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Unveiling the potential of machine learning in schizophrenia diagnosis: A meta-analytic study of task-based neuroimaging data
Wang, Xuan1,2,3,4,5; Yan, Chao1,2; Yang, Peng-yuan6; Xia, Zheng1; Cai, Xin-lu7,8; Wang, Yi3,4,5; Kwok, Sze Chai1,2,9,10; Chan, Raymond C. K.3,4,5
第一作者Xuan Wang
通讯作者邮箱cyan@psy.ecnu.edu.cn (chao yan)
心理所单位排序4
摘要

The emergence of machine learning (ML) techniques has opened up new avenues for identifying biomarkers associated with schizophrenia (SCZ) using task-related fMRI (t-fMRI) designs. To evaluate the effectiveness of this approach, we conducted a comprehensive meta-analysis of 31 t-fMRI studies using a bivariate model. Our findings revealed a high overall sensitivity of 0.83 and specificity of 0.82 for t-fMRI studies. Notably, neuropsychological domains modulated the classification performance, with selective attention demonstrating a significantly higher specificity than working memory (beta = 0.98, z = 2.11, P = 0.04). Studies involving older, chronic patients with SCZ reported higher sensitivity (P <0.015) and specificity (P <0.001) than those involving younger, first-episode patients or high-risk individuals for psychosis. Additionally, we found that the severity of negative symptoms was positively associated with the specificity of the classification model (beta = 7.19, z = 2.20, P = 0.03). Taken together, these results support the potential of using task-based fMRI data in combination with machine learning techniques to identify biomarkers related to symptom outcomes in SCZ, providing a promising avenue for improving diagnostic accuracy and treatment efficacy. Future attempts to deploy ML classification should consider the factors of algorithm choice, data quality and quantity, as well as issues related to generalization.

关键词attention machine learning meta-analysis schizophrenia task-based fMRI
2023-12-29
DOI10.1111/pcn.13625
发表期刊PSYCHIATRY AND CLINICAL NEUROSCIENCES
ISSN1323-1316
页码12
期刊论文类型实证研究
收录类别SCI
资助项目MOE (Ministry of Education of China) Project of Humanities and Social Sciences ; National Natural Science Foundation of China[32171084] ; Natural Science Foundation of Shanghai[21ZR1421000] ; Philip K. H. Foundation ; [20YJC190025] ; [2021ZD0200800]
出版者WILEY
WOS关键词NEGATIVE SYMPTOMS ; HIGH-RISK ; FUNCTIONAL CONNECTIVITY ; LATENT INHIBITION ; BRAIN NETWORKS ; PSYCHOSIS ; CLASSIFICATION ; FMRI ; INDIVIDUALS ; ABNORMALITIES
WOS研究方向Neurosciences & Neurology ; Psychiatry
WOS类目Clinical Neurology ; Neurosciences ; Psychiatry
WOS记录号WOS:001134068300001
WOS分区Q1
资助机构MOE (Ministry of Education of China) Project of Humanities and Social Sciences ; National Natural Science Foundation of China ; Natural Science Foundation of Shanghai ; Philip K. H. Foundation
引用统计
被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.psych.ac.cn/handle/311026/46748
专题中国科学院心理健康重点实验室
通讯作者Yan, Chao
作者单位1.East China Normal Univ, Affiliated Mental Hlth Ctr ECNU, Sch Psychol & Cognit Sci, Key Lab Brain Funct Genom MOE&STCSM, Shanghai, Peoples R China
2.Shanghai Changning Mental Hlth Ctr, Shanghai, Peoples R China
3.Chinese Acad Sci, Neuropsychol & Appl Cognit Neurosci Lab, Beijing, Peoples R China
4.Chinese Acad Sci, Inst Psychol, CAS Key Lab Mental Hlth, Beijing, Peoples R China
5.Univ Chinese Acad Sci, Dept Psychol, Beijing, Peoples R China
6.Univ Ghent, Fac Sci, Ghent, Belgium
7.Hangzhou Normal Univ, Inst Brain Sci, Hangzhou, Peoples R China
8.Hangzhou Normal Univ, Sch Basic Med Sci, Dept Physiol, Hangzhou, Peoples R China
9.Duke Kunshan Univ, Data Sci Res Ctr, Div Nat & Appl Sci, Phylocognit Lab, Kunshan, Peoples R China
10.East China Normal Univ, Shanghai Key Lab Magnet Resonance, Shanghai, Peoples R China
第一作者单位中国科学院心理健康重点实验室
推荐引用方式
GB/T 7714
Wang, Xuan,Yan, Chao,Yang, Peng-yuan,et al. Unveiling the potential of machine learning in schizophrenia diagnosis: A meta-analytic study of task-based neuroimaging data[J]. PSYCHIATRY AND CLINICAL NEUROSCIENCES,2023:12.
APA Wang, Xuan.,Yan, Chao.,Yang, Peng-yuan.,Xia, Zheng.,Cai, Xin-lu.,...&Chan, Raymond C. K..(2023).Unveiling the potential of machine learning in schizophrenia diagnosis: A meta-analytic study of task-based neuroimaging data.PSYCHIATRY AND CLINICAL NEUROSCIENCES,12.
MLA Wang, Xuan,et al."Unveiling the potential of machine learning in schizophrenia diagnosis: A meta-analytic study of task-based neuroimaging data".PSYCHIATRY AND CLINICAL NEUROSCIENCES (2023):12.
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