Fusion Learning from Dynamic Functional Connectivity: Combining the Amplitude and Phase of fMRI Signals to Identify Brain Disorders

TL;DR

This paper introduces a multi-scale fusion learning framework that combines amplitude and phase information from fMRI signals to enhance brain disorder detection, demonstrating superior performance over existing methods.

Contribution

The study presents MSFL, a novel multi-scale fusion learning approach integrating amplitude and phase features from dFC for improved brain disorder classification.

Findings

MSFL outperforms existing models in classifying autism and depression.

Both amplitude and phase features significantly contribute to detection.

Model explanation confirms the importance of combined features.

Abstract

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.