MPANet: Multi-scale Pyramid Attention Network for Collaborative Modeling Spatio-Temporal Patterns of Default Mode Network

The functional activity of the default mode network (DMN) in the resting state is complex and spontaneous. Modeling spatio-temporal patterns of DMN based on four-dimensional Resting-state functional Magnetic Resonance Imaging (Rs-fMRI) provides a basis for exploring spontaneous brain functional activities. However, how to utilize spatio-temporal features to complete the multi-level description of 4D Rs-fMRI with diverse characteristics in the shallow stage of the model and accurately characterize the DMN holistic spatio-temporal patterns remains challenging in the current DMN spatio-temporal patterns modeling. To this end, we propose a Multi-scale Pyramid Attention Network (MPANet) to focus on shallow features and model the spatio-temporal patterns of resting-state personalized DMN. Specifically, in the spatial stage, we design a multi-scale pyramid block in the shallow layer to expand the receptive field and extract granular information at different levels, which realize feature enhancement and guides the model to characterize the DMN spatial pattern. In the temporal stage, parallel guidance from spatial to the temporal pattern is achieved through the fast down-sampling operation and introduction of multi-head attention blocks for a more effective fusion of spatio-temporal features. The results based on a publicly available dataset demonstrate that MPANet outperforms other state-of-the-art methods. This network presents a robust tool for modeling the spatio-temporal patterns of individuals with DMN, and its exceptional performance suggests promising potential for clinical applications.