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

• URL: http://arxiv.org/abs/2305.10077v1
• Date: Wed, 17 May 2023 09:22:20 GMT
• Title: Dynamic Structural Brain Network Construction by Hierarchical Prototype Embedding GCN using T1-MRI
• Authors: Yilin Leng, Wenju Cui, Chen Bai, Zheng Yanyan, Jian Zheng
• Abstract summary: We propose a novel dynamic structural brain network construction method based on T1-MRI. We first cluster spatially-correlated channel and generate several critical brain regions as prototypes. We introduce a contrastive loss function to constrain the prototypes distribution, which embed the hierarchical brain semantic structure into the latent space.
• Score: 2.1580450836713574
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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/*******.

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