Deep learning for prediction of Mild Cognitive Impairment and Dementia of the Alzheimer's type

摘要 阿尔茨海默病和相关的痴呆症是主要的公共卫生挑战， 预计在未来几十年内流行。阿尔茨海默病越来越被认为是有网络的- 水平效应和相互作用。在这个项目中，我们将开发一个深度学习模型来学习潜在的 功能神经成像的表现，以解开潜在的来源并更好地重建 数据。 功能磁共振成像中的深度学习方法在泛化和可解释性方面面临着共同的挑战。至 解决这些问题，系统将学习可被解码和解释为空间的表示 大脑网络的模式和时间动力学；并且很容易推广到不同的对象，大脑 状态、行为任务和疾病状况，而无需从头开始重新设计或重新培训系统。 拟议的对阿尔茨海默病的关注是将这一概念应用于临床的第一步。 我们将利用两个公开可用的大型数据(例如，人类连接项目-老龄化、阿尔茨海默病 神经成像计划)以及NIA P30资助的密歇根州纵向队列 阿尔茨海默病研究中心(MADRC)；这一队列每年接受神经学和 神经心理评估，特别独特，因为它由大约45%的非裔美国人组成。这 这项研究对少数民族人群尤其相关，因为非洲裔美国人几乎是少数民族人口的两倍 作为非西班牙裔美国白人，导致认知能力下降；然而，人们所了解的大部分 痴呆症生物标记物是基于主要是非西班牙裔美国白人的研究样本。 这个项目的总体目标是开发一个增强的深度学习模型，以改进数据表示， 临床行为量表的亚型分类和预测及其在轻度认知领域的应用 阿尔茨海默病的损害(MCI)和痴呆(DAT)。

ABSTRACT Alzheimer’s disease and associated dementias are major public health challenges with a multifold increase in prevalence expected in the coming decades. Alzheimer’s disease is increasingly recognized as having network- level effects and interactions. In this project, we will develop a deep learning model to learn the latent representation of functional neuroimaging, in order to disentangle the underlying sources and better reconstruct the data. Deep learning approaches in fMRI have faced a common challenge on generalizability and explainability. To address these issues, the system will learn representations that can be decoded and interpreted as spatial patterns and temporal dynamics of brain networks; and be readily generalizable to different subjects, brain states, behavioral tasks, and disease conditions without a need to redesign or retrain the system from scratch. The proposed focus on Alzheimer’s disease is the first step in exploiting this notion for clinical application. We will leverage both publicly available large data (e.g., Human Connectome Project-Aging, Alzheimer’s Disease Neuroimaging Initiative) as well as the well-characterized longitudinal cohort of the NIA P30-funded Michigan Alzheimer’s Disease Research Center (MADRC); this cohort undergoes annual neurological and neuropsychological evaluations and is particularly unique since it consists of ~45% African Americans. This research is particularly relevant for ethnic minority populations since African Americans are almost twice as likely to develop cognitive decline as Non-Hispanic white Americans; yet most of what has been learned about dementia biomarkers is based on study samples that are primarily Non-Hispanic white Americans. The overall goal of this project is to develop an enhanced deep learning model for improved data representation, subtype classification and prediction of clinical behavioral measures and apply it to the domain of mild cognitive impairment (MCI) and dementia of the Alzheimer’s Type (DAT).