fMRI physiological signatures of aging and Alzheimer's Disease

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，使新的 对人脑功能系统的研究。功能磁共振成像面临挑战，但也带来了机遇 大胆的信号来源于多个相互交织的神经和生理来源。一大专业 功能磁共振信号的贡献者来自呼吸量(RV)和心率的缓慢(&lt；0.15赫兹)波动 (HR)；这些全身性生理波动可占整个fMRI信号的很大比例 灰色物质，并表现出与功能网络重叠的空间模式。虽然经常被视为 令人困惑的是，与系统生理学相关的功能磁共振数据组件本身可能提供了有用的信息 关于大脑功能和生理学，使脑血管、自主神经功能和 大脑与身体的相互作用。然而，许多现有的fMRI数据集缺乏并发的生理记录，并且 当前的数据驱动技术不能明确地解析低频生理信号源 没有外周心脏和呼吸记录可供参考。这项提案进行了新颖的分析 建立功能磁共振生理反应、大脑网络和神经认知功能之间的联系。 此外，还提出了直接从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。通过分析大型、公众 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 系列仅来自静息状态的fMRI数据，它概括了参与者在整个成年人的寿命；和2) 全脑fmri生理特征与年龄和表型变异的关系；3)探讨fmri的应用价值。 生理反应作为阿尔茨海默病的早期标志。我们将制作所有产生的信号， 模型和社区随时可用的代码，以便研究人员可以将我们的方法应用和扩展到 提升许多现有数据集的价值。通过解决神经和生理问题的方法 来源：fmri信号动力学，这个项目对提高fmri的精确度也有意义。 用于在个体水平上绘制大脑回路。

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.