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

深度学习预测轻度认知障碍和阿尔茨海默氏症型痴呆

• 批准号: 10662094
• 负责人: Zhongming Liu
• 金额: $ 22.45万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10662094
• 关键词: 3-Dimensional; Address; African American population; Aging; Alzheimer' s Disease; Back; Behavior; Behavioral; Biological Markers; Brain; Classification; Cognitive; Complement; Data; Data Reporting; Data Set; Dementia; Disease; Elderly; Etiology; Evaluation; Functional Magnetic Resonance Imaging; Funding; Goals; Human; Image; Impaired cognition; Knowledge; Learning; Location; Longitudinal cohort; Measures; Michigan; Modeling; Neurologic; Neuropsychology; Not Hispanic or Latino; Pathology; Pattern; Phenotype; Prevalence; Public Health; Research; Rest; Sampling Studies; Source; Stress Tests; System; Techniques; Time; Training; Work; base; behavior measurement; behavior prediction; brain volume; caucasian American; clinical application; clinical predictors; cohort; connectome; deep learning; deep learning model; design; ethnic minority population; human data; improved; individual response; mild cognitive impairment; neuroimaging; open data; reconstruction; young adult

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).

抽象的 阿尔茨海默病和相关痴呆症是主要的公共卫生挑战，患病人数成倍增加 预计未来几十年阿尔茨海默病的患病率将越来越被认为与网络有关。 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。 功能神经影像的表示，以便理清潜在的来源并更好地重建 数据。 fMRI 中的深度学习方法在普遍性和可解释性方面面临着共同的挑战。 为了解决这些问题，系统将学习可以解码并解释为空间的表示 大脑网络的模式和时间动态；并且很容易推广到不同的主题、大脑 状态、行为任务和疾病状况，无需从头开始重新设计或重新训练系统。 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。 我们将利用公开的大数据（例如，人类连接组计划-衰老、阿尔茨海默病 神经影像倡议）以及 NIA P30 资助的密歇根州特征明确的纵向队列 阿尔茨海默病研究中心 (MADRC)；该队列每年接受神经学和 神经心理学评估，并且特别独特，因为它由约 45% 的非裔美国人组成。 研究与少数族裔人口尤其相关，因为非洲裔美国人的可能性几乎是少数族裔人口的两倍 作为非西班牙裔美国白人，认知能力下降； 痴呆症生物标志物基于主要是非西班牙裔美国白人的研究样本。 该项目的总体目标是开发增强的深度学习模型以改进数据表示， 临床行为测量的亚型分类和预测并将其应用于轻度认知领域 损伤（MCI）和阿尔茨海默氏型痴呆（DAT）。