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Zhao Q, Gao Z, Yu W, Xiao Y, Hu N, Wei X, Tao B, Zhu F, Li S, Lui S. Multivariate associations between neuroanatomy and cognition in unmedicated and medicated individuals with schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2024; 10:62. [PMID: 39004627 PMCID: PMC11247086 DOI: 10.1038/s41537-024-00482-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 06/28/2024] [Indexed: 07/16/2024]
Abstract
Previous studies that focused on univariate correlations between neuroanatomy and cognition in schizophrenia identified some inconsistent findings. Moreover, antipsychotic medication may impact the brain-behavior profiles in affected individuals. It remains unclear whether unmedicated and medicated individuals with schizophrenia would share common neuroanatomy-cognition associations. Therefore, we aimed to investigate multivariate neuroanatomy-cognition relationships in both groups. A sample of 59 drug-naïve individuals with first-episode schizophrenia (FES) and a sample of 115 antipsychotic-treated individuals with schizophrenia were finally included. Multivariate modeling was conducted in the two patient samples between multiple cognitive domains and neuroanatomic features, such as cortical thickness (CT), cortical surface area (CSA), and subcortical volume (SV). We observed distinct multivariate correlational patterns between the two samples of individuals with schizophrenia. In the FES sample, better performance in token motor, symbol coding, and verbal fluency tests was associated with greater thalamic volumes but lower CT in the prefrontal and anterior cingulate cortices. Two significant multivariate correlations were identified in antipsychotic-treated individuals: 1) worse verbal memory performance was related to smaller volumes for the most subcortical structures and smaller CSA mainly in the temporal regions and inferior parietal lobule; 2) a lower symbol coding test score was correlated with smaller CSA in the right parahippocampal gyrus but greater volume in the right caudate. These multivariate patterns were sample-specific and not confounded by imaging quality, illness duration, antipsychotic dose, or psychopathological symptoms. Our findings may help to understand the neurobiological basis of cognitive impairments and the development of cognition-targeted interventions.
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Affiliation(s)
- Qiannan Zhao
- 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
| | - Wei Yu
- 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
| | - Yuan Xiao
- 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
| | - Na Hu
- 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
| | - Xia Wei
- 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
| | - 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
| | - Fei Zhu
- 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
| | - Siyi 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
| | - 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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Du Y, Niu J, Xing Y, Li B, Calhoun VD. Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade. Schizophr Bull 2024:sbae110. [PMID: 38982882 DOI: 10.1093/schbul/sbae110] [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: 07/11/2024]
Abstract
BACKGROUND AND HYPOTHESIS Schizophrenia (SZ) is characterized by significant cognitive and behavioral disruptions. Neuroimaging techniques, particularly magnetic resonance imaging (MRI), have been widely utilized to investigate biomarkers of SZ, distinguish SZ from healthy conditions or other mental disorders, and explore biotypes within SZ or across SZ and other mental disorders, which aim to promote the accurate diagnosis of SZ. In China, research on SZ using MRI has grown considerably in recent years. STUDY DESIGN The article reviews advanced neuroimaging and artificial intelligence (AI) methods using single-modal or multimodal MRI to reveal the mechanism of SZ and promote accurate diagnosis of SZ, with a particular emphasis on the achievements made by Chinese scholars around the past decade. STUDY RESULTS Our article focuses on the methods for capturing subtle brain functional and structural properties from the high-dimensional MRI data, the multimodal fusion and feature selection methods for obtaining important and sparse neuroimaging features, the supervised statistical analysis and classification for distinguishing disorders, and the unsupervised clustering and semi-supervised learning methods for identifying neuroimage-based biotypes. Crucially, our article highlights the characteristics of each method and underscores the interconnections among various approaches regarding biomarker extraction and neuroimage-based diagnosis, which is beneficial not only for comprehending SZ but also for exploring other mental disorders. CONCLUSIONS We offer a valuable review of advanced neuroimage analysis and AI methods primarily focused on SZ research by Chinese scholars, aiming to promote the diagnosis, treatment, and prevention of SZ, as well as other mental disorders, both within China and internationally.
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Affiliation(s)
- Yuhui Du
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Ju Niu
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Ying Xing
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Bang Li
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Vince D Calhoun
- The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, 30303, GA, USA
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3
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Meyhoefer I, Sprenger A, Derad D, Grotegerd D, Leenings R, Leehr EJ, Breuer F, Surmann M, Rolfes K, Arolt V, Romer G, Lappe M, Rehder J, Koutsouleris N, Borgwardt S, Schultze-Lutter F, Meisenzahl E, Kircher TTJ, Keedy SS, Bishop JR, Ivleva EI, McDowell JE, Reilly JL, Hill SK, Pearlson GD, Tamminga CA, Keshavan MS, Gershon ES, Clementz BA, Sweeney JA, Hahn T, Dannlowski U, Lencer R. Evidence from comprehensive independent validation studies for smooth pursuit dysfunction as a sensorimotor biomarker for psychosis. Sci Rep 2024; 14:13859. [PMID: 38879556 PMCID: PMC11180169 DOI: 10.1038/s41598-024-64487-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 06/10/2024] [Indexed: 06/19/2024] Open
Abstract
Smooth pursuit eye movements are considered a well-established and quantifiable biomarker of sensorimotor function in psychosis research. Identifying psychotic syndromes on an individual level based on neurobiological markers is limited by heterogeneity and requires comprehensive external validation to avoid overestimation of prediction models. Here, we studied quantifiable sensorimotor measures derived from smooth pursuit eye movements in a large sample of psychosis probands (N = 674) and healthy controls (N = 305) using multivariate pattern analysis. Balanced accuracies of 64% for the prediction of psychosis status are in line with recent results from other large heterogenous psychiatric samples. They are confirmed by external validation in independent large samples including probands with (1) psychosis (N = 727) versus healthy controls (N = 292), (2) psychotic (N = 49) and non-psychotic bipolar disorder (N = 36), and (3) non-psychotic affective disorders (N = 119) and psychosis (N = 51) yielding accuracies of 65%, 66% and 58%, respectively, albeit slightly different psychosis syndromes. Our findings make a significant contribution to the identification of biologically defined profiles of heterogeneous psychosis syndromes on an individual level underlining the impact of sensorimotor dysfunction in psychosis.
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Affiliation(s)
- Inga Meyhoefer
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
- Otto-Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Muenster, Muenster, Germany
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Duesseldorf/LVR, Duesseldorf, Germany
| | - Andreas Sprenger
- Department of Neurology, University of Luebeck, Luebeck, Germany
| | - David Derad
- Department of Neurology, University of Luebeck, Luebeck, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Ramona Leenings
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Elisabeth J Leehr
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Fabian Breuer
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Marian Surmann
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Karen Rolfes
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Volker Arolt
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
- Otto-Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Muenster, Muenster, Germany
| | - Georg Romer
- Department of Child Adolescence Psychiatry and Psychotherapy, University of Muenster, Muenster, Germany
| | - Markus Lappe
- Otto-Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Muenster, Muenster, Germany
- Institute of Psychology, University of Muenster, Muenster, Germany
| | - Johanna Rehder
- Institute of Psychology, University of Muenster, Muenster, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University Munich, Munich, Germany
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Max-Planck-Institute of Psychiatry Munich, Munich, Germany
| | - Stefan Borgwardt
- Department of Psychiatry and Psychotherapy, University of Luebeck, Luebeck, Germany
- Department of Psychiatry, Psychiatric University Hospital, University of Basel, Basel, Switzerland
| | - Frauke Schultze-Lutter
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Duesseldorf/LVR, Duesseldorf, Germany
- Department of Psychology, Faculty of Psychology, Airlangga University, Surabaya, Indonesia
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Eva Meisenzahl
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Duesseldorf/LVR, Duesseldorf, Germany
| | - Tilo T J Kircher
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Sarah S Keedy
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, USA
| | - Jeffrey R Bishop
- Department of Experimental and Clinical Pharmacology and Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, USA
| | - Elena I Ivleva
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jennifer E McDowell
- Departments of Psychology and Neuroscience, Bio-Imaging Research Center, University of Georgia, Athens, GA, USA
| | - James L Reilly
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Scot Kristian Hill
- Department of Psychology, Rosalind Franklin University of Medicine and Science, Chicago, IL, USA
| | - Godfrey D Pearlson
- Departments of Psychiatry and Neuroscience, Yale School of Medicine, and Olin Research Center, Institute of Living/Hartford Hospital, Hartford, CT, USA
| | - Carol A Tamminga
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Matcheri S Keshavan
- Department of Psychiatry, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Elliot S Gershon
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, USA
| | - Brett A Clementz
- Departments of Psychology and Neuroscience, Bio-Imaging Research Center, University of Georgia, Athens, GA, USA
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, USA
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Rebekka Lencer
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany.
- Otto-Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Muenster, Muenster, Germany.
- Department of Psychiatry and Psychotherapy, University of Luebeck, Luebeck, Germany.
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4
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Wen J, Antoniades M, Yang Z, Hwang G, Skampardoni I, Wang R, Davatzikos C. Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning. Biol Psychiatry 2024:S0006-3223(24)01286-1. [PMID: 38718880 DOI: 10.1016/j.biopsych.2024.04.017] [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] [Received: 01/17/2024] [Revised: 03/29/2024] [Accepted: 04/22/2024] [Indexed: 05/21/2024]
Abstract
Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better understanding of disease heterogeneity by identifying disease subtypes with different brain phenotypic measures. In this review, we first present a systematic literature overview of studies using machine learning and multimodal magnetic resonance imaging to unravel disease heterogeneity in various neuropsychiatric and neurodegenerative disorders, including Alzheimer's disease, schizophrenia, major depressive disorder, autism spectrum disorder, and multiple sclerosis, as well as their potential in a transdiagnostic framework, where neuroanatomical and neurobiological commonalities were assessed across diagnostic boundaries. Subsequently, we summarize relevant machine learning methodologies and their clinical interpretability. We discuss the potential clinical implications of the current findings and envision future research avenues. Finally, we discuss an emerging paradigm called dimensional neuroimaging endophenotypes. Dimensional neuroimaging endophenotypes dissects the neurobiological heterogeneity of neuropsychiatric and neurodegenerative disorders into low-dimensional yet informative, quantitative brain phenotypic representations, serving as robust intermediate phenotypes (i.e., endophenotypes), presumably reflecting the interplay of underlying genetic, lifestyle, and environmental processes associated with disease etiology.
