Alzheimer's disease (AD) is associated with local (e.g. brain tissue atrophy) and global brain changes (loss of cerebral connectivity), which can be detected by high-resolution structural magnetic resonance imaging. Conventionally, these changes and their relation to AD are investigated independently. Here, we introduce a novel, highly-scalable approach that simultaneously captures $\textit{local}$ and $\textit{global}$ changes in the diseased brain. It is based on a neural network architecture that combines patch-based, high-resolution 3D-CNNs with global topological features, evaluating multi-scale brain tissue connectivity. Our local-global approach reached competitive results with an average precision score of $0.95\pm0.03$ for the classification of cognitively normal subjects and AD patients (prevalence $\approx 55\%$).
翻译:阿尔茨海默氏病(AD)与本地(例如脑组织萎缩)和全球大脑变化(脑连接丧失)有关,可以通过高分辨率结构磁共振成像检测到。 公约对这些变化及其与AD的关系进行了独立调查。 在这里,我们引入了一种新型的、高度可扩展的方法,同时捕捉患病大脑中的$\textit{logal}$和$\textit{global} $的变化。 它基于一个神经网络结构,将基于补丁、高分辨率的3D-CNNs与全球地貌特征相结合,评估多尺度脑组织连接。 我们的本地-全球方法达到了竞争性结果,平均精确得分为0.95美元/pm0.03美元,用于对认知正常对象和AD病人进行分类(流行值$\approx 55 ⁇ )。