融合脑信号振幅与相位信息，提升自闭症和抑郁症的识别准确率
Fusion Learning from Dynamic Functional Connectivity: Combining the Amplitude and Phase of fMRI Signals to Identify Brain Disorders
- 构建多尺度融合框架，同时利用振幅相关性和相位同步性
- 在两个公开数据集上分类性能显著优于现有模型
- 适用于脑疾病诊断研究，尤其关注功能连接动态特性
基于静息态功能磁共振成像（fMRI）的动态功能连接（dFC）在脑科学中广泛应用。滑动窗口相关法（SWC）是常用方法，通过计算脑区信号振幅时间序列的相关系数构建dFC。本文提出一种融合振幅与相位信息的新方法——多尺度融合学习框架（MSFL），结合SWC（捕捉振幅相关性）与相位同步性（PS，度量相位一致性）。在ABIDE I数据集上评估自闭症谱系障碍，在REST-meta-MDD数据集上评估重度抑郁障碍，结果表明MSFL显著优于现有对比模型。使用SHAP框架进行模型解释，验证了SWC与PS两类dFC特征均对疾病检测有贡献。
原文摘要 · Abstract (English)
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.
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