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Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science, University of Southern California, Los Angeles, California.
| | - Mathilde Antoniades
- Artificial Intelligence in Biomedical Imaging Laboratory, Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory, Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Gyujoon Hwang
- Psychiatry and Behavioral Medicine, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Ioanna Skampardoni
- Artificial Intelligence in Biomedical Imaging Laboratory, Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Rongguang Wang
- Artificial Intelligence in Biomedical Imaging Laboratory, Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory, Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
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5
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Luo C, Pi X, Zhang Q, Hu N, Xiao Y, Sweeney JA, Bishop JR, Gong Q, Xie D, Lui S. A subtype of schizophrenia patients with altered methylation level of genes related to immune cell activity. Psychol Med 2024:1-9. [PMID: 38505948 DOI: 10.1017/s0033291724000667] [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: 03/21/2024]
Abstract
BACKGROUND Epigenetic changes are plausible molecular sources of clinical heterogeneity in schizophrenia. A subgroup of schizophrenia patients with elevated inflammatory or immune-dysregulation has been reported by previous studies. However, little is known about epigenetic changes in genes related to immune activation in never-treated first-episode patients with schizophrenia (FES) and its consistency with that in treated long-term ill (LTS) patients. METHODS In this study, epigenome-wide profiling with a DNA methylation array was applied using blood samples of both FES and LTS patients, as well as their corresponding healthy controls. Non-negative matrix factorization (NMF) and k -means clustering were performed to parse heterogeneity of schizophrenia, and the consistency of subtyping results from two cohorts. was tested. RESULTS This study identified a subtype of patients in FES participants (47.5%) that exhibited widespread methylation level alterations of genes enriched in immune cell activity and a significantly higher proportion of neutrophils. This clustering of FES patients was validated in LTS patients, with high correspondence in epigenetic and clinical features across two cohorts. CONCLUSIONS In summary, this study demonstrated a subtype of schizophrenia patients across both FES and LTS cohorts, defined by widespread alterations in methylation profile of genes related to immune function and distinguishing clinical features. This finding illustrates the promise of novel treatment strategies targeting immune dysregulation for a subpopulation of schizophrenia patients.
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Affiliation(s)
- Chunyan Luo
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and molecular imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
- Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, China
| | - Xuenan Pi
- Laboratory of Omics Technology and Bioinformatics, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Qi Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and molecular imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
- Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, China
| | - Na Hu
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and molecular imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
- Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, China
| | - Yuan Xiao
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and molecular imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
- Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, China
| | - John A Sweeney
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati OH 45219, USA
| | - Jeffrey R Bishop
- Department of Experimental and Clinical Pharmacology and Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and molecular imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
- Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, China
| | - Dan Xie
- Laboratory of Omics Technology and Bioinformatics, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and molecular imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
- Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, China
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6
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Facal F, Arrojo M, Páramo M, Costas J. Association between psychiatric admissions in patients with schizophrenia and IL-6 plasma levels polygenic score. Eur Arch Psychiatry Clin Neurosci 2024:10.1007/s00406-024-01786-z. [PMID: 38492051 DOI: 10.1007/s00406-024-01786-z] [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] [Received: 07/13/2023] [Accepted: 02/16/2024] [Indexed: 03/18/2024]
Abstract
Schizophrenia diagnosis and admission history were associated with a polygenic score (PGS) for schizophrenia based on a subset of variants that act by modifying the expression of genes whose expression is also modified by antipsychotics. This gene set was enriched in cytokine production. Interleukin-6 (IL-6) is the only cytokine whose plasma levels were associated both with schizophrenia diagnosis and with acute decompensations in the largest meta-analysis. Therefore, we hypothesized that an IL-6 PGS, but not other cytokines PGSs, would be associated with schizophrenia chronicity/psychiatric admissions. Using the IL-6 PGS model from The PGS Catalog, IL-6 PGS was calculated in 427 patients with schizophrenia and data regarding admission history. Association between IL-6 PGS and chronicity, measured as number and duration of psychiatric admissions, or ever readmission was analyzed by multivariate ordinal and logistic regression, respectively. Specificity of results was assessed by analysis of PGSs from the other cytokines at The PGS Catalog with meta-analytic evidence of association with schizophrenia diagnosis or acute decompensations, IL-1RA, IL-4, IL-8, and IL-12. IL-6 PGS was associated with schizophrenia chronicity, explaining 1.51% of variability (OR = 1.29, 95% CI 1.07-1.55, P = 0.007). There was no association with ever readmission. Other cytokines PGSs were not associated with chronicity. Association with IL-6 PGS was independent of association with schizophrenia PGS. Our results provide evidence that genetically regulated higher levels of IL-6 are involved in schizophrenia chronicity, highlighting the relevance of immunity processes for a subgroup of patients.
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Affiliation(s)
- Fernando Facal
- Instituto de Investigación Sanitaria (IDIS) de Santiago de Compostela, Complexo Hospitalario Universitario de Santiago de Compostela (CHUS), Servizo Galego de Saúde (SERGAS), Hospital Clínico Universitario, edificio Consultas, Andar-2, 15706, Santiago de Compostela, Galicia, Spain.
- Servizo de Psiquiatría, Complexo Hospitalario Universitario de Santiago de Compostela, Servizo Galego de Saúde (SERGAS), Santiago de Compostela, Galicia, Spain.
| | - Manuel Arrojo
- Instituto de Investigación Sanitaria (IDIS) de Santiago de Compostela, Complexo Hospitalario Universitario de Santiago de Compostela (CHUS), Servizo Galego de Saúde (SERGAS), Hospital Clínico Universitario, edificio Consultas, Andar-2, 15706, Santiago de Compostela, Galicia, Spain
- Servizo de Psiquiatría, Complexo Hospitalario Universitario de Santiago de Compostela, Servizo Galego de Saúde (SERGAS), Santiago de Compostela, Galicia, Spain
| | - Mario Páramo
- Instituto de Investigación Sanitaria (IDIS) de Santiago de Compostela, Complexo Hospitalario Universitario de Santiago de Compostela (CHUS), Servizo Galego de Saúde (SERGAS), Hospital Clínico Universitario, edificio Consultas, Andar-2, 15706, Santiago de Compostela, Galicia, Spain
- Servizo de Psiquiatría, Complexo Hospitalario Universitario de Santiago de Compostela, Servizo Galego de Saúde (SERGAS), Santiago de Compostela, Galicia, Spain
| | - Javier Costas
- Instituto de Investigación Sanitaria (IDIS) de Santiago de Compostela, Complexo Hospitalario Universitario de Santiago de Compostela (CHUS), Servizo Galego de Saúde (SERGAS), Hospital Clínico Universitario, edificio Consultas, Andar-2, 15706, Santiago de Compostela, Galicia, Spain
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7
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Wen J, Antoniades M, Yang Z, Hwang G, Skampardoni I, Wang R, Davatzikos C. Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning. ARXIV 2024:arXiv:2401.09517v1. [PMID: 38313197 PMCID: PMC10836087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better understanding of disease heterogeneity by identifying disease subtypes that present significant differences in various brain phenotypic measures. In this review, we first present a systematic literature overview of studies using machine learning and multimodal MRI to unravel disease heterogeneity in various neuropsychiatric and neurodegenerative disorders, including Alzheimer's disease, schizophrenia, major depressive disorder, autism spectrum disorder, multiple sclerosis, as well as their potential in transdiagnostic settings. Subsequently, we summarize relevant machine learning methodologies and discuss an emerging paradigm which we call dimensional neuroimaging endophenotype (DNE). DNE dissects the neurobiological heterogeneity of neuropsychiatric and neurodegenerative disorders into a low-dimensional yet informative, quantitative brain phenotypic representation, serving as a robust intermediate phenotype (i.e., endophenotype) largely reflecting underlying genetics and etiology. Finally, we discuss the potential clinical implications of the current findings and envision future research avenues.
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Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Mathilde Antoniades
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Gyujoon Hwang
- Psychiatry and Behavioral Medicine, Medical College of Wisconsin, Watertown Plank Rd, Milwaukee, WI, USA
| | - Ioanna Skampardoni
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rongguang Wang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Wang C, Wang C, Ren Y, Zhang R, Ai L, Wu Y, Ran X, Wang M, Hu H, Shen J, Zhao Z, Yang Y, Ren W, Yu Y. Multi feature fusion network for schizophrenia classification and abnormal brain network recognition. Brain Res Bull 2024; 206:110848. [PMID: 38104673 DOI: 10.1016/j.brainresbull.2023.110848] [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: 10/19/2023] [Revised: 12/06/2023] [Accepted: 12/13/2023] [Indexed: 12/19/2023]
Abstract
Schizophrenia classification and abnormal brain network recognition have an important research significance. Researchers have proposed many classification methods based on machine learning and deep learning. However, fewer studies utilized the advantages of complementary information from multi feature to learn the best representation of schizophrenia. In this study, we proposed a multi-feature fusion network (MFFN) using functional network connectivity (FNC) and time courses (TC) to distinguish schizophrenia patients from healthy controls. DNN backbone was adopted to learn the feature map of functional network connectivity, C-RNNAM backbone was designed to learn the feature map of time courses, and Deep SHAP was applied to obtain the most discriminative brain networks. We proved the effectiveness of this proposed model using the combining two public datasets and evaluated this model quantitatively using the evaluation indexes. The results showed that the functional network connectivity generated by independent component analysis has advantage in schizophrenia classification by comparing static and dynamic functional connections. This method obtained the best classification accuracy (ACC=87.30%, SPE=89.28%, SEN=85.71%, F1 =88.23%, and AUC=0.9081), and it demonstrated the superiority of this proposed model by comparing state-of-the-art methods. Ablation experiment also demonstrated that multi feature fusion and attention module can improve classification accuracy. The most discriminative brain networks showed that default mode network and visual network of schizophrenia patients have aberrant connections in brain networks. In conclusion, this method can identify schizophrenia effectively and visualize the abnormal brain network, and it has important clinical application value.
