Utilizing neuroimaging and machine learning (ML) to differentiate schizophrenia (SZ) patients from normal controls (NCs) and for detecting abnormal brain regions in schizophrenia has several benefits and can provide a reference for the clinical diagnosis of schizophrenia. In this study, structural magnetic resonance images (sMRIs) from SZ patients and NCs were used for discriminative analysis. This study proposed an ML framework based on coarse-to-fine feature selection. The proposed framework used two-sample t-tests to extract the differences between groups first, then further eliminated the nonrelevant and redundant features with recursive feature elimination (RFE), and finally utilized the support vector machine (SVM) to learn the decision models with selected gray matter (GM) and white matter (WM) features. Previous studies have tended to report differences at the group level instead of at the individual level and cannot be widely applied. The method proposed in this study extends the diagnosis to the individual level and has a higher recognition rate than previous methods. The experimental results of this study demonstrate that the proposed framework distinguishes SZ patients from NCs, with the highest classification accuracy reaching over 85%. The identified biomarkers are also consistent with previous literature findings. As a universal method, the proposed framework can be extended to diagnose other diseases.

中文翻译：

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通过机器学习使用结构MRI检测精神分裂症的异常大脑区域。

利用神经影像和机器学习（ML）将精神分裂症（SZ）患者与正常对照（NCs）区别开来，并检测精神分裂症中的异常大脑区域具有许多好处，可为精神分裂症的临床诊断提供参考。在这项研究中，使用来自SZ患者和NC的结构磁共振图像（sMRIs）进行判别分析。这项研究提出了一种基于粗糙到精细特征选择的机器学习框架。拟议的框架使用两样本t-测试首先提取组之间的差异，然后通过递归特征消除（RFE）进一步消除不相关和冗余的特征，最后利用支持向量机（SVM）来学习具有选定灰质（GM）和白色的决策模型物质（WM）功能。以前的研究倾向于报告在小组一级而不是在个人一级的差异，因此不能广泛应用。本研究提出的方法将诊断扩展到个人水平，并且比以前的方法具有更高的识别率。这项研究的实验结果表明，该框架将SZ患者与NC患者区分开来，分类准确率最高，达到85％以上。鉴定的生物标志物也与先前的文献发现一致。作为一种通用方法