Advanced signal processing methods for neural data analysis to support development of brain dynamic biomarkers for research and clinical applications in patients with Alzheimer's and related dementias

用于神经数据分析的先进信号处理方法，支持开发大脑动态生物标志物，用于阿尔茨海默氏症和相关痴呆症患者的研究和临床应用

• 批准号: 10739673
• 负责人: Patrick L. Purdon
• 金额: $ 130.38万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10739673
• 关键词: Algorithms; Alzheimer' s Disease; Alzheimer' s disease pathology; Alzheimer' s disease patient; Alzheimer' s disease related dementia; Alzheimer’s disease biomarker; Amyloid; Amyloid beta-Protein; Articulation; Biological Markers; Brain; Clinical; Clinical Trials; Cognitive; Communities; Data; Data Analyses; Development; Device or Instrument Development; Digital biomarker; Disease Progression; EP300 gene; Early Diagnosis; Elderly; Electroencephalography; Error Sources; Event-Related Potentials; Frequencies; Goals; Individual; Magnetic Resonance Imaging; Markov Chains; Measurement; Measures; Methods; Modeling; Morphologic artifacts; National Institute on Aging; Nerve Degeneration; Neurobehavioral Manifestations; Noise; Pharmaceutical Preparations; Phase; Population; Research; Rest; Signal Transduction; Sleep; Software Tools; Source; Space Models; Structure; Technology; Therapeutic; Thick; Time; Variant; biomarker development; clinical application; cognitive performance; design; digital technology; improved; innovation; insight; interest; learning algorithm; neural; neuroimaging marker; neurophysiology; neurotransmission; non rapid eye movement; novel; response; signal processing; source localization; spatial relationship; task analysis; tau Proteins; tool

Digital technologies can have enormous impact in the prediction, early detection, and tracking of Alzheimer’s disease progression. In particular, there is a need to develop digital biomarkers that can detect early changes in brain function before the onset of cognitive symptoms and/or brain biomarkers. The EEG is a compelling candidate for an early “digital biomarker” of AD as numerous EEG features are known to be correlated with AD progression and fundamental biomarkers. Unfortunately, there is limited evidence that these same EEG measures, as currently constructed to describe population-level data, can accurately track, or predict AD progression in individuals. One reason for this is that EEG signals have many sources of with- and between- subject variation that are not accounted for in current analysis methods, leading to imprecise markers that only have sufficient statistical power at the population-level. There have been recent advances in neural signal processing that make it possible to account for these sources of error and in turn dramatically improve the precision of EEG-derived measures. Over the past several years our lab has made significant strides to account for these sources of error leading us to develop novel, sophisticated signal processing algorithms that can enhance the precision of EEG derived measures. Through the specific aims of this project, we seek to provide the AD research community with a suite of powerful, accessible signal processing software tools that will dramatically enhance the precision and quality of EEG-derived biomarkers related to AD progression.

数字技术可能会对阿尔茨海默氏症的预测，早期检测和跟踪产生巨大影响 疾病进展。特别是，有必要开发数字生物标志物，可以检测到早期的变化 认知症状和/或脑生物标志物发作之前的大脑功能。脑电图是一个引人注目的 众所周知，众多脑电图功能与AD相关 进展和基本生物标志物。不幸的是，有限的证据表明这些脑电图 目前为描述人口级数据而构建的措施可以准确跟踪或预测广告 个体的进展。原因之一是，脑电图信号具有许多源 当前分析方法中未考虑的主题变化，导致暗示标记仅 在人群级别具有足够的统计能力。神经信号的最新进展 处理这些错误，从而使这些错误来源成为可能，从而显着改善 EEG衍生的措施的精度。在过去的几年中，我们的实验室取得了长足的进步 对于这些错误来源，导致我们开发了可以 提高EEG得出的措施的精度。通过该项目的具体目的，我们试图提供 广告研究社区，具有一套功能强大，可访问的信号处理软件工具，这些工具将 动态增强与AD进展相关的EEG衍生生物标志物的精度和质量。