Unveiling the potential of machine learning in schizophrenia diagnosis: A meta-analytic study of task-based neuroimaging data
文献类型:期刊论文
作者 | Wang, Xuan6,7,8,9,10; Yan, Chao9,10; Yang, Peng-yuan5; Xia, Zheng10; Cai, Xin-lu3,4; Wang, Yi6,7,8; Kwok, Sze Chai1,2,9,10; Chan, Raymond C. K.6,7,8 |
刊名 | PSYCHIATRY AND CLINICAL NEUROSCIENCES |
出版日期 | 2023-12-29 |
页码 | 12 |
通讯作者邮箱 | cyan@psy.ecnu.edu.cn (chao yan) |
ISSN号 | 1323-1316 |
关键词 | attention machine learning meta-analysis schizophrenia task-based fMRI |
DOI | 10.1111/pcn.13625 |
产权排序 | 4 |
文献子类 | 实证研究 |
英文摘要 | 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. |
收录类别 | SCI |
WOS关键词 | NEGATIVE SYMPTOMS ; HIGH-RISK ; FUNCTIONAL CONNECTIVITY ; LATENT INHIBITION ; BRAIN NETWORKS ; PSYCHOSIS ; CLASSIFICATION ; FMRI ; INDIVIDUALS ; ABNORMALITIES |
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] |
WOS研究方向 | Neurosciences & Neurology ; Psychiatry |
出版者 | WILEY |
WOS记录号 | WOS:001134068300001 |
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 |
源URL | [http://ir.psych.ac.cn/handle/311026/46747] |
专题 | 心理研究所_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, Shanghai Key Lab Magnet Resonance, Shanghai, Peoples R China 2.Duke Kunshan Univ, Data Sci Res Ctr, Div Nat & Appl Sci, Phylocognit Lab, Kunshan, Peoples R China 3.Hangzhou Normal Univ, Sch Basic Med Sci, Dept Physiol, Hangzhou, Peoples R China 4.Hangzhou Normal Univ, Inst Brain Sci, Hangzhou, Peoples R China 5.Univ Ghent, Fac Sci, Ghent, Belgium 6.Univ Chinese Acad Sci, Dept Psychol, Beijing, Peoples R China 7.Chinese Acad Sci, Inst Psychol, CAS Key Lab Mental Hlth, Beijing, Peoples R China 8.Chinese Acad Sci, Neuropsychol & Appl Cognit Neurosci Lab, Beijing, Peoples R China 9.Shanghai Changning Mental Hlth Ctr, Shanghai, Peoples R China 10.East China Normal Univ, Affiliated Mental Hlth Ctr ECNU, Sch Psychol & Cognit Sci, Key Lab Brain Funct Genom MOE&STCSM, Shanghai, Peoples R China |
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. |
入库方式: OAI收割
来源:心理研究所
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