PDF(Pubmed)
Abstract：
The pathogenesis of Alzheimer\'s disease (AD) remains unclear, but revealing individual differences in functional connectivity (FC) may provide insights and improve diagnostic precision. A hierarchical clustering-based autoencoder with functional connectivity was proposed to categorize 82 AD patients from the Alzheimer\'s Disease Neuroimaging Initiative. Compared to directly performing clustering, using an autoencoder to reduce the dimensionality of the matrix can effectively eliminate noise and redundant information in the data, extract key features, and optimize clustering performance. Subsequently, subtype differences in clinical and graph theoretical metrics were assessed. Results indicate a significant inter-subject heterogeneity in the degree of FC disruption among AD patients. We have identified two neurophysiological subtypes: subtype I exhibits widespread functional impairment across the entire brain, while subtype II shows mild impairment in the Limbic System region. What is worth noting is that we also observed significant differences between subtypes in terms of neurocognitive assessment scores associations with network functionality, and graph theory metrics. Our method can accurately identify different functional disruptions in subtypes of AD, facilitating personalized treatment and early diagnosis, ultimately improving patient outcomes.
摘要：
阿尔茨海默病(AD)的发病机制尚不清楚,但揭示功能连接（FC）的个体差异可能会提供见解并提高诊断精度。提出了一种具有功能连通性的基于分层聚类的自动编码器，用于对阿尔茨海默病神经影像学计划中的82名AD患者进行分类。与直接执行聚类相比，使用自动编码器来降低矩阵的维数，可以有效地消除数据中的噪声和冗余信息，提取关键特征，优化集群性能。随后，评估了临床和图形理论指标的亚型差异.结果表明，AD患者中FC破坏程度存在显着的受试者间异质性。我们已经确定了两种神经生理学亚型：I型在整个大脑中表现出广泛的功能障碍，而亚型II在边缘系统区域显示轻度损害。值得注意的是，我们还观察到，就神经认知评估得分与网络功能的关联而言，亚型之间存在显着差异。和图论度量。我们的方法可以准确识别AD亚型中的不同功能破坏，促进个性化治疗和早期诊断，最终改善患者预后。