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Affiliation(s)
- Chang Wang
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; Henan Key Laboratory of Biological Psychiatry, Xinxiang, China; School of Medical Engineering, Xinxiang Medical University, Xinxiang, China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China; Xinxiang Engineering Technology Research Center of Intelligent Medical Imaging Diagnosis, Xinxiang, China
| | - Chen Wang
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; Henan Key Laboratory of Biological Psychiatry, Xinxiang, China; School of Medical Engineering, Xinxiang Medical University, Xinxiang, China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China; Xinxiang Engineering Technology Research Center of Intelligent Medical Imaging Diagnosis, Xinxiang, China
| | - Yaning Ren
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; Henan Key Laboratory of Biological Psychiatry, Xinxiang, China; School of Medical Engineering, Xinxiang Medical University, Xinxiang, China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China; Xinxiang Engineering Technology Research Center of Intelligent Medical Imaging Diagnosis, Xinxiang, China
| | - Rui Zhang
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; Henan Key Laboratory of Biological Psychiatry, Xinxiang, China; School of Medical Engineering, Xinxiang Medical University, Xinxiang, China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China; Xinxiang Engineering Technology Research Center of Intelligent Medical Imaging Diagnosis, Xinxiang, China
| | - Lunpu Ai
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; Henan Key Laboratory of Biological Psychiatry, Xinxiang, China; School of Medical Engineering, Xinxiang Medical University, Xinxiang, China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
| | - Yang Wu
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; Henan Key Laboratory of Biological Psychiatry, Xinxiang, China; School of Medical Engineering, Xinxiang Medical University, Xinxiang, China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China; Xinxiang Engineering Technology Research Center of Intelligent Medical Imaging Diagnosis, Xinxiang, China
| | - Xiangying Ran
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; Henan Key Laboratory of Biological Psychiatry, Xinxiang, China; School of Medical Engineering, Xinxiang Medical University, Xinxiang, China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
| | - Mengke Wang
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; Henan Key Laboratory of Biological Psychiatry, Xinxiang, China; School of Medical Engineering, Xinxiang Medical University, Xinxiang, China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
| | - Heshun Hu
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; Henan Key Laboratory of Biological Psychiatry, Xinxiang, China; School of Medical Engineering, Xinxiang Medical University, Xinxiang, China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
| | - Jiefen Shen
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China; Xinxiang Engineering Technology Research Center of Intelligent Medical Imaging Diagnosis, Xinxiang, China
| | - Zongya Zhao
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; Henan Key Laboratory of Biological Psychiatry, Xinxiang, China; School of Medical Engineering, Xinxiang Medical University, Xinxiang, China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
| | - Yongfeng Yang
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; Henan Key Laboratory of Biological Psychiatry, Xinxiang, China
| | - Wenjie Ren
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
| | - Yi Yu
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; Henan Key Laboratory of Biological Psychiatry, Xinxiang, China; School of Medical Engineering, Xinxiang Medical University, Xinxiang, China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China.
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9
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Fritze S, Brandt GA, Benedyk A, Moldavski A, Geiger-Primo LS, Andoh J, Volkmer S, Braun U, Kubera KM, Wolf RC, von der Goltz C, Schwarz E, Meyer-Lindenberg A, Tost H, Hirjak D. Psychomotor slowing in schizophrenia is associated with cortical thinning of primary motor cortex: A three cohort structural magnetic resonance imaging study. Eur Neuropsychopharmacol 2023; 77:53-66. [PMID: 37717350 DOI: 10.1016/j.euroneuro.2023.08.499] [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: 07/08/2023] [Revised: 08/17/2023] [Accepted: 08/28/2023] [Indexed: 09/19/2023]
Abstract
Psychomotor slowing (PS) is characterized by slowed movements and lower activity levels. PS is frequently observed in schizophrenia (SZ) and distressing because it impairs performance of everyday tasks and social activities. Studying brain topography contributing to PS in SZ can help to understand the underlying neurobiological mechanisms as well as help to develop more effective treatments that specifically target affected brain areas. Here, we conducted structural magnetic resonance imaging (sMRI) of three independent cohorts of right-handed SZ patients (SZ#1: n = 72, SZ#2: n = 37, SZ#3: n = 25) and age, gender and education matched healthy controls (HC) (HC#1: n = 40, HC#2: n = 37, HC#3: n = 38). PS severity in the three SZ cohorts was determined using the Positive and Negative Syndrome Scale (PANSS) item #G7 (motor retardation) and Trail-Making-Test B (TMT-B). FreeSurfer v7.2 was used for automated parcellation and segmentation of cortical and subcortical regions. SZ#1 patients showed reduced cortical thickness in right precentral gyrus (M1; p = 0.04; Benjamini-Hochberg [BH] corr.). In SZ#1, cortical thinning in right M1 was associated with PANSS item #G7 (p = 0.04; BH corr.) and TMT-B performance (p = 0.002; BH corr.). In SZ#1, we found a significant correlation between PANSS item #G7 and TMT-B (p = 0.005, ρ=0.326). In conclusion, PANSS G#7 and TMT-B might have a surrogate value for predicting PS in SZ. Cortical thinning of M1 rather than alterations of subcortical structures may point towards cortical pathomechanism underlying PS in SZ.
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Affiliation(s)
- Stefan Fritze
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Geva A Brandt
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Anastasia Benedyk
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Germany
| | - Alexander Moldavski
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Germany
| | - Lena S Geiger-Primo
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Germany
| | - Jamila Andoh
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Germany
| | - Sebastian Volkmer
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Hector Institute for Artificial Intelligence in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Urs Braun
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Katharina M Kubera
- Center for Psychosocial Medicine, Department of General Psychiatry, University of Heidelberg, Heidelberg, Germany
| | - Robert C Wolf
- Center for Psychosocial Medicine, Department of General Psychiatry, University of Heidelberg, Heidelberg, Germany
| | | | - Emanuel Schwarz
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Hector Institute for Artificial Intelligence in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Andreas Meyer-Lindenberg
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Germany
| | - Heike Tost
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Germany
| | - Dusan Hirjak
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
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Ding Y, Ou Y, Yan H, Liu F, Li H, Li P, Xie G, Cui X, Guo W. Uncovering the Neural Correlates of Anhedonia Subtypes in Major Depressive Disorder: Implications for Intervention Strategies. Biomedicines 2023; 11:3138. [PMID: 38137360 PMCID: PMC10740577 DOI: 10.3390/biomedicines11123138] [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: 10/03/2023] [Revised: 11/10/2023] [Accepted: 11/21/2023] [Indexed: 12/24/2023] Open
Abstract
Major depressive disorder (MDD) represents a serious public health concern, negatively affecting individuals' quality of life and making a substantial contribution to the global burden of disease. Anhedonia is a core symptom of MDD and is associated with poor treatment outcomes. Variability in anhedonia components within MDD has been observed, suggesting heterogeneity in psychopathology across subgroups. However, little is known about anhedonia subgroups in MDD and their underlying neural correlates across subgroups. To address this question, we employed a hierarchical cluster analysis based on Temporal Experience of Pleasure Scale subscales in 60 first-episode, drug-naive MDD patients and 32 healthy controls. Then we conducted a connectome-wide association study and whole-brain voxel-wise functional analyses for identified subgroups. There were three main findings: (1) three subgroups with different anhedonia profiles were identified using a data mining approach; (2) several parts of the reward network (especially pallidum and dorsal striatum) were associated with anticipatory and consummatory pleasure; (3) different patterns of within- and between-network connectivity contributed to the disparities of anhedonia profiles across three MDD subgroups. Here, we show that anhedonia in MDD is not uniform and can be categorized into distinct subgroups, and our research contributes to the understanding of neural underpinnings, offering potential treatment directions. This work emphasizes the need for tailored approaches in the complex landscape of MDD. The identification of homogeneous, stable, and neurobiologically valid MDD subtypes could significantly enhance our comprehension and management of this multifaceted condition.
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Affiliation(s)
- Yudan Ding
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, China; (Y.D.); (H.Y.); (G.X.)
| | - Yangpan Ou
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, China; (Y.D.); (H.Y.); (G.X.)
| | - Haohao Yan
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, China; (Y.D.); (H.Y.); (G.X.)
| | - Feng Liu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China;
| | - Huabing Li
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha 410011, China;
| | - Ping Li
- Department of Psychiatry, Qiqihar Medical University, Qiqihar 161006, China;
| | - Guangrong Xie
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, China; (Y.D.); (H.Y.); (G.X.)
| | - Xilong Cui
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, China; (Y.D.); (H.Y.); (G.X.)
| | - Wenbin Guo
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, China; (Y.D.); (H.Y.); (G.X.)
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11
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Chai C, Ding H, Du X, Xie Y, Man W, Zhang Y, Ji Y, Liang M, Zhang B, Ning Y, Zhuo C, Yu C, Qin W. Dissociation between neuroanatomical and symptomatic subtypes in schizophrenia. Eur Psychiatry 2023; 66:e78. [PMID: 37702075 PMCID: PMC10594537 DOI: 10.1192/j.eurpsy.2023.2446] [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: 10/31/2022] [Revised: 05/20/2023] [Accepted: 08/01/2023] [Indexed: 09/14/2023] Open
Abstract
BACKGROUND Schizophrenia is a complex and heterogeneous syndrome with high clinical and biological stratification. Identifying distinctive subtypes can improve diagnostic accuracy and help precise therapy. A key challenge for schizophrenia subtyping is understanding the subtype-specific biological underpinnings of clinical heterogeneity. This study aimed to investigate if the machine learning (ML)-based neuroanatomical and symptomatic subtypes of schizophrenia are associated. METHODS A total of 314 schizophrenia patients and 257 healthy controls from four sites were recruited. Gray matter volume (GMV) and Positive and Negative Syndrome Scale (PANSS) scores were employed to recognize schizophrenia neuroanatomical and symptomatic subtypes using K-means and hierarchical methods, respectively. RESULTS Patients with ML-based neuroanatomical subtype-1 had focally increased GMV, and subtype-2 had widespread reduced GMV than the healthy controls based on either K-means or Hierarchical methods. In contrast, patients with symptomatic subtype-1 had severe PANSS scores than subtype-2. No differences in PANSS scores were shown between the two neuroanatomical subtypes; similarly, no GMV differences were found between the two symptomatic subtypes. Cohen's Kappa test further demonstrated an apparent dissociation between the ML-based neuroanatomical and symptomatic subtypes (P > 0.05). The dissociation patterns were validated in four independent sites with diverse disease progressions (chronic vs. first episodes) and ancestors (Chinese vs. Western). CONCLUSIONS These findings revealed a replicable dissociation between ML-based neuroanatomical and symptomatic subtypes of schizophrenia, which provides a new viewpoint toward understanding the heterogeneity of schizophrenia.
