Digital biomarker for a low cost ambulatory test for early detection of Alzheimer's disease

项目总结/摘要 目前，没有建立的生物标志物存在，以稳健地预测认知症状的临床表现 阿尔茨海默病（AD）和AD相关痴呆患者。PET和MRI大脑生物标记物价格昂贵 并且是侵入性的，因此迫切需要一种非侵入性的、廉价的和便携式的AD筛查工具， 可以很容易地部署在家庭或住宅社区。而非大脑信号表征生物学 和行为特征可能被证明是有价值的，两种类型的大脑信号也有很大的希望作为数字生物标志物。 早期阶段的AD：癫痫活动（EA）和异常的功能性脑网络。重要的是，两者 生物标志物可以收集负担得起的和可靠的最新的干电极流动 脑电图技术。AD患者的癫痫发作患病率是 一般人群（Pandis和Scarmeas，2012）;然而，使用EA作为AD数字生物标志物， 大部分未开发。还众所周知，遗忘型MCI（aMCI）和AD患者表现出微妙的功能性差异。 网络中断是AD的有希望的预测因子，如我们的小组所示（例如Pusil等人，2019）和 其他人，但没有以前的研究评估EA和功能网络之间的联合影响。的 该建议的科学前提是双重的：（i）组合EA和功能网络生物标志物将预测 从aMCI到AD的转换比孤立的单个信号更鲁棒，以及（ii）一种新的深度学习模型 它执行多模态（MEG和EEG）学习以找到AD的共享签名，但最终产生一个模型， 需要经济实惠的脑电图数据，将产生一个强大的生物标志物。该提案将寻求三个具体的 目标。1)识别EA的特定特征，其指示aMCI转化; 2）设计数字生物标志物， 从EA特征和功能性大脑网络预测aMCI转化; 3）将数字生物标志物扩展到 动态脑电图干电极技术。为了实现这些目标，我们将收集MEG，湿电极EEG， 200例aMCI患者的干电极（动态）EEG数据， 癫痫学家，并监测病人的年转化率为AD。然后，我们将设计并验证一个深度 学习模型称为Siamese Multiple Graph to Gauss（SMG 2G），它在MEG上执行多模态学习 和EEG网络（图形）数据，但最终产生一个模型，需要EEG数据来预测 aMCI转化。最终产品将是一个干电极动态脑电图数字生物标志物，可以很容易地 在家中或住宅设施中测量。本申请中提出的研究是创新的，因为它 是第一个将联合收割机EA和功能网络信号结合起来设计AD生物标志物的人，并通过切割- 边缘机器学习它也很重要，因为它将在科学和技术上垂直推进该领域， 在临床上，通过大规模的早期检测AD。我们的团队已经做好了充分的准备 项目，具有临床和工程专业知识，多年来的密切合作，初步数据 支持目标和机构支持。

PROJECT SUMMARY/ABSTRACT Presently, no established biomarker exists to robustly predict the clinical manifestation of cognitive symptoms in persons with Alzheimer’s disease (AD) and AD-related dementias. PET and MRI brain biomarkers are costly and invasive, thus there is a critical need for a noninvasive, inexpensive, and portable AD screening tool that can be easily deployed in-home or in residential communities. While non-brain signals characterizing biological and behavioral traits may prove valuable, two types of brain signal also hold strong promise as digital biomarkers of early stages of AD: epileptogenic activity (EA), and aberrant functional brain networks. Importantly, both biomarkers can be collected affordably and reliably with the latest dry-electrode ambulatory electroencephalography (EEG) technology. AD patients have a tenfold higher seizure prevalence compared to the general population (Pandis and Scarmeas, 2012); however, the use of EA as an AD digital biomarker is largely unexplored. It is also well known that amnestic MCI (aMCI) and AD patients show subtle functional network disruptions that are promising predictors of AD, as shown by our group (e.g Pusil et al., 2019) and others, but there is no previous research assessing the joint impact between EA and functional networks. The scientific premise of this proposal is two-fold: (i) a combinatorial EA and functional network biomarker will predict conversion from aMCI to AD more robustly than a single signal in isolation, and (ii) a novel deep learning model that performs multimodal (MEG and EEG) learning to find shared signatures of AD, but ultimately yields a model that needs affordable EEG-only data, will yield a powerful biomarker. This proposal will pursue three specific aims. 1) Identify specific features of EA that prognosticate aMCI conversion; 2) Design a digital biomarker that predicts aMCI conversion from EA features and functional brain networks; 3) Extend the digital biomarker to ambulatory EEG with dry electrode technology. To achieve these aims, we will collect MEG, wet-electrode EEG, and dry-electrode (ambulatory) EEG data from 200 aMCI patients, evaluate their signals with expert epileptologists, and monitor the patient’s yearly conversion rate to AD. We will then design and validate a deep learning model called Siamese Multiple Graph to Gauss (SMG2G), which performs multimodal learning on MEG and EEG network (graph) data but ultimately yields a model that needs EEG-only data to make predictions of aMCI conversion. The final product will be a dry-electrode ambulatory EEG digital biomarker that can be readily measured in home or in a residential facility. The research proposed in this application is innovative because it is the first to combine EA and functional network signals to design an AD biomarker and achieves this by cutting- edge machine learning. It is also significant because it will advance the field vertically both scientifically and clinically by enabling large-scale, early detection of AD. Our team is especially well-prepared to undertake this project, with clinical and engineering expertise, strong collaboration over the years, with preliminary data supporting the aims, and institutional support.