Although genetic risk factors and network-level neuroimaging abnormalities have shown effects on cognitive performance and brain atrophy in Alzheimer’s disease (AD), little is understood about how apolipoprotein E (APOE) ε4 allele, the best-known genetic risk for AD, affect brain connectivity before the onset of symptomatic AD. This study aims to investigate APOE ε4 effects on brain connectivity from the perspective of multimodal connectome. Here, we propose a novel multimodal brain network modeling framework and a network quantification method based on persistent homology for identifying APOE ε4-related network differences. Specifically, we employ sparse representation to integrate multimodal brain network information derived from both the resting state functional magnetic resonance imaging (rs-fMRI) data and the diffusion-weighted magnetic resonance imaging (dw-MRI) data. Moreover, persistent homology is proposed to avoid the ad hoc selection of a specific regularization parameter and to capture valuable brain connectivity patterns from the topological perspective. The experimental results demonstrate that our method outperforms the competing methods, and reasonably yields connectomic patterns specific to APOE ε4 carriers and non-carriers. We have proposed a multimodal framework that integrates structural and functional connectivity information for constructing a fused brain network with greater discriminative power. Using persistent homology to extract topological features from the fused brain network, our method can effectively identify APOE ε4-related brain connectomic biomarkers.

中文翻译：

---

APOE ε4 对多模式脑连接体特征的影响：持久同源性研究


尽管遗传风险因素和网络水平神经影像异常已显示出对阿尔茨海默病 (AD) 认知能力和脑萎缩的影响，但人们对载脂蛋白 E (APOE) ε4 等位基因（AD 最著名的遗传风险）如何影响大脑知之甚少。在有症状的 AD 出现之前就已建立连接。本研究旨在从多模态连接组的角度探讨 APOE ε4 对大脑连接的影响。在这里，我们提出了一种新颖的多模式脑网络建模框架和一种基于持久同源性的网络量化方法，用于识别 APOE ε4 相关的网络差异。具体来说，我们采用稀疏表示来整合源自静息态功能磁共振成像（rs-fMRI）数据和扩散加权磁共振成像（dw-MRI）数据的多模态脑网络信息。此外，提出了持久同源性以避免特定正则化参数的临时选择，并从拓扑角度捕获有价值的大脑连接模式。实验结果表明，我们的方法优于竞争方法，并且合理地产生了针对 APOE ε4 携带者和非携带者的连接组模式。我们提出了一个多模态框架，该框架集成了结构和功能连接信息，用于构建具有更大辨别力的融合大脑网络。利用持久同源性从融合脑网络中提取拓扑特征，我们的方法可以有效识别 APOE ε4 相关的脑连接组生物标志物。