Xuan Wang, Chao Yan, Peng-Yuan Yang, Zheng Xia, Xin-Lu Cai, Yi Wang, Sze Chai Kwok, Raymond C K Chan
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引用次数: 0
摘要
机器学习(ML)技术的出现为使用任务相关功能磁共振成像(t-fMRI)设计识别与精神分裂症(SCZ)相关的生物标志物开辟了新的途径。为了评估该方法的有效性,我们使用双变量模型对31项t-fMRI研究进行了综合荟萃分析。我们的研究结果显示,t-fMRI研究的总体敏感性为0.83,特异性为0.82。值得注意的是,神经心理领域调节分类表现,选择性注意表现出显著高于工作记忆的特异性(β = 0.98, z = 2.11, p = 0.04)。涉及老年慢性SCZ患者的研究报告了更高的敏感性(ps
Unveiling the potential of machine learning in schizophrenia diagnosis: A meta-analytic study of task-based neuroimaging data.
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.
期刊介绍:
PCN (Psychiatry and Clinical Neurosciences)
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Published 12 online issues a year by JSPN
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