Multi-task dynamic graph learning for brain disorder identification with functional MRI

Dynamic functional connectivity (FC) analysis based on resting-state functional magnetic resonance imaging (rs-fMRI) is widely used for automated diagnosis of brain disorders. A large number of dynamic FC analysis methods rely on sliding window techniques to extract time-varying features of brain activity from localized time periods. However, these methods are sensitive to window parameters and individual differences, leading to significant variability in the extracted features and impacting the stability and accuracy of disease classification. Additionally, while dynamic graph learning holds promise in modeling time-varying brain networks, existing methods still encounter difficulties in effectively capturing spatio-temporal dynamic information. Therefore, in this paper we propose a multi-task dynamic graph learning framework (MT-DGL) to align FC trajectories and learn the spatio-temporal dynamic information for brain disease recognition. The MT-DGL mainly includes three parts: (1) SPD-valued FC trajectory alignment module for overcoming the model’s dependence on sliding window parameters and mitigating the impact of asynchrony in execution rates across individuals, (2) Mamba-based multi-scale dynamic graph learning module for extracting spatio-temporal dynamic features from fMRI time series, and (3) multi-scale fusion and multi-task learning strategy to enhance the model’s understanding of age-related brain FC changes and improve the effectiveness of brain disorder identification. Experimental results indicate that the proposed method exhibits excellent performance in several publicly available fMRI datasets. Specifically, on the largest site in the ABIDE dataset, the accuracy and area under the curve reached 73.9% and 74.9%, respectively.