IEEE Trans Neural Syst Rehabil Eng. 2023;31:4017-4028. doi: 10.1109/TNSRE.2023.3323432. Epub 2023 Oct 18.
Integrating the brain structural and functional connectivity features is of great significance in both exploring brain science and analyzing cognitive impairment clinically. However, it remains a challenge to effectively fuse structural and functional features in exploring the complex brain network. In this paper, a novel brain structure-function fusing-representation learning (BSFL) model is proposed to effectively learn fused representation from diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI) for mild cognitive impairment (MCI) analysis. Specifically, the decomposition-fusion framework is developed to first decompose the feature space into the union of the uniform and unique spaces for each modality, and then adaptively fuse the decomposed features to learn MCI-related representation. Moreover, a knowledge-aware transformer module is designed to automatically capture local and global connectivity features throughout the brain. Also, a uniform-unique contrastive loss is further devised to make the decomposition more effective and enhance the complementarity of structural and functional features. The extensive experiments demonstrate that the proposed model achieves better performance than other competitive methods in predicting and analyzing MCI. More importantly, the proposed model could be a potential tool for reconstructing unified brain networks and predicting abnormal connections during the degenerative processes in MCI.
整合大脑结构和功能连接特征在探索大脑科学和临床分析认知障碍方面具有重要意义。然而，在探索复杂的大脑网络时，有效地融合结构和功能特征仍然是一个挑战。在本文中，提出了一种新颖的脑结构-功能融合表示学习（BSFL）模型，用于从弥散张量成像（DTI）和静息态功能磁共振成像（fMRI）中有效地学习融合表示，以进行轻度认知障碍（MCI）分析。具体来说，开发了分解-融合框架，首先将特征空间分解为每个模态的均匀和独特空间的并集，然后自适应地融合分解后的特征以学习与 MCI 相关的表示。此外，设计了一个知识感知的转换器模块，用于自动捕获整个大脑的局部和全局连接特征。此外，进一步设计了统一-独特对比损失，以使分解更加有效，并增强结构和功能特征的互补性。广泛的实验表明，与其他竞争方法相比，所提出的模型在预测和分析 MCI 方面具有更好的性能。更重要的是，所提出的模型可能成为重建统一脑网络和预测 MCI 退行过程中异常连接的潜在工具。
