dFC) has been associated with the disorder. The full spectrum of ADHD-related variation of brain dynamics and its association with behavioral and cognitive features remain to be established. METHODS We sought to identify patterns of brain dynamics linked to specific behavioral and cognitive dimensions using sparse canonical correlation analysis across a cohort of children with and without ADHD (122 children in total, 63 with ADHD). Then, using mediation analysis, we tested the hypothesis that cognitive deficits mediate the relationship between brain dynamics and ADHD-associated behaviors. RESULTS We identified four distinct patterns of dFC, each corresponding to a specific dimension of behavioral or cognitive function (r = 0.811-0.879). Specifically, the inattention/hyperactivity dimension was positively associated with dFC within the default mode network (DMN) and negatively associated with dFC between DMN and the sensorimotor network (SMN); the somatization dimension was positively associated with dFC within DMN and SMN; the inhibition and flexibility dimension and fluency and memory dimensions were both positively associated with dFC within DMN and between DMN and SMN, and negatively associated with dFC between DMN and the fronto-parietal network. Furthermore, we observed that cognitive functions of inhibition and flexibility mediated the relationship between brain dynamics and behavioral manifestations of inattention and hyperactivity. CONCLUSIONS These findings document the importance of distinct patterns of dynamic functional brain activity for different cardinal behavioral and cognitive features related to ADHD.
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Affiliation(s)
- Lekai Luo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China
| | - Lizhou Chen
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Functional and Molecular Imaging Key Laboratory of Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - Yuxia Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China
| | - Qian Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China
| | - Ning He
- Department of Psychiatry, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - Yuanyuan Li
- Department of Psychiatry, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - Wanfang You
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China
| | - Yaxuan Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China
| | - Fenghua Long
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China
| | - Lanting Guo
- Department of Psychiatry, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - Kui Luo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China
- National Engineering Research Center for Biomaterials, Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - John A. Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH 45219, USA
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen 361021, Fujian, P.R China
| | - Fei Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China
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