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Affiliation(s)
- Chao Chai
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Hao Ding
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Xiaotong Du
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yingying Xie
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Weiqi Man
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Yu Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yi Ji
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Meng Liang
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Bin Zhang
- Department of Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yuping Ning
- Department of Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Chuanjun Zhuo
- Department of Psychiatry, Tianjin Fourth Center Hospital, Tianjin, China
| | - Chunshui Yu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
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Shishido R, Kunii Y, Hino M, Izumi R, Nagaoka A, Hayashi H, Kakita A, Tomita H, Yabe H. Evidence for increased DNA damage repair in the postmortem brain of the high stress-response group of schizophrenia. Front Psychiatry 2023; 14:1183696. [PMID: 37674553 PMCID: PMC10478254 DOI: 10.3389/fpsyt.2023.1183696] [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: 03/10/2023] [Accepted: 06/12/2023] [Indexed: 09/08/2023] Open
Abstract
Background Schizophrenia (SZ) is a disorder diagnosed by specific symptoms and duration and is highly heterogeneous, clinically and pathologically. Although there are an increasing number of studies on the association between genetic and environmental factors in the development of SZ, the actual distribution of the population with different levels of influence of these factors has not yet been fully elucidated. In this study, we focused on stress as an environmental factor and stratified SZ based on the expression levels of stress-responsive molecules in the postmortem prefrontal cortex. Methods We selected the following stress-responsive molecules: interleukin (IL) -1β, IL-6, IL-10, tumor necrosis factor-α, interferon-γ, glucocorticoid receptor, brain-derived neurotrophic factor, synaptophysin, S100 calcium-binding protein B, superoxide dismutase, postsynaptic density protein 95, synuclein, apolipoprotein A1 (ApoA1), ApoA2, and solute carrier family 6 member 4. We performed RNA sequencing in the prefrontal gray matter of 25 SZ cases and 21 healthy controls and conducted a hierarchical cluster analysis of SZ based on the gene expression levels of stress-responsive molecules, which yielded two clusters. After assessing the validity of the clusters, they were designated as the high stress-response SZ group and the low stress-response SZ group, respectively. Ingenuity Pathway Analysis of differentially expressed genes (DEGs) between clusters was performed, and Terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL) staining was conducted on four cases each in the high and low stress-response SZ groups to validate DNA damage. Results We found higher prevalence of family history of SZ in the low stress-response SZ group (0/3 vs. 5/4, p = 0.04). Pathway analysis of DEGs between clusters showed the highest enrichment for DNA double-strand break repair. TUNEL staining showed a trend toward a lower percentage of TUNEL-positive cells in the high stress-response SZ group. Conclusion Our results suggest that there are subgroups of SZ with different degrees of stress impact. Furthermore, the pathophysiology of these subgroups may be associated with DNA damage repair. These results provide new insights into the interactions and heterogeneity between genetic and environmental factors.
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Affiliation(s)
- Risa Shishido
- Department of Neuropsychiatry, School of Medicine, Fukushima Medical University, Fukushima, Japan
| | - Yasuto Kunii
- Department of Neuropsychiatry, School of Medicine, Fukushima Medical University, Fukushima, Japan
- Department of Disaster Psychiatry, International Research Institute of Disaster Science, Tohoku University, Sendai, Japan
| | - Mizuki Hino
- Department of Neuropsychiatry, School of Medicine, Fukushima Medical University, Fukushima, Japan
- Department of Disaster Psychiatry, International Research Institute of Disaster Science, Tohoku University, Sendai, Japan
| | - Ryuta Izumi
- Department of Neuropsychiatry, School of Medicine, Fukushima Medical University, Fukushima, Japan
| | - Atsuko Nagaoka
- Department of Neuropsychiatry, School of Medicine, Fukushima Medical University, Fukushima, Japan
| | - Hideki Hayashi
- Department of Pathology, Brain Research Institute, Niigata University, Niigata, Japan
| | - Akiyoshi Kakita
- Department of Pathology, Brain Research Institute, Niigata University, Niigata, Japan
| | - Hiroaki Tomita
- Department of Disaster Psychiatry, International Research Institute of Disaster Science, Tohoku University, Sendai, Japan
| | - Hirooki Yabe
- Department of Neuropsychiatry, School of Medicine, Fukushima Medical University, Fukushima, Japan
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Xu XJ, Liu TL, He L, Pu B. Changes in neurotransmitter levels, brain structural characteristics, and their correlation with PANSS scores in patients with first-episode schizophrenia. World J Clin Cases 2023; 11:5215-5223. [PMID: 37621579 PMCID: PMC10445057 DOI: 10.12998/wjcc.v11.i22.5215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 06/15/2023] [Accepted: 07/10/2023] [Indexed: 08/04/2023] Open
Abstract
BACKGROUND In patients with schizophrenia, the brain structure and neurotransmitter levels change, which may be related to the occurrence and progression of this disease. AIM To explore the relationships between changes in neurotransmitters, brain structural characteristics, and the scores of the Positive and Negative Symptom Scale (PANSS) in patients with first-episode schizophrenia. METHODS The case group comprised 97 patients with schizophrenia, who were evaluated using the Canadian Neurological Scale and confirmed by laboratory tests at Ningbo Mental Hospital from January 2020 to July 2022. The control group comprised 100 healthy participants. For all participants, brain structural characteristics were explored by measuring brain dopamine (DA), glutamic acid (Glu), and gamma-aminobutyric acid (GABA) levels, with magnetic resonance imaging. The case group was divided into negative and positive symptom subgroups using PANSS scores for hierarchical analysis. Linear correlation analysis was used to analyze the correlations between neurotransmitters, brain structural characteristics, and PANSS scores. RESULTS Patients in the case group had higher levels of DA and lower levels of Glu and GABA, greater vertical and horizontal distances between the corpus callosum and the inferior part of the fornix and larger ventricle area than patients in the control group (P < 0.05). Patients with positive schizophrenia symptoms had significantly higher levels of DA, Glu, and GABA than those with negative symptoms (P < 0.05). In patients with positive schizophrenia symptoms, PANSS score was significantly positively correlated with DA, vertical and horizontal distances between the corpus callosum and the infrafornix, and ventricular area, and was significantly negatively correlated with Glu and GABA (P < 0.05). In patients with negative schizophrenia symptoms, PANSS score was significantly positively correlated with DA, vertical distance between the corpus callosum and the infrafornix, horizontal distance between the corpus callosum and the infrafornix, and ventricular area, and was significantly negatively correlated with Glu and GABA (P < 0.05). CONCLUSION In patients with first-episode schizophrenia, DA levels increased, Glu and GABA levels decreased, the thickness of the corpus callosum increased, and these variables were correlated with PANSS scores.
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Affiliation(s)
- Xian-Jia Xu
- The Fifth Ward, Ningbo Psychiatric Hospital, Ningbo 315000, Zhejiang Province, China
| | - Tang-Long Liu
- Department of Science and Education, Ningbo Psychiatric Hospital, Ningbo 315000, Zhejiang Province, China
| | - Liang He
- The Sixteenth Ward, Ningbo Psychiatric Hospital, Ningbo 315000, Zhejiang Province, China
| | - Ben Pu
- The Twelfth Ward, Ningbo Psychiatric Hospital, Ningbo 315000, Zhejiang Province, China
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Xu XJ, Liu TL, He L, Pu B. Changes in neurotransmitter levels, brain structural characteristics, and their correlation with PANSS scores in patients with first-episode schizophrenia. World J Clin Cases 2023; 11:5209-5217. [DOI: 10.12998/wjcc.v11.i22.5209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 06/15/2023] [Accepted: 07/10/2023] [Indexed: 08/03/2023] Open
Abstract
BACKGROUND In patients with schizophrenia, the brain structure and neurotransmitter levels change, which may be related to the occurrence and progression of this disease.
AIM To explore the relationships between changes in neurotransmitters, brain structural characteristics, and the scores of the Positive and Negative Symptom Scale (PANSS) in patients with first-episode schizophrenia.
METHODS The case group comprised 97 patients with schizophrenia, who were evaluated using the Canadian Neurological Scale and confirmed by laboratory tests at Ningbo Mental Hospital from January 2020 to July 2022. The control group comprised 100 healthy participants. For all participants, brain structural characteristics were explored by measuring brain dopamine (DA), glutamic acid (Glu), and gamma-aminobutyric acid (GABA) levels, with magnetic resonance imaging. The case group was divided into negative and positive symptom subgroups using PANSS scores for hierarchical analysis. Linear correlation analysis was used to analyze the correlations between neurotransmitters, brain structural characteristics, and PANSS scores.
RESULTS Patients in the case group had higher levels of DA and lower levels of Glu and GABA, greater vertical and horizontal distances between the corpus callosum and the inferior part of the fornix and larger ventricle area than patients in the control group (P < 0.05). Patients with positive schizophrenia symptoms had significantly higher levels of DA, Glu, and GABA than those with negative symptoms (P < 0.05). In patients with positive schizophrenia symptoms, PANSS score was significantly positively correlated with DA, vertical and horizontal distances between the corpus callosum and the infrafornix, and ventricular area, and was significantly negatively correlated with Glu and GABA (P < 0.05). In patients with negative schizophrenia symptoms, PANSS score was significantly positively correlated with DA, vertical distance between the corpus callosum and the infrafornix, horizontal distance between the corpus callosum and the infrafornix, and ventricular area, and was significantly negatively correlated with Glu and GABA (P < 0.05).
