Dynamic Structural Brain Network Construction by Hierarchical Prototype Embedding GCN Using T1-MRI

Abstract

Constructing structural brain networks using T1-weighted MRI (T1-MRI) presents a significant challenge due to the lack of direct regional connectivity. Current methods with T1-MRI rely on predefined regions or isolated pretrained modules to localize atrophy regions, which neglects individual specificity. Besides, existing methods capture global structural context only on the whole-image-level, which weaken correlation between regions and the hierarchical distribution nature of brain structure. We hereby propose a novel dynamic structural brain network construction method based on T1-MRI, which can dynamically localize critical regions and constrain the hierarchical distribution among them. Specifically, we first cluster spatially-correlated channel and generate several critical brain regions as prototypes. Then, we introduce a contrastive loss function to constrain the prototypes distribution, which embed the hierarchical brain semantic structure into the latent space. Self-attention and GCN are then used to dynamically construct hierarchical correlations of critical regions for brain network and explore the correlation, respectively. Our method is trained on ADNI-1 and tested on ADNI-2 databases for mild cognitive impairment (MCI) conversion prediction, and achieve the state-of-the-art (SOTA) performance.