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。

使用 T1-MRI 的分层原型嵌入 GCN 构建动态结构性脑网络

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

Brain network analysis is essential for diagnosing and intervention for
Alzheimer's disease (AD). However, previous research relied primarily on
specific time-consuming and subjective toolkits. Only few tools can obtain the
structural brain networks from brain diffusion tensor images (DTI). In this
paper, we propose a diffusion based end-to-end brain network generative model
Brain Diffuser that directly shapes the structural brain networks from DTI.
Compared to existing toolkits, Brain Diffuser exploits more structural
connectivity features and disease-related information by analyzing disparities
in structural brain networks across subjects. For the case of Alzheimer's
disease, the proposed model performs better than the results from existing
toolkits on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

本研究提出了基于扩散张量图像的端到端脑网络生成模型 Brain Diffuser，旨在分析结构脑网络的差异以获得结构连接性的更多特征和疾病相关信息，在阿尔茨海默病（AD）的情况下比现有工具包在 ADNI 数据库上展现出更好的性能表现。