Collaborative Research: SCH: Assessment of Cognitive Decline using Multimodal Neuroimaging with Embedded Artificial Intelligence

合作研究：SCH：使用多模态神经影像和嵌入式人工智能评估认知衰退

• 批准号: 10438005
• 负责人: Roee Holtzer
• 金额: $ 30.19万
• 依托单位: VILLANOVA UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10438005
• 关键词: Age-associated memory impairment; Alzheimer' s Disease; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Artificial Intelligence; Attention; Base of the Brain; Behavioral; Biochemical Genetics; Biological Markers; Brain; Brain imaging; Caregivers; Classification; Cognitive; Collection; Computer software; Data; Data Science; Data Set; Dementia; Detection; Development; Devices; Diagnostic Procedure; Early Diagnosis; Elderly; Electroencephalogram; Electroencephalography; Electrophysiology (science); Emotional; Environment; Financial cost; Frequencies; Genetic Markers; Human Experimentation; Human Resources; Impaired cognition; Individual; Lead; Learning; Literature; Machine Learning; Memory; Metabolic; Methods; Modality; Monitor; Neuropsychological Tests; Process; Psyche structure; Research; Risk; Societies; Statistical Data Interpretation; Stimulus; System; Techniques; Testing; Time; Validation; base; behavior measurement; behavioral response; cognitive performance; cognitive task; cognitive testing; design; diagnostic biomarker; executive function; feature extraction; feature selection; functional near infrared spectroscopy; handheld mobile device; hemodynamics; imaging modality; innovation; machine learning method; machine learning model; mild cognitive impairment; mobile application; multimodal neuroimaging; multimodality; neuroimaging; novel; novel marker; relating to nervous system; signal processing; tool

Summary: Alzheimer’s Disease (AD) and Alzheimer’s Disease-Related Dementias (ADRD) are highly prevalent among older individuals throughout the world. The adverse impact of cognitive decline, ranging from mild cognitive impairment (MCI) to AD and ADRD, presents not only a prohibitive financial cost but also physical, mental, and emotional burden to older adults, their caregivers, and society. MCI is a well-established risk factor for AD. However, traditional diagnostic procedures and biomarkers have limited utility in identifying alterations in brain mechanisms that underlie the cognitive decline observed in MCI. While the literature concerning neuroimaging correlates of MCI, AD and ADRD is considerable, traditional brain imaging methods are expensive, restrictive, and typically conducted separately. Moreover, research using multimodal noninvasive neuroimaging methods that can be utilized in naturalistic settings to detect brain-based signatures of MCI has been limited. Developing tools to extract such signatures can lead to the identification of novel biomarkers that can guide the development of precise, and individualized assessment and treatment of age-related cognitive decline and dementia. In this project, we will develop a toolchain for the assessment of MCI using multimodal neuroimaging and machine learning (ML) methods. We propose three specific aims: (1) to develop a comprehensive cognitive testing battery sensitive to MCI in a mobile software synchronized with multimodal functional near infrared spectroscopy and electroencephalography (fNIRS-EEG) based neuroimaging system that can concurrently provide electrophysiological, hemodynamic and behavioral measures; (2) to extract, select, and validate the multitude of within and across modality biomarkers from fNIRS-EEG data in temporal, spatial, spectral, and complexity domains together with the behavioral ones; (3) to develop a comprehensive multimodal ML approach to detect MCI based on fNIRS-EEG and behavioral features. Developing a mobile application that combines fNIRS and EEG on one platform that could be used in less expensive and restrictive testing environments to determine functional brain alterations in older adults with MCI is very innovative. The findings of this project can lead to a transformation in early detection and monitoring of cognitive decline in older adults at risk of developing AD.

摘要：阿尔茨海默病(AD)和阿尔茨海默病相关痴呆(ADRD)在世界各地的老年人中非常普遍。认知衰退的负面影响，从轻度认知障碍(MCI)到AD和ADRD，不仅给老年人、他们的照顾者和社会带来了令人望而却步的经济成本，还给他们带来了身体、精神和情感上的负担。MCI是AD的一个公认的危险因素。然而，传统的诊断程序和生物标记物在识别大脑机制变化方面的作用有限，这些变化是MCI认知能力下降的基础。虽然有关MCI、AD和ADRD之间神经成像相关性的文献很多，但传统的脑成像方法昂贵、限制性强，通常是单独进行的。此外，使用可在自然环境中使用的多模式非侵入性神经成像方法来检测MCI的基于大脑的特征的研究一直受到限制。开发提取这种特征的工具可以导致识别新的生物标记物，这些生物标记物可以指导对年龄相关性认知功能减退和痴呆的精确和个性化评估和治疗。 在这个项目中，我们将开发一个工具链，用于使用多模式神经成像和机器学习(ML)方法评估MCI。我们提出了三个具体目标：(1)在与多模式功能近红外光谱和脑电信号(fNIRS-EEG)同步的移动软件中开发对MCI敏感的综合认知测试单元，该系统可以同时提供电生理、血流动力学和行为测量；(2)从fNIRS-EEG数据中提取、选择和验证多个通道内和跨通道的生物标志物，以及行为特征；(3)开发基于fNIRS-EEG和行为特征的综合多模式ML检测MCI。 开发一种在一个平台上结合fNIRS和EEG的移动应用程序，可以在成本较低和限制较少的测试环境中使用，以确定患有MCI的老年人的功能性大脑变化，这是非常具有创新性的。该项目的发现可能导致早期发现和监测有患阿尔茨海默病风险的老年人认知能力下降的转变。