CONCLUSION In patients with first-episode schizophrenia, DA levels increased, Glu and GABA levels decreased, the thickness of the corpus callosum increased, and these variables were correlated with PANSS scores.
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Affiliation(s)
- Xian-Jia Xu
- The Fifth Ward, Ningbo Psychiatric Hospital, Ningbo 315000, Zhejiang Province, China
| | - Tang-Long Liu
- Department of Science and Education, Ningbo Psychiatric Hospital, Ningbo 315000, Zhejiang Province, China
| | - Liang He
- The Sixteenth Ward, Ningbo Psychiatric Hospital, Ningbo 315000, Zhejiang Province, China
| | - Ben Pu
- The Twelfth Ward, Ningbo Psychiatric Hospital, Ningbo 315000, Zhejiang Province, China
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15
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Palaniyappan L. Clusters of psychosis: compensation as a contributor to the heterogeneity of schizophrenia. J Psychiatry Neurosci 2023; 48:E325-E329. [PMID: 37643803 PMCID: PMC10473036 DOI: 10.1503/jpn.230120] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/31/2023] Open
Affiliation(s)
- Lena Palaniyappan
- From the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montréal, Que. and the Robarts Research Institute & Lawson Health Research Institute, London, Ont.
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16
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Gengeç Benli Ş, Andaç M. Constructing the Schizophrenia Recognition Method Employing GLCM Features from Multiple Brain Regions and Machine Learning Techniques. Diagnostics (Basel) 2023; 13:2140. [PMID: 37443534 DOI: 10.3390/diagnostics13132140] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 06/15/2023] [Accepted: 06/16/2023] [Indexed: 07/15/2023] Open
Abstract
Accurately diagnosing schizophrenia, a complex psychiatric disorder, is crucial for effectively managing the treatment process and methods. Various types of magnetic resonance (MR) images have the potential to serve as biomarkers for schizophrenia. The aim of this study is to numerically analyze differences in the textural characteristics that may occur in the bilateral amygdala, caudate, pallidum, putamen, and thalamus regions of the brain between individuals with schizophrenia and healthy controls via structural MR images. Towards this aim, Gray Level Co-occurence Matrix (GLCM) features obtained from five regions of the right, left, and bilateral brain were classified using machine learning methods. In addition, it was analyzed in which hemisphere these features were more distinctive and which method among Adaboost, Gradient Boost, eXtreme Gradient Boosting, Random Forest, k-Nearest Neighbors, Linear Discriminant Analysis (LDA), and Naive Bayes had higher classification success. When the results were examined, it was demonstrated that the GLCM features of these five regions in the left hemisphere could be classified as having higher performance in schizophrenia compared to healthy individuals. Using the LDA algorithm, classification success was achieved with a 100% AUC, 94.4% accuracy, 92.31% sensitivity, 100% specificity, and an F1 score of 91.9% in healthy and schizophrenic individuals. Thus, it has been revealed that the textural characteristics of the five predetermined regions, instead of the whole brain, are an important indicator in identifying schizophrenia.
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Affiliation(s)
- Şerife Gengeç Benli
- Department of Biomedical Engineering, Faculty of Engineering, Erciyes University, Kayseri 38280, Turkey
| | - Merve Andaç
- Department of Biomedical Engineering, Faculty of Engineering, Erciyes University, Kayseri 38280, Turkey
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17
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Dwyer DB, Chand GB, Pigoni A, Khuntia A, Wen J, Antoniades M, Hwang G, Erus G, Doshi J, Srinivasan D, Varol E, Kahn RS, Schnack HG, Meisenzahl E, Wood SJ, Zhuo C, Sotiras A, Shinohara RT, Shou H, Fan Y, Schaulfelberger M, Rosa P, Lalousis PA, Upthegrove R, Kaczkurkin AN, Moore TM, Nelson B, Gur RE, Gur RC, Ritchie MD, Satterthwaite TD, Murray RM, Di Forti M, Ciufolini S, Zanetti MV, Wolf DH, Pantelis C, Crespo-Facorro B, Busatto GF, Davatzikos C, Koutsouleris N, Dazzan P. Psychosis brain subtypes validated in first-episode cohorts and related to illness remission: results from the PHENOM consortium. Mol Psychiatry 2023; 28:2008-2017. [PMID: 37147389 PMCID: PMC10575777 DOI: 10.1038/s41380-023-02069-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 03/15/2023] [Accepted: 04/05/2023] [Indexed: 05/07/2023]
Abstract
Using machine learning, we recently decomposed the neuroanatomical heterogeneity of established schizophrenia to discover two volumetric subgroups-a 'lower brain volume' subgroup (SG1) and an 'higher striatal volume' subgroup (SG2) with otherwise normal brain structure. In this study, we investigated whether the MRI signatures of these subgroups were also already present at the time of the first-episode of psychosis (FEP) and whether they were related to clinical presentation and clinical remission over 1-, 3-, and 5-years. We included 572 FEP and 424 healthy controls (HC) from 4 sites (Sao Paulo, Santander, London, Melbourne) of the PHENOM consortium. Our prior MRI subgrouping models (671 participants; USA, Germany, and China) were applied to both FEP and HC. Participants were assigned into 1 of 4 categories: subgroup 1 (SG1), subgroup 2 (SG2), no subgroup membership ('None'), and mixed SG1 + SG2 subgroups ('Mixed'). Voxel-wise analyses characterized SG1 and SG2 subgroups. Supervised machine learning analyses characterized baseline and remission signatures related to SG1 and SG2 membership. The two dominant patterns of 'lower brain volume' in SG1 and 'higher striatal volume' (with otherwise normal neuromorphology) in SG2 were identified already at the first episode of psychosis. SG1 had a significantly higher proportion of FEP (32%) vs. HC (19%) than SG2 (FEP, 21%; HC, 23%). Clinical multivariate signatures separated the SG1 and SG2 subgroups (balanced accuracy = 64%; p < 0.0001), with SG2 showing higher education but also greater positive psychosis symptoms at first presentation, and an association with symptom remission at 1-year, 5-year, and when timepoints were combined. Neuromorphological subtypes of schizophrenia are already evident at illness onset, separated by distinct clinical presentations, and differentially associated with subsequent remission. These results suggest that the subgroups may be underlying risk phenotypes that could be targeted in future treatment trials and are critical to consider when interpreting neuroimaging literature.
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Affiliation(s)
- Dominic B Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany.
- Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia.
- Orygen, Melbourne, VIC, Australia.
| | - Ganesh B Chand
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Alessandro Pigoni
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
- Social and Affective Neuroscience Group, MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Adyasha Khuntia
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
- Max-Planck Institute of Psychiatry, Munich, Germany
| | - Junhao Wen
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mathilde Antoniades
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Gyujoon Hwang
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jimit Doshi
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dhivya Srinivasan
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Erdem Varol
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Statistics, Zuckerman Institute, Columbia University, New York, NY, USA
| | - Rene S Kahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hugo G Schnack
- Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands
| | - Eva Meisenzahl
- LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany
| | - Stephen J Wood
- Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia
- Orygen, Melbourne, VIC, Australia
- University of Birmingham, Edgbaston, UK
| | - Chuanjun Zhuo
- Department of Psychiatric-Neuroimaging-Genetics and Co-morbidity Laboratory (PNGC-Lab), Nankai University Affiliated Tianjin Anding Hospital; Department of Psychiatry, Tianjin Medical University, Tianjin, China
| | - Aristeidis Sotiras
- Department of Radiology and Institute for Informatics, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Russell T Shinohara
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Haochang Shou
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Pedro Rosa
- Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil
| | - Paris A Lalousis
- Institute for Mental Health and Centre for Brain Health, University of Birmingham, Birmingham, UK
| | - Rachel Upthegrove
- Institute for Mental Health and Centre for Brain Health, University of Birmingham, Birmingham, UK
- Early Intervention Service, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK
| | | | - Tyler M Moore
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Barnaby Nelson
- Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia
- Orygen, Melbourne, VIC, Australia
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Marylyn D Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D Satterthwaite
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Robin M Murray
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Marta Di Forti
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Simone Ciufolini
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Marcus V Zanetti
- Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil
- Hospital Sírio-Libanês, São Paulo, Brazil
| | - Daniel H Wolf
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
| | - Benedicto Crespo-Facorro
- Mental Health Service, Hospital Universitario Virgen del Rocío, Seville, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III (CIBERSAM), Madrid, Spain
- Instituto de Biomedicina de Sevilla (IBiS), Seville, Spain
- Department of Psychiatry, Universidad de Sevilla, Seville, Spain
| | - Geraldo F Busatto
- Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany.
- Max-Planck Institute of Psychiatry, Munich, Germany.
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK.
| | - Paola Dazzan
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK.
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18
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McCutcheon RA, Keefe RSE, McGuire PK. Cognitive impairment in schizophrenia: aetiology, pathophysiology, and treatment. Mol Psychiatry 2023; 28:1902-1918. [PMID: 36690793 PMCID: PMC10575791 DOI: 10.1038/s41380-023-01949-9] [Citation(s) in RCA: 50] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 01/03/2023] [Accepted: 01/06/2023] [Indexed: 01/25/2023]
Abstract
Cognitive deficits are a core feature of schizophrenia, account for much of the impaired functioning associated with the disorder and are not responsive to existing treatments. In this review, we first describe the clinical presentation and natural history of these deficits. We then consider aetiological factors, highlighting how a range of similar genetic and environmental factors are associated with both cognitive function and schizophrenia. We then review the pathophysiological mechanisms thought to underlie cognitive symptoms, including the role of dopamine, cholinergic signalling and the balance between GABAergic interneurons and glutamatergic pyramidal cells. Finally, we review the clinical management of cognitive impairments and candidate novel treatments.
