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Di Camillo F, Grimaldi DA, Cattarinussi G, Di Giorgio A, Locatelli C, Khuntia A, Enrico P, Brambilla P, Koutsouleris N, Sambataro F. Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis. Psychiatry Clin Neurosci 2024. [PMID: 39290174 DOI: 10.1111/pcn.13736] [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: 04/29/2024] [Revised: 07/31/2024] [Accepted: 08/19/2024] [Indexed: 09/19/2024]
Abstract
BACKGROUND Recent advances in multivariate pattern recognition have fostered the search for reliable neuroimaging-based biomarkers in psychiatric conditions, including schizophrenia. These approaches consider the complex pattern of alterations in brain function and structure, overcoming the limitations of traditional univariate methods. To assess the reliability of neuroimaging-based biomarkers and the contribution of study characteristics in distinguishing individuals with schizophrenia spectrum disorder (SSD) from healthy controls (HCs), we conducted a systematic review of the studies that used multivariate pattern recognition for this objective. METHODS We systematically searched PubMed, Scopus, and Web of Science for studies on SSD classification using multivariate pattern analysis on magnetic resonance imaging data. We employed a bivariate random-effects meta-analytic model to explore the classification of sensitivity (SE) and specificity (SP) across studies while also evaluating the moderator effects of clinical and non-clinical variables. RESULTS A total of 119 studies (with 12,723 patients with SSD and 13,196 HCs) were identified. The meta-analysis estimated a SE of 79.1% (95% confidence interval [CI], 77.1%-81.0%) and a SP of 80.0% (95% CI, 77.8%-82.0%). In particular, the Positive and Negative Syndrome Scale and the Global Assessment of Functioning scores, age, age of onset, duration of untreated psychosis, deep learning, algorithm type, features selection, and validation methods had significant effects on classification performance. CONCLUSIONS Multivariate pattern analysis reliably identifies neuroimaging-based biomarkers of SSD, achieving ∼80% SE and SP. Despite clinical heterogeneity, discernible brain modifications effectively differentiate SSD from HCs. Classification performance depends on patient-related and methodological factors crucial for the development, validation, and application of prospective models in clinical settings.
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Affiliation(s)
- Fabio Di Camillo
- Department of Neuroscience (DNS), University of Padova, Padua, Italy
| | | | - Giulia Cattarinussi
- Department of Neuroscience (DNS), University of Padova, Padua, Italy
- Padova Neuroscience Center, University of Padova, Padua, Italy
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Annabella Di Giorgio
- Department of Mental Health and Addictions, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - Clara Locatelli
- Department of Mental Health and Addictions, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - Adyasha Khuntia
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
- Max-Planck-Institute of Psychiatry, Munich, Germany
| | - Paolo Enrico
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
- Department of Neurosciences and Mental Health, Fondazione IRCSS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Paolo Brambilla
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
- Department of Neurosciences and Mental Health, Fondazione IRCSS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Nikolaos Koutsouleris
- Max-Planck-Institute of Psychiatry, Munich, Germany
- Department of Psychiatry, Munich University Hospital, Munich, Germany
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Fabio Sambataro
- Department of Neuroscience (DNS), University of Padova, Padua, Italy
- Padova Neuroscience Center, University of Padova, Padua, Italy
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Li J, Wang D, Xia J, Zhang C, Meng Y, Xu S, Chen H, Liao W. Divergent suicidal symptomatic activations converge on somato-cognitive action network in depression. Mol Psychiatry 2024; 29:1980-1989. [PMID: 38351174 PMCID: PMC11408245 DOI: 10.1038/s41380-024-02450-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/19/2024] [Accepted: 01/23/2024] [Indexed: 02/16/2024]
Abstract
Individuals with depression have the highest lifetime prevalence of suicide attempts (SA) among mental illnesses. Numerous neuroimaging studies have developed biomarkers from task-related neural activation in depressive patients with SA, but the findings are inconsistent. Empowered by the contemporary interconnected view of depression as a neural system disorder, we sought to identify a specific brain circuit utilizing published heterogeneous neural activations. We systematically reviewed all published cognitive and emotional task-related functional MRI studies that investigated differences in the location of neural activations between depressive patients with and without SA. We subsequently mapped an underlying brain circuit functionally connecting to each experimental activation using a large normative connectome database (n = 1000). The identified SA-related functional network was compared to the network derived from the disease control group. Finally, we decoded this convergent functional connectivity network using microscale transcriptomic and chemo-architectures, and macroscale psychological processes. We enrolled 11 experimental tasks from eight studies, including depressive patients with SA (n = 147) and without SA (n = 196). The heterogeneous SA-related neural activations localized to the somato-cognitive action network (SCAN), exhibiting robustness to little perturbations and specificity for depression. Furthermore, the SA-related functional network was colocalized with brain-wide gene expression involved in inflammatory and immunity-related biological processes and aligned with the distribution of the GABA and noradrenaline neurotransmitter systems. The findings demonstrate that the SA-related functional network of depression is predominantly located at the SCAN, which is an essential implication for understanding depressive patients with SA.
