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Constructing structural brain networks using T1-weighted magnetic resonanceimaging (T1-MRI) presents a significant challenge due to the lack of directregional connectivity information. Current methods with T1-MRI rely onpredefined regions or isolated pretrained location modules to obtain atrophicregions, which neglects individual specificity. Besides, existing methodscapture global structural context only on the whole-image-level, which weakencorrelation between regions and the hierarchical distribution nature of brainconnectivity.We hereby propose a novel dynamic structural brain networkconstruction method based on T1-MRI, which can dynamically localize criticalregions and constrain the hierarchical distribution among them for constructingdynamic structural brain network. Specifically, we first clusterspatially-correlated channel and generate several critical brain regions asprototypes. Further, we introduce a contrastive loss function to constrain theprototypes distribution, which embed the hierarchical brain semantic structureinto the latent space. Self-attention and GCN are then used to dynamicallyconstruct hierarchical correlations of critical regions for brain network andexplore the correlation, respectively. Our method is evaluated on ADNI-1 andADNI-2 databases for mild cognitive impairment (MCI) conversion prediction, andacheive the state-of-the-art (SOTA) performance. Our source code is availableat http://github.com/.

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