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Affiliation(s)
- Robert A McCutcheon
- Department of Psychiatry, University of Oxford, Oxford, UK.
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, London, UK.
- Oxford health NHS Foundation Trust, Oxford health NHS Foundation Trust, Oxford, UK.
| | - Richard S E Keefe
- Departments of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA
| | - Philip K McGuire
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford health NHS Foundation Trust, Oxford health NHS Foundation Trust, Oxford, UK
- NIHR Oxford Health Biomedical Research Centre, Oxford, UK
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19
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Wei X, Dong S, Su Z, Tang L, Zhao P, Pan C, Wang F, Tang Y, Zhang W, Zhang X. NetMoST: A network-based machine learning approach for subtyping schizophrenia using polygenic SNP allele biomarkers. ARXIV 2023:arXiv:2302.00104v2. [PMID: 36776814 PMCID: PMC9915719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 02/14/2023]
Abstract
Subtyping neuropsychiatric disorders like schizophrenia is essential for improving the diagnosis and treatment of complex diseases. Subtyping schizophrenia is challenging because it is polygenic and genetically heterogeneous, rendering the standard symptom-based diagnosis often unreliable and unrepeatable. We developed a novel network-based machine-learning approach, netMoST, to subtyping psychiatric disorders. NetMoST identifies polygenic risk SNP-allele modules from genome-wide genotyping data as polygenic haplotype biomarkers (PHBs) for disease subtyping. We applied netMoST to subtype a cohort of schizophrenia subjects into three distinct biotypes with differentiable genetic, neuroimaging and functional characteristics. The PHBs of the first biotype (36.9% of all patients) were related to neurodevelopment and cognition, the PHBs of the second biotype (28.4%) were enriched for neuroimmune functions, and the PHBs of the third biotype (34.7%) were associated with the transport of calcium ions and neurotransmitters. Neuroimaging patterns provided additional support to the new biotypes, with unique regional homogeneity (ReHo) patterns observed in the brains of each biotype compared with healthy controls. Our findings demonstrated netMoST's capability for uncovering novel biotypes of complex diseases such as schizophrenia. The results also showed the power of exploring polygenic allelic patterns that transcend the conventional GWAS approaches.
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Affiliation(s)
- Xinru Wei
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu 210001, China
| | - Shuai Dong
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu 210001, China
| | - Zhao Su
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu 210001, China
| | - Lili Tang
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China
| | - Pengfei Zhao
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China
| | - Chunyu Pan
- School of Computer Science and Engineering, Northeastern University, Shenyang, China
| | - Fei Wang
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China
| | - Yanqing Tang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
- Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, China
- Department of Gerontology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Weixiong Zhang
- Department of Health Technology and Informatics, Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China
| | - Xizhe Zhang
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu 210001, China
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20
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Gao Z, Xiao Y, Zhu F, Tao B, Yu W, Lui S. The whole-brain connectome landscape in patients with schizophrenia: a systematic review and meta-analysis of graph theoretical characteristics. Neurosci Biobehav Rev 2023; 148:105144. [PMID: 36990373 DOI: 10.1016/j.neubiorev.2023.105144] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 03/14/2023] [Accepted: 03/24/2023] [Indexed: 03/29/2023]
Abstract
The alterations of connectome in schizophrenia have been reported, but the results remain inconsistent. We conducted a systematic review and random-effects meta-analysis on structural or functional connectome MRI studies comparing global graph theoretical characteristics between schizophrenia and healthy controls. Meta-regression and subgroup analyses were performed to examine confounding effects. Based on the included 48 studies, Structural connectome in schizophrenia showed a significant decrease in segregation (lower clustering coefficient and local efficiency, Hedge's g= -0.352 and -0.864, respectively) and integration (higher characteristic path length and lower global efficiency, Hedge's g= 0.532 and -0.577 respectively). The functional connectome showed no difference between groups except γ. Moderator analysis indicated that clinical and methodological factors exerted a potential effect on the graph theoretical characteristics. Our analysis revealed a weaker small-worldization trend in structural connectome of schizophrenia. For the relatively unchanged functional connectome, more homogenous and high-quality studies are warranted to elucidate whether the change was blurred by heterogeneity or the presentation of pathophysiological reconfiguration.
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21
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Chen J, Patil KR, Yeo BTT, Eickhoff SB. Leveraging Machine Learning for Gaining Neurobiological and Nosological Insights in Psychiatric Research. Biol Psychiatry 2023; 93:18-28. [PMID: 36307328 DOI: 10.1016/j.biopsych.2022.07.025] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 07/06/2022] [Accepted: 07/28/2022] [Indexed: 11/18/2022]
Abstract
Much attention is currently devoted to developing diagnostic classifiers for mental disorders. Complementing these efforts, we highlight the potential of machine learning to gain biological insights into the psychopathology and nosology of mental disorders. Studies to this end have mainly used brain imaging data, which can be obtained noninvasively from large cohorts and have repeatedly been argued to reveal potentially intermediate phenotypes. This may become particularly relevant in light of recent efforts to identify magnetic resonance imaging-derived biomarkers that yield insight into pathophysiological processes as well as to refine the taxonomy of mental illness. In particular, the accuracy of machine learning models may be used as dependent variables to identify features relevant to pathophysiology. Moreover, such approaches may help disentangle the dimensional (within diagnosis) and often overlapping (across diagnoses) symptomatology of psychiatric illness. We also point out a multiview perspective that combines data from different sources, bridging molecular and system-level information. Finally, we summarize recent efforts toward a data-driven definition of subtypes or disease entities through unsupervised and semisupervised approaches. The latter, blending unsupervised and supervised concepts, may represent a particularly promising avenue toward dissecting heterogeneous categories. Finally, we raise several technical and conceptual aspects related to the reviewed approaches. In particular, we discuss common pitfalls pertaining to flawed input data or analytic procedures that would likely lead to unreliable outputs.
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Affiliation(s)
- Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China; Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-universität Düsseldorf, Düsseldorf, Germany
| | - B T Thomas Yeo
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Integrative Sciences & Engineering Programme, National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-universität Düsseldorf, Düsseldorf, Germany
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22
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Li Q, Yao L, You W, Liu J, Deng S, Li B, Luo L, Zhao Y, Wang Y, Wang Y, Zhang Q, Long F, Sweeney JA, Gu S, Li F, Gong Q. Controllability of Functional Brain Networks and Its Clinical Significance in First-Episode Schizophrenia. Schizophr Bull 2022; 49:659-668. [PMID: 36402458 PMCID: PMC10154712 DOI: 10.1093/schbul/sbac177] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
BACKGROUND AND HYPOTHESIS Disrupted control of brain state transitions may contribute to the diverse dysfunctions of cognition, emotion, and behavior that are fundamental to schizophrenia. Control theory provides the rationale for evaluating brain state transitions from a controllability perspective, which may help reveal the brain mechanism for clinical features such as cognitive control deficits associated with schizophrenia. We hypothesized that brain controllability would be altered in patients with schizophrenia, and that controllability of brain networks would be related to clinical symptomatology. STUDY DESIGN Controllability measurements of functional brain networks, including average controllability and modal controllability, were calculated and compared between 125 first-episode never-treated patients with schizophrenia and 133 healthy controls (HCs). Associations between controllability metrics and clinical symptoms were evaluated using sparse canonical correlation analysis. STUDY RESULTS Compared to HCs, patients showed significantly increased average controllability (PFDR = .023) and decreased modal controllability (PFDR = .023) in dorsal anterior cingulate cortex (dACC). General psychopathology symptoms and positive symptoms were positively correlated with average controllability in regions of default mode network and negatively associated with average controllability in regions of sensorimotor, dorsal attention, and frontoparietal networks. CONCLUSIONS Our findings suggest that altered controllability of functional activity in dACC may play a critical role in the pathophysiology of schizophrenia, consistent with the importance of this region in cognitive and brain state control operations. The demonstration of associations of functional controllability with psychosis symptoms suggests that the identified alterations in average controllability of brain function may contribute to the severity of acute psychotic illness in schizophrenia.
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Affiliation(s)
- 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.,Functional and Molecular Imaging Key Laboratory, Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - Li Yao
- 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.,Functional and Molecular Imaging Key Laboratory, 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.,Functional and Molecular Imaging Key Laboratory, Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - Jiang Liu
- Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Shikuang Deng
- Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Bin Li
- Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - 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.,Functional and Molecular Imaging Key Laboratory, Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - Youjin Zhao
- 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.,Functional and Molecular Imaging Key Laboratory, 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.,Functional and Molecular Imaging Key Laboratory, Sichuan University, 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.,Functional and Molecular Imaging Key Laboratory, Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - Qian Zhang
- 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.,Functional and Molecular Imaging Key Laboratory, Sichuan University, 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.,Functional and Molecular Imaging Key Laboratory, 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
| | - Shi Gu
- Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 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.,Functional and Molecular Imaging Key Laboratory, Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - Qiyong Gong
- 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.,Functional and Molecular Imaging Key Laboratory, Sichuan University, Chengdu 610041, Sichuan, P.R. China
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23
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Zhao Q, Cao H, Zhang W, Li S, Xiao Y, Tamminga CA, Keshavan MS, Pearlson GD, Clementz BA, Gershon ES, Hill SK, Keedy SK, Ivleva EI, Lencer R, Sweeney JA, Gong Q, Lui S. A subtype of institutionalized patients with schizophrenia characterized by pronounced subcortical and cognitive deficits. Neuropsychopharmacology 2022; 47:2024-2032. [PMID: 35260788 PMCID: PMC9556672 DOI: 10.1038/s41386-022-01300-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 01/28/2022] [Accepted: 02/19/2022] [Indexed: 02/05/2023]
Abstract
Some patients with schizophrenia have severe cognitive impairment and functional deficits that require long-term institutional care. The patterns of brain-behavior alterations in these individuals, and their differences from patients living successfully in the community, remain poorly understood. Previous cognition-based studies for stratifying schizophrenia patients highlight the importance of subcortical structures in the context of illness heterogeneity. In the present study, subcortical volumes from 96 institutionalized patients with long-term schizophrenia were evaluated using cluster analysis to test for heterogeneity. These data were compared to those from two groups of community-dwelling individuals with schizophrenia for comparison purposes, including 68 long-term ill and 126 first-episode individuals. A total of 290 demographically matched healthy participants were included as normative references at a 1:1 ratio for each patient sample. A subtype of institutionalized patients was identified based on their pattern of subcortical alterations. Using a machine learning algorithm developed to discriminate the two groups of institutionalized patients, all three patient samples were found to have similar rates of patients assigned to the two subtypes (approximately 50% each). In institutionalized patients, only the subtype with the identified pattern of subcortical alterations had greater neocortical and cognitive abnormalities than those in the similarity classified community-dwelling patients with long-term illness. Thus, for the subtype of patients with a distinctive pattern of subcortical alterations, when the distinct pattern of subcortical alterations is present and particularly severe, it is associated with cognitive impairments that may contribute to persistent disability and institutionalization.
