The diagnosis of Alzheimer's disease (AD) in routine clinical practice is most commonly based on subjective clinical interpretations. Quantitative electroencephalography (QEEG) measures have been shown to reflect neurodegenerative processes in AD and might qualify as affordable and thereby widely available markers to facilitate the objectivization of AD assessment. Here, we present a novel framework combining Riemannian tangent space mapping and elastic net regression for the development of brain atrophy markers. While most AD QEEG studies are based on small sample sizes and psychological test scores as outcome measures, here we train and test our models using data of one of the largest prospective EEG AD trials ever conducted, including MRI biomarkers of brain atrophy.

翻译：在常规临床实践中对阿尔茨海默氏病(AD)的诊断最常以主观临床解释为依据,定量脑电图(QEEG)措施已经显示反映AD中的神经降解过程,并可能符合可负担得起的标准,从而可以广泛使用,以便利AD评估的客观化。这里,我们提出了一个新颖的框架,将里曼尼相近的空间测绘和弹性网回归结合起来,用于开发大脑萎缩标记。虽然大多数AD QEEG研究基于小样本大小和心理测试分数作为结果衡量标准,但在这里,我们用有史以来最大的EEGAD试验之一的数据,包括脑萎缩的MRI生物标记,来培训和测试我们的模型。