Multi-head self-attention mechanism-based global feature learning model for ASD diagnosis

Background and Objectives: The static functional connectivity (SFC) networks based on resting-state functional MRI (rs-fMRI) typically focus on local correlations between specific brain regions, neglecting the broader connections across the entire brain. This limitation can hinder the accurate diagnosis of neurological conditions such as Autism Spectrum Disorder (ASD). This study aimed to overcome this limitation and improve ASD dentification accuracy. Methods: We propose a self-attention based ASD classification model. Employing sliding windows with longer window width, we locally sample the original data to increase the training sample size, thereby alleviating model overfitting. Subsequently, we introduce the multi-head self-attention mechanism, forming a deep model composed of stacked attention blocks. This ensure the capture of not only local correlations but also overall brain network features, significantly enhancing the classification accuracy of ASD. Results: Our proposed model was evaluated on fMRI data from the ABIDE NYU site. Experimental results demonstrated an accuracy of 81.47%, a sensitivity of 83.8%, and a specificity of 80.16%. Compared to other methods in the literature, our approach exhibited superior accuracy. Furthermore, the experiments revealed that the biomarkers used by the model for classification are primarily distributed across brain regions such as the superior frontal gyrus, middle frontal gyrus, and hippocampus, aligning with previous research findings. Conclusion: The sliding window method effectively enriches the dataset and alleviates overfitting. Simultaneously, the suggested model, which relies on self-attention mechanisms, has the ability to effectively extract global information from brain regions, providing a viable method to improve the accuracy of ASD identification.