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Affiliation(s)
- Qiannan Zhao
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan Province, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Hengyi Cao
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA
| | - Wenjing Zhang
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan Province, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Siyi Li
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan Province, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Yuan Xiao
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan Province, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Carol A Tamminga
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Matcheri S Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Godfrey D Pearlson
- Departments of Psychiatry and Neurobiology, Yale University and Olin Neuropsychiatric Research Center, Hartford, CT, USA
| | - Brett A Clementz
- Department of Psychology, University of Georgia, Athens, GA, USA
| | - Elliot S Gershon
- Department of Psychiatry, University of Chicago, Chicago, IL, USA
| | - Scot Kristian Hill
- Department of Psychology, Rosalind Franklin University of Medicine and Science, Chicago, IL, USA
| | - Sarah K Keedy
- Department of Psychiatry, University of Chicago, Chicago, IL, USA
| | - Elena I Ivleva
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Rebekka Lencer
- Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany
| | - John A Sweeney
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Qiyong Gong
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China.
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan Province, China.
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China.
| | - Su Lui
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China.
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan Province, China.
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China.
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24
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Zhou H, Wang D, Cao B, Zhang X. Association of reduced cortical thickness and psychopathological symptoms in patients with first-episode drug-naïve schizophrenia. Int J Psychiatry Clin Pract 2022; 27:42-50. [PMID: 36193901 DOI: 10.1080/13651501.2022.2129067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/10/2022]
Abstract
OBJECTIVE There is growing evidence that reduced cortical thickness has been considered to be a central abnormality in schizophrenia. Brain imaging studies have demonstrated that the cerebral cortex becomes thinner in patients with first-episode schizophrenia. This study aimed to examine whether cortical thickness is altered in drug-naïve schizophrenia in a Chinese Han population and the relationship between cortical thickness and clinical symptoms. METHODS We compared cortical thickness in 41 schizophrenia patients and 30 healthy controls. Psychopathology of patients with schizophrenia was assessed using the Positive and Negative Syndrome Scale (PANSS). RESULTS The cortical thickness of left banks of superior temporal sulcus, left lateral occipital gyrus, left rostral middle frontal gyrus, right inferior parietal lobule and right lateral occipital gyrus in schizophrenia patients was generally thinner compared with healthy controls. Correlation analysis revealed a negative correlation between cortical thickness of the left banks of superior temporal sulcus and general psychopathology of PANSS. CONCLUSIONS Our results suggest that cortical thickness abnormalities are already present early in the onset of schizophrenia and are associated with psychopathological symptoms, suggesting that it plays an important role in the pathogenesis and symptomatology of schizophrenia.Key points(1) The first-episode drug-naïve schizophrenia had reduced cortical thickness than the controls.(2) Cortical thickness was associated with psychopathological symptoms in patients with schizophrenia.
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Affiliation(s)
- Huixia Zhou
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, PR China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Dongmei Wang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, PR China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Bo Cao
- Department of Psychiatry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Canada.,Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Xiangyang Zhang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, PR China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
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25
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Rantala MJ, Luoto S, Borráz-León JI, Krams I. Schizophrenia: the new etiological synthesis. Neurosci Biobehav Rev 2022; 142:104894. [PMID: 36181926 DOI: 10.1016/j.neubiorev.2022.104894] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 08/25/2022] [Accepted: 09/25/2022] [Indexed: 10/31/2022]
Abstract
Schizophrenia has been an evolutionary paradox: it has high heritability, but it is associated with decreased reproductive success. The causal genetic variants underlying schizophrenia are thought to be under weak negative selection. To unravel this paradox, many evolutionary explanations have been suggested for schizophrenia. We critically discuss the constellation of evolutionary hypotheses for schizophrenia, highlighting the lack of empirical support for most existing evolutionary hypotheses-with the exception of the relatively well supported evolutionary mismatch hypothesis. It posits that evolutionarily novel features of contemporary environments, such as chronic stress, low-grade systemic inflammation, and gut dysbiosis, increase susceptibility to schizophrenia. Environmental factors such as microbial infections (e.g., Toxoplasma gondii) can better predict the onset of schizophrenia than polygenic risk scores. However, researchers have not been able to explain why only a small minority of infected people develop schizophrenia. The new etiological synthesis of schizophrenia indicates that an interaction between host genotype, microbe infection, and chronic stress causes schizophrenia, with neuroinflammation and gut dysbiosis mediating this etiological pathway. Instead of just alleviating symptoms with drugs, the parasite x genotype x stress model emphasizes that schizophrenia treatment should focus on detecting and treating possible underlying microbial infection(s), neuroinflammation, gut dysbiosis, and chronic stress.
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Affiliation(s)
- Markus J Rantala
- Department of Biology, University of Turku, FIN-20014 Turku, Finland.
| | - Severi Luoto
- School of Population Health, University of Auckland, 1023 Auckland, New Zealand
| | | | - Indrikis Krams
- Institute of Ecology and Earth Sciences, University of Tartu, 51014 Tartu, Estonia; Department of Zoology and Animal Ecology, Faculty of Biology, University of Latvia, 1004, Rīga, Latvia
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26
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Zeng J, Zhang W, Wu G, Wang X, Shah C, Li S, Xiao Y, Yao L, Cao H, Li Z, Sweeney JA, Lui S, Gong Q. Effects of Antipsychotic Medications and Illness Duration on Brain Features That Distinguish Schizophrenia Patients. Schizophr Bull 2022; 48:1354-1362. [PMID: 35925035 PMCID: PMC9673268 DOI: 10.1093/schbul/sbac094] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
BACKGROUND AND HYPOTHESIS Previous studies have reported effects of antipsychotic treatment and illness duration on brain features. This study used a machine learning approach to examine whether these factors in aggregate impacted the utility of MRI features for differentiating individual schizophrenia patients from healthy controls. STUDY DESIGN This case-control study used patients with never-treated first-episode schizophrenia (FES, n = 179) and long-term ill schizophrenia (LTSZ, n = 30), with follow-up of the FES group after treatment (n = 71), a group of patients who had received long-term antipsychotic treatment (n = 93) and age and sex-matched healthy controls (n = 373) for each patient group. A multiple kernel learning classifier combining both structural and functional brain features was used to discriminate individual patients from controls. STUDY RESULTS MRI features differentiated untreated FES (0.73) and LTSZ (0.83) patients from healthy controls with moderate accuracy, but accuracy was significantly higher in antipsychotic-treated FES (0.94) and LTSZ (0.98) patients. Treatment was associated with significantly increased accuracy of case identification in both early course and long-term ill patients (both p < .001). Effects of illness duration, examined separately in treated and untreated patients, were less robust. CONCLUSIONS Our results demonstrate that initiation of antipsychotic treatment alters brain features in ways that further distinguish individual schizophrenia patients from healthy individuals, and have a modest effect of illness duration. Intrinsic illness-related brain alterations in untreated patients, regardless of illness duration, are not sufficiently robust for accurate identification of schizophrenia patients.
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Affiliation(s)
| | | | - Guorong Wu
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, China
| | - Xiaowan Wang
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, China
| | - Chandan Shah
- Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Siyi Li
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Yuan Xiao
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Li Yao
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Hengyi Cao
- Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China,Center for Psychiatry Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY, USA,Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA
| | - Zhenlin Li
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - John A Sweeney
- Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China,Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Su Lui
- To whom correspondence should be addressed; Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China, tel/fax: +86-28-85423960; e-mail:
| | - Qiyong Gong
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China,Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
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27
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Childers E, Bowen EFW, Rhodes CH, Granger R. Immune-Related Genomic Schizophrenic Subtyping Identified in DLPFC Transcriptome. Genes (Basel) 2022; 13:genes13071200. [PMID: 35885983 PMCID: PMC9319783 DOI: 10.3390/genes13071200] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 05/26/2022] [Accepted: 06/29/2022] [Indexed: 02/04/2023] Open
Abstract
Well-documented evidence of the physiologic, genetic, and behavioral heterogeneity of schizophrenia suggests that diagnostic subtyping may clarify the underlying pathobiology of the disorder. Recent studies have demonstrated that increased inflammation may be a prominent feature of a subset of schizophrenics. However, these findings are inconsistent, possibly due to evaluating schizophrenics as a single group. In this study, we segregated schizophrenic patients into two groups (“Type 1”, “Type 2”) by their gene expression in the dorsolateral prefrontal cortex and explored biological differences between the subgroups. The study included post-mortem tissue samples that were sequenced in multiple, publicly available gene datasets using different sequencing methods. To evaluate the role of inflammation, the expression of genes in multiple components of neuroinflammation were examined: complement cascade activation, glial cell activation, pro-inflammatory mediator secretion, blood–brain barrier (BBB) breakdown, chemokine production and peripheral immune cell infiltration. The Type 2 schizophrenics showed widespread abnormal gene expression across all the neuroinflammation components that was not observed in Type 1 schizophrenics. Our results demonstrate the importance of separating schizophrenic patients into their molecularly defined subgroups and provide supporting evidence for the involvement of the immune-related pathways in a schizophrenic subset.
