Unveiling the potential of machine learning in schizophrenia diagnosis: A meta‐analytic study of task‐based neuroimaging data

Author:

Wang Xuan1234,Yan Chao12ORCID,Yang Peng‐yuan5,Xia Zheng1,Cai Xin‐lu6,Wang Yi34,Kwok Sze Chai1278ORCID,Chan Raymond C.K.34ORCID

Affiliation:

1. Key Laboratory of Brain Functional Genomics (MOE&STCSM), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science East China Normal University Shanghai China

2. Shanghai Changning Mental Health Center Shanghai China

3. Neuropsychology and Applied Cognitive Neuroscience Laboratory; CAS Key Laboratory of Mental Health Institute of Psychology, Chinese Academy of Sciences Beijing China

4. Department of Psychology University of Chinese Academy of Sciences Beijing China

5. Faculty of Science Ghent University Ghent Belgium

6. Institute of Brain Science and Department of Physiology, School of Basic Medical Sciences Hangzhou Normal University Hangzhou China

7. Phylo‐Cognition Laboratory, Division of Natural and Applied Sciences, Data Science Research Center Duke Kunshan University Kunshan China

8. Shanghai Key Laboratory of Magnetic Resonance East China Normal University Shanghai China

Abstract

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 (β = 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 (β = 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.

Funder

Ministry of Education of the People's Republic of China

National Natural Science Foundation of China

Natural Science Foundation of Shanghai Municipality

Publisher

Wiley

Subject

Psychiatry and Mental health,Neurology (clinical),Neurology,General Medicine,General Neuroscience

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