Neuroimaging technique is a powerful tool to characterize the abnormality of brain networks in schizophrenia. However, the neurophysiological substrate of schizophrenia is still unclear. Here we investigated the patterns of brain functional and structural changes in female patients with schizophrenia using elastic net logistic regression analysis of resting-state functional magnetic resonance imaging data. Data from 52 participants (25 female schizophrenia patients and 27 healthy controls) were obtained. Using an elastic net penalty, the brain regions most relevant to schizophrenia pathology were defined in the models using the amplitude of low-frequency fluctuations (ALFF) and gray matter, respectively. The receiver operating characteristic analysis showed reliable classification accuracy with 85.7% in ALFF analysis, and 77.1% in gray matter analysis. Notably, our results showed eight common regions between the ALFF and gray matter analyses, including the Frontal-Inf-Orb-R, Rolandic-Oper-R, Olfactory-R, Angular-L, Precuneus-L, Precuenus-R, Heschl-L, and Temporal-Pole-Mid-R. In addition, the severity of symptoms was found positively associated with the ALFF within the Rolandic-Oper-R and Frontal-Inf-Orb-R. Our findings indicated that elastic net logistic regression could be a useful tool to identify the characteristics of schizophrenia -related brain deterioration, which provides novel insights into schizophrenia diagnosis and prediction.

神经影像技术是表征精神分裂症大脑网络异常的有力工具。然而，精神分裂症的神经生理学基础仍不清楚。在这里，我们利用静息态功能磁共振成像数据的弹性网逻辑回归分析，研究了女性精神分裂症患者的大脑功能和结构变化模式。获得了 52 名参与者（25 名女性精神分裂症患者和 27 名健康对照）的数据。使用弹性净罚分，分别使用低频波动（ALFF）和灰质的幅度在模型中定义与精神分裂症病理学最相关的大脑区域。接收者操作特征分析显示出可靠的分类准确度，ALFF 分析为 85.7%，灰质分析为 77.1%。值得注意的是，我们的结果显示了 ALFF 和灰质分析之间的八个共同区域，包括 Frontal-Inf-Orb-R、Rolandic-Oper-R、Olfactory-R、Angular-L、Precuneus-L、Precuenus-R、Heschl- L 和颞极中 R。此外，还发现症状的严重程度与 Rolandic-Oper-R 和 Frontal-Inf-Orb-R 内的 ALFF 呈正相关。我们的研究结果表明，弹性网络逻辑回归可能是识别精神分裂症相关大脑退化特征的有用工具，为精神分裂症的诊断和预测提供了新的见解。