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

Publication ,  Journal Article
Wang, X; Yan, C; Yang, P-Y; Xia, Z; Cai, X-L; Wang, Y; Kwok, SC; Chan, RCK
Published in: Psychiatry and clinical neurosciences
March 2024

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.

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Published In

Psychiatry and clinical neurosciences

DOI

EISSN

1440-1819

ISSN

1323-1316

Publication Date

March 2024

Volume

78

Issue

3

Start / End Page

157 / 168

Related Subject Headings

  • Schizophrenia
  • Neuroimaging
  • Magnetic Resonance Imaging
  • Machine Learning
  • Humans
  • Biomarkers
  • 5202 Biological psychology
  • 3209 Neurosciences
  • 3202 Clinical sciences
  • 1702 Cognitive Sciences
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Wang, X., Yan, C., Yang, P.-Y., Xia, Z., Cai, X.-L., Wang, Y., … Chan, R. C. K. (2024). Unveiling the potential of machine learning in schizophrenia diagnosis: A meta-analytic study of task-based neuroimaging data. Psychiatry and Clinical Neurosciences, 78(3), 157–168. https://doi.org/10.1111/pcn.13625
Wang, Xuan, Chao Yan, Peng-Yuan Yang, Zheng Xia, Xin-Lu Cai, Yi Wang, Sze Chai Kwok, and Raymond C. K. Chan. “Unveiling the potential of machine learning in schizophrenia diagnosis: A meta-analytic study of task-based neuroimaging data.Psychiatry and Clinical Neurosciences 78, no. 3 (March 2024): 157–68. https://doi.org/10.1111/pcn.13625.
Wang X, Yan C, Yang P-Y, Xia Z, Cai X-L, Wang Y, et al. Unveiling the potential of machine learning in schizophrenia diagnosis: A meta-analytic study of task-based neuroimaging data. Psychiatry and clinical neurosciences. 2024 Mar;78(3):157–68.
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, vol. 78, no. 3, Mar. 2024, pp. 157–68. Epmc, doi:10.1111/pcn.13625.
Wang X, Yan C, Yang P-Y, Xia Z, Cai X-L, Wang Y, Kwok SC, Chan RCK. Unveiling the potential of machine learning in schizophrenia diagnosis: A meta-analytic study of task-based neuroimaging data. Psychiatry and clinical neurosciences. 2024 Mar;78(3):157–168.
Journal cover image

Published In

Psychiatry and clinical neurosciences

DOI

EISSN

1440-1819

ISSN

1323-1316

Publication Date

March 2024

Volume

78

Issue

3

Start / End Page

157 / 168

Related Subject Headings

  • Schizophrenia
  • Neuroimaging
  • Magnetic Resonance Imaging
  • Machine Learning
  • Humans
  • Biomarkers
  • 5202 Biological psychology
  • 3209 Neurosciences
  • 3202 Clinical sciences
  • 1702 Cognitive Sciences