  Constructing structural brain networks using T1-weighted magnetic resonance
imaging (T1-MRI) presents a significant challenge due to the lack of direct
regional connectivity information. Current methods with T1-MRI rely on
predefined regions or isolated pretrained location modules to obtain atrophic
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
connectivity.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 for constructing
dynamic structural brain network. Specifically, we first cluster
spatially-correlated channel and generate several critical brain regions as
prototypes. Further, 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 evaluated on ADNI-1 and
ADNI-2 databases for mild cognitive impairment (MCI) conversion prediction, and
acheive the state-of-the-art (SOTA) performance. Our source code is available
at http://github.com/*******.
