fMRI physiological signatures of aging and Alzheimer's Disease

衰老和阿尔茨海默病的功能磁共振成像生理特征

• 批准号: 10361105
• 负责人: Catherine Elizabeth Chang
• 金额: $ 105.56万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2024-09-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10361105
• 关键词: Adult; Age; Aging; Alzheimer disease detection; Alzheimer' s Disease; Big Data; Blood Vessels; Brain; Brain Mapping; Brain Stem; Cardiac; Cell Nucleus; Cerebrovascular Circulation; Cerebrovascular system; Code; Cognitive; Communities; Complex; Coupling; Data; Data Set; Databases; Disease; Early Diagnosis; Emotional; Emotions; Exhibits; Frequencies; Functional Imaging; Functional Magnetic Resonance Imaging; Health; Heart Rate; Human; Individual; Individual Differences; Investigation; Link; Longevity; Machine Learning; Measurement; Mediating; Methodology; Methods; Modeling; Neurocognitive; Neurons; Neurosciences; Participant; Pattern; Peripheral; Phenotype; Physiological; Physiology; Process; Pulse Oximetry; Research Personnel; Respiration; Rest; Role; STEM research; Series; Signal Transduction; Source; Structure; System; Techniques; Time; Validation; Variant; Work; age difference; age related; base; cerebrovascular; deep learning; denoising; functional gain; gray matter; heart rate variability; indexing; insight; mind body interaction; novel; pathological aging; relating to nervous system; respiratory; response; stem

PROJECT SUMMARY/ABSTRACT The growing availability of large functional magnetic resonance imaging (fMRI) datasets has enabled new investigations into functional systems of the human brain. A challenge – but also opportunity – of fMRI arises from the fact that BOLD signal stems from multiple intertwined neural and physiological sources. One major contributor to fMRI signals arises from slow (<0.15 Hz) fluctuations in respiration volume (RV) and heart rate (HR); these systemic physiological fluctuations can account for a substantial proportion of fMRI signals across gray matter, and exhibit spatial patterns that overlap with functional networks. While often treated as a confound, the components of fMRI data linked with systemic physiology may itself present useful information about brain function and physiology, enabling novel investigation of brain vasculature, autonomic function, and brain-body interactions. However, many existing fMRI datasets lack concurrent physiological recordings, and current data-driven techniques do not unambiguously resolve low-frequency physiological signal sources without peripheral cardiac and respiratory recordings for reference. This proposal conducts novel analyses to establish associations between fMRI physiological responses, brain networks, and neurocognitive function. Further, new techniques are proposed for extracting RV and HR time series directly from fMRI data, thereby enriching existing fMRI datasets with missing physiological information. Through analysis of large, public datasets, we will: 1) optimize and validate a deep learning technique for reconstructing physiological time series from resting-state fMRI data alone, which generalizes to participants across the adult lifespan; and 2) relate brain-wide fMRI physiological features to age and phenotypic variation; and 3) probe the value of fMRI physiological responses as early markers of Alzheimer's Disease. We will make all of the resulting signals, models, and code readily available to the community, so that researchers can apply and extend our methods to enhance the value of many existing datasets. Through approaches for resolving neural and physiological sources underlying fMRI signal dynamics, this project also has implications for increasing the precision of fMRI for mapping brain circuits at the level of the individual.

项目摘要/摘要 大型功能磁共振成像（fMRI）数据集的可用性不断增长。 研究人脑功能系统。 fMRI的挑战（但也是机会）出现 从来自多个相互交织的神经和生理来源的大胆信号植物。一个主要 fMRI信号的贡献是由呼吸体积（RV）和心率缓慢（<0.15 Hz）的慢（<0.15 Hz）引起的 （HR）;这些系统性的生理波动可以占跨MMRI信号的很大比例 灰质和与功能网络重叠的裸露空间模式。虽然经常被视为 混淆，与全身生理相关的fMRI数据的组成部分本身可能会提供有用的信息 关于大脑功能和生理学，可以对脑脉管系统，自主功能和 脑体相互作用。但是，许多现有的fMRI数据集缺乏并发的生理记录，并且 当前数据驱动的技术不会明确解决低频生理信号源 没有外周心脏和呼吸记录供参考。该提案进行了新的分析 建立fMRI身体反应，大脑网络和神经认知功能之间的关联。 此外，提出了直接从fMRI数据中提取RV和HR时间序列的新技术，从而 通过缺少物理信息来丰富现有的fMRI数据集。通过分析大型公众 数据集，我们将：1）优化和验证一种重建生理时间的​​深度学习技术 仅从静止状态fMRI数据中进行的序列，该数据概括为整个成人寿命的参与者；和2） 将大脑范围的fMRI物理特征与年龄和表型变异相关联； 3）探测fMRI的值 生理反应是阿尔茨海默氏病的早期标记。我们将制作所有由此产生的信号， 模型和易于提供的代码，以便研究人员可以应用并将我们的方法扩展到 增强许多现有数据集的价值。通过解决神经和生理的方法 fMRI信号动力学的来源，该项目也对提高fMRI的精度有影响 用于在个体水平上绘制脑电路。