Brain entropy mapping in Alzheimer's Disease

抽象的。阿尔茨海默病(AD)已经影响了数千万人，但仍然无法治愈。这个 阿尔茨海默病的主要危险因素是衰老，由于进行性脑损伤而导致高脑熵。值得注意的是， 大脑不断消耗大量能量来维持其功能的完整性，很可能会产生大量的 “储备”，以抵消高熵。这个储备区的故障可能预示着疾病的临界点 转换和进步。储备故障可以通过补偿结果来衡量：功能 脑熵(FBEN)，它可能揭示弥合AD之间知识鸿沟的关键信息 病理和临床症状：AD病理早在AD症状出现之前就开始了，可能不会导致痴呆， 并可能为疾病的早期检测提供脑标志物。这项计划是为了表征fBEN在 正常衰老和AD连续体并检验倒U形fBEN模型：fBEN随着年龄和AD的增加而增加 正常衰老中的病理改变，但在AD的连续过程中减少。我们将使用大型现有的 静息状态功能磁共振成像(RsfMRI)数据。我们的团队在2010年开始了基于rsfMRI的fBEN映射，并发布了第一个 开源的fBEN映射工具。该工具已广泛应用于许多神经科学和翻译领域 学习。为了能够扩展到大数据，我们将在这个项目中开发一个进一步的加速版本。在目标1中，我们 将使用公共数据库中2000多名健康年轻人和老年人的数据来计算fBEN 研究fBEN与年龄、教育程度和认知功能的关系。我们假设大脑有 与储备相关的网络，其fBEN随着受教育年限的增加而降低，并与 大脑功能，表明在这个网络中低fBEN是大脑储备的一个指标。目标2将描述fBEN的特征 并评估其与AD病理和临床症状的关系。我们假设有一个很强的 病理与疾病相互作用对fBEN的影响：fBEN随年龄增长而增加，在正常衰老和较低年龄组中，随着病理改变而增加 FBEN与更好的认知相关；在AD连续体中，这些关联将被颠倒，因此fBEN将 病理水平越低，fBEN越低，认知功能越差。目标3将评估可行性 在AIMS 1和2中阐明的区域的基线fBEN用于早期疾病检测。因为fBEN可能只会 反映AD的某些功能异常，我们将fBEN与其他成像生物标记物结合在一起 以达到更高的预测精度。我们还将构建一个使用fBEN预测AD的模型 正常衰老的病理学。本项目的临床效果包括：1)验证假设的fBEN模型 将有助于描述AD的病理和临床差异；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疾病的早期发现；4)AD 病理预测对于正常衰老和AD管理具有重要的临床价值 测量要么令人痛苦，要么太昂贵。这个新项目的可行性和许多新的 我们的大量初步数据初步证明了这些假设。

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.