School of Electronic Information, Central South University, Changsha, 410148, China.
School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
Cereb Cortex. 2024 Oct 3;34(10). doi: 10.1093/cercor/bhae393.
Alzheimer's disease is the most common major neurocognitive disorder. Although currently, no cure exists, understanding the neurobiological substrate underlying Alzheimer's disease progression will facilitate early diagnosis and treatment, slow disease progression, and improve prognosis. In this study, we aimed to understand the morphological changes underlying Alzheimer's disease progression using structural magnetic resonance imaging data from cognitively normal individuals, individuals with mild cognitive impairment, and Alzheimer's disease via a contrastive variational autoencoder model. We used contrastive variational autoencoder to generate synthetic data to boost the downstream classification performance. Due to the ability to parse out the nonclinical factors such as age and gender, contrastive variational autoencoder facilitated a purer comparison between different Alzheimer's disease stages to identify the pathological changes specific to Alzheimer's disease progression. We showed that brain morphological changes across Alzheimer's disease stages were significantly associated with individuals' neurofilament light chain concentration, a potential biomarker for Alzheimer's disease, highlighting the biological plausibility of our results.
阿尔茨海默病是最常见的主要神经认知障碍。虽然目前尚无治愈方法，但了解阿尔茨海默病进展的神经生物学基础将有助于早期诊断和治疗，减缓疾病进展，改善预后。在这项研究中，我们旨在使用认知正常个体、轻度认知障碍个体和阿尔茨海默病患者的结构磁共振成像数据，通过对比变分自动编码器模型来了解阿尔茨海默病进展的形态学变化。我们使用对比变分自动编码器生成合成数据来提高下游分类性能。由于能够解析出年龄和性别等非临床因素，对比变分自动编码器促进了不同阿尔茨海默病阶段之间更纯粹的比较，以确定与阿尔茨海默病进展相关的特定病理变化。我们表明，阿尔茨海默病各阶段的大脑形态变化与个体的神经丝轻链浓度显著相关，神经丝轻链浓度是阿尔茨海默病的潜在生物标志物，这突出了我们结果的生物学合理性。
