Brain entropy mapping in Alzheimer's Disease

阿尔茨海默氏病的脑熵图

• 批准号: 10461974
• 负责人: Ze Wang
• 金额: $ 58.38万
• 依托单位: UNIVERSITY OF MARYLAND BALTIMORE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10461974
• 关键词: Affect; Age; Aging; Alzheimer' s Disease; Alzheimer' s disease brain; Alzheimer' s disease model; Alzheimer' s disease pathology; Alzheimer' s disease risk; Alzheimer’s disease biomarker; Amyloid beta-Protein; Area; Biological Markers; Brain; Brain Diseases; Brain Injuries; Clinical; Cognition; Cognitive; Complex; Consumption; Data; Data Set; Dementia; Detection; Deterioration; Disease; Disease Management; Education; Elderly; Entropy; Failure; Financial compensation; Functional Magnetic Resonance Imaging; Future; Hippocampus (Brain); Impaired cognition; Individual; Intervention; Knowledge; Language; Literature; Magnetic Resonance Imaging; Maps; Measurement; Measures; Medial; Memory; Modeling; Normal Range; Pain; Pathology; Patients; Pattern; Persons; Positron-Emission Tomography; Prefrontal Cortex; Prevalence; Public Health; Publishing; Reporting; Research; Research Personnel; Rest; Role; Severity of illness; Shapes; Symptoms; Temporal Lobe; Testing; Woman; Work; aging population; base; cognitive control; cognitive function; cognitive reserve; deep learning; disability; experience; functional outcomes; glucose metabolism; imaging biomarker; improved; large scale data; machine learning method; men; method development; mild cognitive impairment; neuroimaging; neuroimaging marker; neuromechanism; normal aging; novel; open source; potential biomarker; predictive modeling; public database; sex; support vector machine; tau Proteins; therapy development; tool; trait; translational study; uptake

ABSTRACT. Alzheimer’s Disease (AD) has affected tens of millions of people but remains incurable. The major risk factor for AD is aging, which entails high brain entropy due to the progressive brain damages. Notably, the brain constantly consumes a large amount of energy to maintain its functional integrity, likely creating a big “reserve” to counteract the high entropy. Malfunctions of this reserve may indicate a critical point of disease conversion and progression. Reserve malfunction can be measured by the compensation outcome: functional brain entropy (fBEN), which may reveal critical information for bridging the knowledge gap between AD pathology and clinical symptoms: AD pathology begins long before AD symptoms and may not lead to dementia, and may provide a brain marker for early disease detection. This project is proposed to characterize fBEN in normal aging and the AD continuum and test an inverse U-shape fBEN model: fBEN increases with age and AD pathology in normal aging but decreases in the AD continuum. We will test the model using large existing resting state fMRI (rsfMRI) data. Our group started rsfMRI-based fBEN mapping in 2010 and released the first open-source fBEN mapping tool. The tool has been widely used in many neuroscientific and translational studies. To be scalable for large data, we will develop a further accelerated version in this project. In Aim 1, we will calculate fBEN using data from 2000+ young and old healthy individuals from public databases and examine the associations of fBEN to age, education, and cognitive function. We hypothesize that the brain has a reserve-related network whose fBEN decreases with years of education and is negatively correlated with brain function, suggesting low fBEN in this network as an indicator of brain reserve. Aim 2 will characterize fBEN in AD and assess its associations to AD pathology and clinical symptoms. We hypothesize that there is a strong pathology vs disease interaction on fBEN: fBEN increases with age and pathology in normal aging and lower fBEN correlates with better cognition; those associations will be reversed in the AD continuum so that fBEN will decrease with pathology level and lower fBEN will correlate to worse cognition. Aim 3 will evaluate the feasibility of baseline fBEN in regions elucidated in Aims 1 and 2 for early disease detection. Because fBEN may only reflect some part of the functional abnormalities in AD, we will combine fBEN with other imaging biomarkers in the model in order to achieve higher prediction accuracy. We will also build a model to use fBEN to predict AD pathology in normal aging. The clinical impact of this project includes: 1) testing the hypothetical fBEN model will help delineate the pathology vs clinical discrepancy in AD; 2) finding the disease related fBEN patterns may provide a potential intervention target; 3) testing the prediction model will aid early disease detection; 4) AD pathology prediction is of great clinical value for normal aging and AD management as current AD pathology measurement is either painful or too expensive. The feasibility of this novel project and the many new hypotheses have been initially demonstrated by our extensive preliminary data.

抽象的。阿尔茨海默氏病（AD）影响了数千万人，但仍然无法治愈。这 AD的主要危险因素是衰老，由于大脑的进行性损害，它需要高大脑熵。尤其， 大脑不断消耗大量能量来维持其功能完整性，可能会产生大量 “储备”以抵消高熵。该储备的故障可能表明疾病的关键点 转换和进展。储备故障可以通过补偿结果来衡量：功能 脑部熵（FBEN），它可能揭示了关键信息，以弥合AD之间的知识差距 病理和临床症状：AD病理学早在AD症状之前就开始了，可能不会导致痴呆， 并可能为早期疾病检测提供大脑标记。该项目的建议是在 正常衰老和AD连续体并测试逆U形FBEN模型：FBEN随着年龄和AD而增加 正常衰老的病理学，但在AD连续体中减少。我们将使用大型现有 静止状态fMRI（RSFMRI）数据。我们的小组于2010年启动了基于RSFMRI的FBEN映射，并发布了第一个 开源FBEN映射工具。该工具已被广泛用于许多神经科学和翻译 研究。为了使大数据可扩展，我们将在此项目中开发进一步的加速版本。在AIM 1中，我们 将使用来自公共数据库的2000多名年轻和老年人健康的人的数据来计算FBEN， 检查FBEN与年龄，教育和认知功能的关联。我们假设大脑有 一个与后备相关的网络，其FBEN随着多年的教育而减少，并且与 大脑功能，表明该网络中的FBEN是大脑储备的指标。 AIM 2将以FBEN为特征 在AD和评估其与AD病理学和临床症状的关联中。我们假设有一个强大的 病理与FBEN上的疾病相互作用：FBEN随着正常衰老和较低的病理的年龄和病理而增加 Fben与更好的认知相关；这些关联将在广告连续体中逆转，以便FBEN将 随着病理水平的降低和较低的FBEN将与认知较差的认知相关。 AIM 3将评估可行性 在目标1和2中阐明的区域中的基线FBEN用于早期疾病检测。因为fben可能只 反映AD功能异常的某些部分，我们将FBEN与其他成像生物标志物相结合 该模型以实现更高的预测准确性。我们还将建立一个模型来使用FBEN预测广告 正常衰老的病理。该项目的临床影响包括：1）测试假设的FBEN模型 将有助于描绘AD中的病理与临床差异； 2）发现与疾病相关的FBEN模式可能 提供潜在的干预目标； 3）测试预测模型将有助于早期疾病检测； 4）广告 病理预测作为当前的AD病理，正常衰老和广告管理具有巨大的临床价值 测量要么痛苦或太昂贵。这个小说项目的可行性和许多新的 我们的广泛初步数据最初证明了假设。