Alzheimer's Disease and normal aging are both characterized by brain atrophy. The question of whether AD-related brain atrophy represents accelerated aging or a neurodegeneration process distinct from that in normal aging remains unresolved. Moreover, precisely disentangling AD-related brain atrophy from normal aging in a clinical context is complex. In this study, we propose a deformation-based morphometry framework to estimate normal aging and AD-specific atrophy patterns of subjects from morphological MRI scans. We first leverage deep-learning-based methods to create age-dependent templates of cognitively normal (CN) subjects. These templates model the normal aging atrophy patterns in a CN population. Then, we use the learned diffeomorphic registration to estimate the one-year normal aging pattern at the voxel level. We register the testing image to the 60-year-old CN template in the second step. Finally, normal aging and AD-specific scores are estimated by measuring the alignment of this registration with the one-year normal aging pattern. The methodology was developed and evaluated on the OASIS3 dataset with 1,014 T1-weighted MRI scans. Of these, 326 scans were from CN subjects, and 688 scans were from individuals clinically diagnosed with AD at different stages of clinical severity defined by clinical dementia rating (CDR) scores. The results show that ventricles predominantly follow an accelerated normal aging pattern in subjects with AD. In turn, hippocampi and amygdala regions were affected by both normal aging and AD-specific factors. Interestingly, hippocampi and amygdala regions showed more of an accelerated normal aging pattern for subjects during the early clinical stages of the disease, while the AD-specific score increases in later clinical stages. Our code is freely available at https://github.com/Fjr9516/DBM_with_DL.

翻译：阿尔茨海默病与正常老化均以脑萎缩为特征。阿尔茨海默病相关的脑萎缩究竟代表加速老化，还是区别于正常老化的独立神经退行过程，这一问题至今尚未明确。此外，在临床背景下精确分离阿尔茨海默病相关脑萎缩与正常老化颇具挑战。本研究提出一种基于形变形态测量框架，通过形态学MRI扫描估算受试者的正常老化与阿尔茨海默病特异性萎缩模式。我们首先利用深度学习方法创建认知正常受试者的年龄依赖模板，这些模板建模了认知正常人群中的正常老化萎缩模式；随后通过学习的微分同胚配准方法在体素水平估算一年期正常老化模式；第二步将测试图像配准至60岁认知正常模板；最终通过测量该配准与一年期正常老化模式的对齐程度，分别计算正常老化评分与阿尔茨海默病特异性评分。该方法基于OASIS3数据集（包含1,014张T1加权MRI扫描）进行开发与评估，其中326张来自认知正常受试者，688张来自经临床痴呆评分量表不同严重阶段确诊的阿尔茨海默病患者。结果显示：阿尔茨海默病受试者的脑室主要呈现加速正常老化模式；而海马与杏仁核区域同时受正常老化与阿尔茨海默病特异性因素影响。值得注意的是，疾病早期临床阶段受试者的海马与杏仁核区域更倾向加速正常老化模式，而阿尔茨海默病特异性评分在后期临床阶段升高。我们的代码已开源发布于https://github.com/Fjr9516/DBM_with_DL。