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, Yayuan Chen Department of Radiology, Tianjin Key Laboratory of Functional Imaging andTianjin Institute of Radiology, Tianjin Medical University General Hospital , Tianjin , China School of Medical Imaging, Tianjin Key Laboratory of Functional Imaging and The Province and Ministry Cosponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University , Tianjin , China Search for other works by this author on: Oxford Academic Sijia Wang Department of Radiology, Tianjin Key Laboratory of Functional Imaging andTianjin Institute of Radiology, Tianjin Medical University General Hospital , Tianjin , China Search for other works by this author on: Oxford Academic Xi Zhang School of Medical Imaging, Tianjin Key Laboratory of Functional Imaging and The Province and Ministry Cosponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University , Tianjin , China Search for other works by this author on: Oxford Academic Qingqing Yang Department of Radiology, TheFirst Affiliated Hospital, Zhejiang University School of Medicine , Hangzhou , China Search for other works by this author on: Oxford Academic Minghui Hua Department of Radiology, Chest Hospital, Tianjin University , Tianjin , China Search for other works by this author on: Oxford Academic Yifan Li School of Medical Imaging, Tianjin Key Laboratory of Functional Imaging and The Province and Ministry Cosponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University , Tianjin , China Search for other works by this author on: Oxford Academic Wen Qin Department of Radiology, Tianjin Key Laboratory of Functional Imaging andTianjin Institute of Radiology, Tianjin Medical University General Hospital , Tianjin , China Search for other works by this author on: Oxford Academic Feng Liu Department of Radiology, Tianjin Key Laboratory of Functional Imaging andTianjin Institute of Radiology, Tianjin Medical University General Hospital , Tianjin , China To whom correspondence should be addressed; Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin 300052, China; tel: +86 22 60362368,e-mail: fengliu@tmu.edu.cn (Feng Liu) Search for other works by this author on: Oxford Academic Meng Liang School of Medical Imaging, Tianjin Key Laboratory of Functional Imaging and The Province and Ministry Cosponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University , Tianjin , China To whom correspondence should be addressed; School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, No. 1 Guangdong Road, Hexi District, Tianjin 300203, China; tel: +86 22 83336090,e-mail: liangmeng@tmu.edu.com (Meng Liang) Search for other works by this author on: Oxford Academic
https://orcid.org/0000-0002-9121-8296 Schizophrenia Bulletin, sbae084, https://doi.org/10.1093/schbul/sbae084
Published:
31 May 2024
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Yayuan Chen, Sijia Wang, Xi Zhang, Qingqing Yang, Minghui Hua, Yifan Li, Wen Qin, Feng Liu, Meng Liang, Functional Connectivity-Based Searchlight Multivariate Pattern Analysis for Discriminating Schizophrenia Patients and Predicting Clinical Variables, Schizophrenia Bulletin, 2024;, sbae084, https://doi.org/10.1093/schbul/sbae084
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Abstract
Background
Schizophrenia, a multifaceted psychiatric disorder characterized by functional dysconnectivity, poses significant challenges in clinical practice. This study explores the potential of functional connectivity (FC)-based searchlight multivariate pattern analysis (CBS-MVPA) to discriminate between schizophrenia patients and healthy controls while also predicting clinical variables.
Study Design
We enrolled 112 schizophrenia patients and 119 demographically matched healthy controls. Resting-state functional magnetic resonance imaging data were collected, and whole-brain FC subnetworks were constructed. Additionally, clinical assessments and cognitive evaluations yielded a dataset comprising 36 clinical variables. Finally, CBS-MVPA was utilized to identify subnetworks capable of effectively distinguishing between the patient and control groups and predicting clinical scores.
Study Results
The CBS-MVPA approach identified 63 brain subnetworks exhibiting significantly high classification accuracies, ranging from 62.2% to 75.6%, in distinguishing individuals with schizophrenia from healthy controls. Among them, 5 specific subnetworks centered on the dorsolateral superior frontal gyrus, orbital part of inferior frontal gyrus, superior occipital gyrus, hippocampus, and parahippocampal gyrus showed predictive capabilities for clinical variables within the schizophrenia cohort.
Conclusion
This study highlights the potential of CBS-MVPA as a valuable tool for localizing the information related to schizophrenia in terms of brain network abnormalities and capturing the relationship between these abnormalities and clinical variables, andthus, deepens our understanding of the neurological mechanisms of schizophrenia.
searchlight MVPA, support machine learning, classification, clinical prediction, resting-state fMRI
© The Author(s) 2024. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.
This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/pages/standard-publication-reuse-rights)
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