Dynamic functional connectivity (dFC) derived from resting-state functional magnetic resonance imaging (fMRI) has been extensively utilized in brain science research. The sliding window correlation (SWC) method is a widely used approach for constructing dFC by computing correlation coefficients between amplitude time series of signals from pairs of brain regions. In this study, we propose an integrated approach that incorporates both amplitude and phase information of fMRI signals to improve the detection of brain disorders. Specifically, we introduce a multi-scale fusion learning framework, namely MSFL, which leverages two complementary dFC features derived from SWC and phase synchronization (PS). Here, SWC captures amplitude correlations, while PS measures phase coherence within dFC. We evaluated the efficacy of MSFL in classifying autism spectrum disorder and major depressive disorder using two publicly available datasets: ABIDE I and REST-meta-MDD, respectively. The results indicate that MSFL significantly outperforms existing comparative models. Moreover, we performed model explanation analysis using the SHAP framework, which showed that both types of dFC features from SWC and PS contribute to detecting brain disorders.

翻译：动态功能连接（dFC）源自静息态功能磁共振成像（fMRI），已广泛用于脑科学研究。滑动窗口相关（SWC）方法是构建dFC的常用技术，通过计算成对脑区信号振幅时间序列间的相关系数实现。本研究提出一种整合fMRI信号振幅和相位信息的综合方法，以提升脑部疾病的检测效能。具体而言，我们设计了多尺度融合学习框架MSFL，该框架利用基于SWC和相位同步（PS）的两种互补性dFC特征：SWC捕捉振幅相关性，而PS则衡量dFC中的相位一致性。我们使用两个公开数据集（ABIDE I和REST-meta-MDD）分别验证了MSFL在自闭症谱系障碍和重度抑郁症分类中的有效性。结果表明，MSFL显著优于现有对比模型。此外，基于SHAP框架的模型解释分析显示，源自SWC和PS的两种dFC特征均对脑部疾病检测具有贡献。