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Affiliation(s)
- 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, 611731, P.R. China.
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China.
| | - Dajing Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Jie Xia
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Chao Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Yao Meng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Shuo Xu
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China.
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. 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, 611731, P.R. China.
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China.
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Wang Y, Yang Y, Xu W, Yao X, Xie X, Zhang L, Sun J, Wang L, Hua Q, He K, Tian Y, Wang K, Ji GJ. Heterogeneous Brain Abnormalities in Schizophrenia Converge on a Common Network Associated With Symptom Remission. Schizophr Bull 2024; 50:545-556. [PMID: 38253437 PMCID: PMC11059819 DOI: 10.1093/schbul/sbae003] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
BACKGROUND AND HYPOTHESIS There is a huge heterogeneity of magnetic resonance imaging findings in schizophrenia studies. Here, we hypothesized that brain regions identified by structural and functional imaging studies of schizophrenia could be reconciled in a common network. STUDY DESIGN We systematically reviewed the case-control studies that estimated the brain morphology or resting-state local function for schizophrenia patients in the literature. Using the healthy human connectome (n = 652) and a validated technique "coordinate network mapping" to identify a common brain network affected in schizophrenia. Then, the specificity of this schizophrenia network was examined by independent data collected from 13 meta-analyses. The clinical relevance of this schizophrenia network was tested on independent data of medication, neuromodulation, and brain lesions. STUDY RESULTS We identified 83 morphological and 60 functional studies comprising 7389 patients with schizophrenia and 7408 control subjects. The "coordinate network mapping" showed that the atrophy and dysfunction coordinates were functionally connected to a common network although they were spatially distant from each other. Taking all 143 studies together, we identified the schizophrenia network with hub regions in the bilateral anterior cingulate cortex, insula, temporal lobe, and subcortical structures. Based on independent data from 13 meta-analyses, we showed that these hub regions were specifically connected with regions of cortical thickness changes in schizophrenia. More importantly, this schizophrenia network was remarkably aligned with regions involving psychotic symptom remission. CONCLUSIONS Neuroimaging abnormalities in cross-sectional schizophrenia studies converged into a common brain network that provided testable targets for developing precise therapies.
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Affiliation(s)
- Yingru Wang
- Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Yinian Yang
- Department of Clinical Psychiatry, School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
| | - Wenqiang Xu
- Department of Clinical Psychiatry, School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
| | - Xiaoqing Yao
- Department of Clinical Psychiatry, School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
| | - Xiaohui Xie
- Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Long Zhang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Jinmei Sun
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Lu Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Qiang Hua
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Kongliang He
- Department of Psychiatry, Fourth People’s Hospital of Hefei, Anhui Mental Health Center, Hefei, China
| | - Yanghua Tian
- Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Kai Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders,Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China
- Anhui Institute of Translational Medicine, Hefei, China
| | - Gong-Jun Ji
- Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
- Department of Clinical Psychiatry, School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders,Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China
- Anhui Institute of Translational Medicine, Hefei, China
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Zhang M, Xie X, Zhan Y, Jiang Y, Bai T, Zhang L, Wang K, Ji GJ, Tian Y. Sensory symptoms relieved by navigated rTMS, targeted using lesion network mapping based on human brain connectome, in a patient after thalamic stroke. Neurophysiol Clin 2024; 54:102953. [PMID: 38382138 DOI: 10.1016/j.neucli.2024.102953] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 01/31/2024] [Accepted: 02/03/2024] [Indexed: 02/23/2024] Open
Affiliation(s)
- Mengdan Zhang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei 230032, China
| | - Xiaohui Xie
- Department of Neurology, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, China
| | - Yuqian Zhan
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei 230032, China
| | - Yu Jiang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei 230032, China
| | - Tongjian Bai
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei 230032, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province 230032, China
| | - Long Zhang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei 230032, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province 230032, China
| | - Kai Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei 230032, China; The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei 230032, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province 230032, China.
| | - Gong-Jun Ji
- The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei 230032, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province 230032, China.
| | - Yanghua Tian
- Department of Neurology, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, China; Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, China; The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei 230032, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province 230032, China.
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