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 this http URL*******.

该研究提出了一种基于 T1-MRI 的新型动态结构脑网络构建方法，该方法可以动态定位关键区域并限制其层次分布，用于结构化的脑网络构建，以预测轻度认知障碍（MCI）病例的转化情况并取得最佳效果。