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.

数字技术可以在阿尔茨海默氏症的预测、早期发现和跟踪方面产生巨大影响 疾病的发展。特别是，需要开发能够检测早期变化的数字生物标记物 在认知症状和/或脑生物标志物出现之前的脑功能。脑电波是一个令人信服的 AD早期“数字生物标记物”的候选者，因为已知许多脑电特征与AD相关 进展和基础生物标记物。不幸的是，只有有限的证据表明这些脑电波 目前构建的描述人口水平数据的衡量标准可以准确跟踪或预测AD 个人的进步。其中一个原因是，脑电信号有许多来源--和之间-- 在当前的分析方法中没有考虑到的对象差异，导致不精确的标记，只有 在人口层面有足够的统计能力。最近在神经信号方面取得了一些进展。 能够解决这些错误来源的处理，进而显著改进 脑电测量的精确度。在过去的几年里，我们的实验室取得了长足的进步 对于这些误差源，我们开发了新颖、复杂的信号处理算法，这些算法可以 提高脑电测量的精确度。通过这个项目的具体目标，我们试图提供 AD研究社区拥有一套强大的、可访问的信号处理软件工具，将 显著提高脑电衍生的与AD进展相关的生物标志物的精确度和质量。