Prob-sparse self-attention extraction of time-aligned dynamic functional connectivity for ASD diagnosis

Dynamic functional connectivity (DFC) has shown promise in the diagnosis of Autism Spectrum Disorder (ASD). However, extracting highly discriminative information from the complex DFC matrix remains a challenging task. In this paper, we propose an ASD classification framework PSA-FCN which is based on time-aligned DFC and Prob-Sparse Self-Attention to address this problem. Specifically, we introduce Prob-Sparse Self-Attention to selectively extract global features, and use self-attention distillation as a transition at each layer to capture local patterns and reduce dimensionality. Additionally, we construct a time-aligned DFC matrix to mitigate the time sensitivity of DFC and extend the dataset, thereby alleviating model overfitting. Our model is evaluated on fMRI data from the ABIDE NYU site, and the experimental results demonstrate that the model outperforms other methods in the paper with a classification accuracy of 81.8 %. Additionally, our research findings reveal significant variability in the DFC connections of brain regions of ASD patients, including Cuneus (CUN), Lingual gyrus (LING), Superior occipital gyrus (SOG), Posterior cingulate gyrus (PCG), and Precuneus (PCUN), which is consistent with prior research. In summary, our proposed PSA framework shows potential in ASD diagnosis as well as automatic discovery of critical ASD-related biomarkers.