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Affiliation(s)
- Eva Childers
- Dartmouth College, Hanover, NH 03755, USA; (E.C.); (E.F.W.B.)
| | | | | | - Richard Granger
- Dartmouth College, Hanover, NH 03755, USA; (E.C.); (E.F.W.B.)
- Correspondence:
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28
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Dwyer DB, Buciuman MO, Ruef A, Kambeitz J, Sen Dong M, Stinson C, Kambeitz-Ilankovic L, Degenhardt F, Sanfelici R, Antonucci LA, Lalousis PA, Wenzel J, Urquijo-Castro MF, Popovic D, Oeztuerk OF, Haas SS, Weiske J, Hauke D, Neufang S, Schmidt-Kraepelin C, Ruhrmann S, Penzel N, Lichtenstein T, Rosen M, Chisholm K, Riecher-Rössler A, Egloff L, Schmidt A, Andreou C, Hietala J, Schirmer T, Romer G, Michel C, Rössler W, Maj C, Borisov O, Krawitz PM, Falkai P, Pantelis C, Lencer R, Bertolino A, Borgwardt S, Noethen M, Brambilla P, Schultze-Lutter F, Meisenzahl E, Wood SJ, Davatzikos C, Upthegrove R, Salokangas RKR, Koutsouleris N. Clinical, Brain, and Multilevel Clustering in Early Psychosis and Affective Stages. JAMA Psychiatry 2022; 79:677-689. [PMID: 35583903 PMCID: PMC9118078 DOI: 10.1001/jamapsychiatry.2022.1163] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Importance Approaches are needed to stratify individuals in early psychosis stages beyond positive symptom severity to investigate specificity related to affective and normative variation and to validate solutions with premorbid, longitudinal, and genetic risk measures. Objective To use machine learning techniques to cluster, compare, and combine subgroup solutions using clinical and brain structural imaging data from early psychosis and depression stages. Design, Setting, and Participants A multisite, naturalistic, longitudinal cohort study (10 sites in 5 European countries; including major follow-up intervals at 9 and 18 months) with a referred patient sample of those with clinical high risk for psychosis (CHR-P), recent-onset psychosis (ROP), recent-onset depression (ROD), and healthy controls were recruited between February 1, 2014, to July 1, 2019. Data were analyzed between January 2020 and January 2022. Main Outcomes and Measures A nonnegative matrix factorization technique separately decomposed clinical (287 variables) and parcellated brain structural volume (204 gray, white, and cerebrospinal fluid regions) data across CHR-P, ROP, ROD, and healthy controls study groups. Stability criteria determined cluster number using nested cross-validation. Validation targets were compared across subgroup solutions (premorbid, longitudinal, and schizophrenia polygenic risk scores). Multiclass supervised machine learning produced a transferable solution to the validation sample. Results There were a total of 749 individuals in the discovery group and 610 individuals in the validation group. Individuals included those with CHR-P (n = 287), ROP (n = 323), ROD (n = 285), and healthy controls (n = 464), The mean (SD) age was 25.1 (5.9) years, and 702 (51.7%) were female. A clinical 4-dimensional solution separated individuals based on positive symptoms, negative symptoms, depression, and functioning, demonstrating associations with all validation targets. Brain clustering revealed a subgroup with distributed brain volume reductions associated with negative symptoms, reduced performance IQ, and increased schizophrenia polygenic risk scores. Multilevel results distinguished between normative and illness-related brain differences. Subgroup results were largely validated in the external sample. Conclusions and Relevance The results of this longitudinal cohort study provide stratifications beyond the expression of positive symptoms that cut across illness stages and diagnoses. Clinical results suggest the importance of negative symptoms, depression, and functioning. Brain results suggest substantial overlap across illness stages and normative variation, which may highlight a vulnerability signature independent from specific presentations. Premorbid, longitudinal, and genetic risk validation suggested clinical importance of the subgroups to preventive treatments.
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Affiliation(s)
- Dominic B Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Madalina-Octavia Buciuman
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,International Max-Planck Research School for Translational Psychiatry, Munich, Germany
| | - Anne Ruef
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
| | - Joseph Kambeitz
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Mark Sen Dong
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
| | - Caedyn Stinson
- Max-Planck School of Cognition, Leipzig, Germany.,Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany
| | - Lana Kambeitz-Ilankovic
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Franziska Degenhardt
- Institute of Human Genetics, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany.,Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Rachele Sanfelici
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Max-Planck Institute of Psychiatry, Munich, Germany
| | - Linda A Antonucci
- Department of Education, Psychology, Communication, University of Bari Aldo Moro, Bari, Italy
| | - Paris Alexandros Lalousis
- Institute for Mental Health and Centre for Brain Health, University of Birmingham, Birmingham, United Kingdom
| | - Julian Wenzel
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | | | - David Popovic
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,International Max-Planck Research School for Translational Psychiatry, Munich, Germany
| | - Oemer Faruk Oeztuerk
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,International Max-Planck Research School for Translational Psychiatry, Munich, Germany
| | - Shalaila S Haas
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Johanna Weiske
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
| | - Daniel Hauke
- Department of Psychiatry (Psychiatric University Hospital, UPK), University of Basel, Basel, Switzerland.,Early Intervention Service, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, United Kingdom.,Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland
| | - Susanne Neufang
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
| | | | - Stephan Ruhrmann
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Nora Penzel
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Theresa Lichtenstein
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Marlene Rosen
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Katharine Chisholm
- Institute for Mental Health and Centre for Brain Health, University of Birmingham, Birmingham, United Kingdom.,Department of Psychology, Aston University, Birmingham, United Kingdom
| | | | - Laura Egloff
- Department of Psychiatry (Psychiatric University Hospital, UPK), University of Basel, Basel, Switzerland
| | - André Schmidt
- Department of Psychiatry (Psychiatric University Hospital, UPK), University of Basel, Basel, Switzerland
| | - Christina Andreou
- Department of Psychiatry (Psychiatric University Hospital, UPK), University of Basel, Basel, Switzerland
| | - Jarmo Hietala
- Department of Psychiatry, University of Turku, Turku, Finland
| | - Timo Schirmer
- GE Healthcare GmbH (previously GE Global Research GmbH), Munich, Germany
| | - Georg Romer
- Department of Child and Adolescent Psychiatry, University of Münster, Münster, Germany
| | - Chantal Michel
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Wulf Rössler
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry Zurich, Zurich, Switzerland
| | - Carlo Maj
- Institute of Genomic Statistics and Bioinformatics, University of Bonn, Bonn, Germany
| | - Oleg Borisov
- Institute of Genomic Statistics and Bioinformatics, University of Bonn, Bonn, Germany
| | - Peter M Krawitz
- Institute of Genomic Statistics and Bioinformatics, University of Bonn, Bonn, Germany
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Max-Planck Institute of Psychiatry, Munich, Germany
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, University of Melbourne & Melbourne Health, Melbourne, Victoria, Australia
| | - Rebekka Lencer
- Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany.,Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Alessandro Bertolino
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Stefan Borgwardt
- Department of Psychiatry (Psychiatric University Hospital, UPK), University of Basel, Basel, Switzerland.,Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Markus Noethen
- Institute of Human Genetics, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milano, Italy.,Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Frauke Schultze-Lutter
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany.,University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland.,Department of Psychology, Faculty of Psychology, Airlangga University, Surabaya, Indonesia
| | | | - Stephen J Wood
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia.,Orygen, the National Centre of Excellence for Youth Mental Health, Melbourne, Victoria, Australia
| | - Christos Davatzikos
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia.,Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Rachel Upthegrove
- Institute for Mental Health and Centre for Brain Health, University of Birmingham, Birmingham, United Kingdom.,Early Intervention Service, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, United Kingdom
| | | | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Max-Planck Institute of Psychiatry, Munich, Germany.,Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
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29
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Zhao Q, Li J, Xiao Y, Cao H, Wang X, Zhang W, Li S, Liao W, Gong Q, Lui S. Distinct neuroanatomic subtypes in antipsychotic-treated patients with schizophrenia classified by the predefined classification in a never-treated sample. PSYCHORADIOLOGY 2021; 1:212-224. [PMID: 38666223 PMCID: PMC11025559 DOI: 10.1093/psyrad/kkab018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 11/17/2021] [Accepted: 12/01/2021] [Indexed: 02/05/2023]
Abstract
Background Distinct neuroanatomic subtypes have been identified in never-treated patients with schizophrenia based on cerebral structural abnormalities, but whether antipsychotic-treated patients would be stratified under the guidance of such previously formed classification remains unclear. Objective The present study aimed to investigate alterations of brain structures in antipsychotic-treated patients with schizophrenia based on a predefined morphological classification and their relationships with cognitive performance. Methods Cortical thickness, surface area, and subcortical volume were extracted from 147 antipsychotic-treated patients with schizophrenia using structural magnetic resonance imaging for classification. The Brief Assessment of Cognition in Schizophrenia (BACS) and Positive and Negative Syndrome Scale (PANSS) were used to assess cognition and symptoms. Results Antipsychotic-treated patients were categorized into three subtypes with distinct patterns of brain morphological alterations. Subtypes 1 and 2 were characterized by widespread deficits in cortical thickness but relatively limited deficits in surface area. In contrast, subtype 3 demonstrated cortical thickening mainly in parietal-occipital regions and widespread deficits in surface area. All three subgroups demonstrated cognitive deficits compared with healthy controls. Significant associations between neuroanatomic and cognitive abnormalities were only observed in subtype 1, where cortical thinning in the left lingual gyrus was conversely related to symbol coding performance. Conclusions Similar to drug-naïve patients, neuroanatomic heterogeneity exists in antipsychotic-treated patients, with disparate associations with cognition. These findings promote our understanding of relationships between neuroanatomic abnormalities and cognitive performance in the context of heterogeneity. Moreover, these results suggest that neurobiological heterogeneity needs to be considered in cognitive research in schizophrenia.
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Affiliation(s)
- Qiannan Zhao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Jiao Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yuan Xiao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Hengyi Cao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY 11030, United States
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY 11004, United States
| | - Xiao Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Wenjing Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Siyi Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu 610041, China
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