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/*******.

翻译：利用T1加权磁共振成像（T1-MRI）构建结构性脑网络面临重大挑战，因缺乏直接的区域连接信息。现有基于T1-MRI的方法依赖预定义区域或独立预训练的位置模块获取萎缩区域，忽略了患者个体特异性。此外，现有方法仅在整图层面捕获全局结构上下文，削弱了区域间的关联性以及脑连接固有的层次分布特性。本文提出一种新颖的基于T1-MRI的动态结构性脑网络构建方法，可动态定位关键区域并约束其间的层次分布，以构建动态结构性脑网络。具体而言，我们首先对空间相关通道进行聚类，生成若干关键脑区作为原型。进一步引入对比损失函数约束原型分布，将脑层次语义结构嵌入潜在空间。随后采用自注意力机制与图卷积网络（GCN）分别动态构建关键区域间的层次关联并探索其相关性。该方法在ADNI-1和ADNI-2数据库上进行轻度认知障碍（MCI）转化预测评估，达到当前最优（SOTA）性能。源代码发布于http://github.com/*******。