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.

项目总结/摘要 大型功能性磁共振成像（fMRI）数据集的日益可用性使新的 研究人类大脑的功能系统。功能磁共振成像的挑战--但也是机遇--出现了 这是因为BOLD信号来源于多个相互交织的神经和生理来源。一个主要 fMRI信号的一个贡献者来自呼吸量（RV）和心率的缓慢（<0.15 Hz）波动 (HR)这些系统性生理波动可以占fMRI信号的很大比例， 灰质，并表现出与功能网络重叠的空间模式。虽然经常被视为 然而，与系统生理学相关的功能磁共振成像数据的组成部分本身可能提供有用的信息 关于脑功能和生理学，使脑血管，自主神经功能， 脑-体相互作用然而，许多现有的fMRI数据集缺乏并发的生理记录， 当前的数据驱动技术不能明确地分辨低频生理信号源 没有外周心脏和呼吸记录作为参考。该提案进行了新颖的分析， 建立功能磁共振成像生理反应，大脑网络和神经认知功能之间的联系。 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 用缺失的生理信息丰富现有的fMRI数据集。通过对大型公共 数据集，我们将：1）优化和验证用于重建生理时间的深度学习技术 仅从静息状态fMRI数据中获得的系列数据，该数据适用于整个成人寿命期的参与者;以及2） 将全脑fMRI生理特征与年龄和表型变化联系起来; 3）探讨fMRI的价值 生理反应作为阿尔茨海默病的早期标志。我们将使所有产生的信号， 模型和代码随时可供社区使用，以便研究人员可以应用和扩展我们的方法， 提高现有数据集的价值。通过解决神经和生理问题的方法 作为fMRI信号动力学的潜在来源，该项目也对提高fMRI的精度有影响。 来绘制个体层面的大脑回路。