Novel Bayesian linear dynamical systems-based methods for discovering human brain circuit dynamics in health and disease

新颖的——贝叶斯——线性——动态——基于系统的——方法——用于发现——人类——大脑——电路——健康和疾病的动力学

• 批准号: 9170593
• 负责人: VINOD MENON
• 金额: $ 38.68万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-30 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9170593
• 关键词: Address; Adult; Algorithms; Alzheimer' s Disease; BRAIN initiative; Basal Ganglia; Bayesian Modeling; Behavior; Brain; Brain region; Clinical; Cognition; Computational algorithm; Computer software; Computing Methodologies; Data; Data Set; Development; Disease; Factor Analysis; Functional Magnetic Resonance Imaging; Functional disorder; Funding; Goals; Health; Human; Impaired cognition; Individual Differences; Link; Mediating; Methods; Mission; Modeling; Neurocognitive; Neurosciences; Parkinson Disease; Patients; Performance; Pharmaceutical Preparations; Play; Process; Property; Public Health; Research; Rodent; Short-Term Memory; System; Techniques; Testing; Time; United States National Institutes of Health; abstracting; base; behavioral outcome; brain research; cognitive function; computerized tools; connectome; dynamic system; human data; in vivo; innovation; nervous system disorder; novel; optogenetics; public health relevance; simulation; temporal measurement; tool; vector

Project Summary/Abstract Understanding how the human brain produces cognition ultimately depends on precise quantitative characterization of context-dependent dynamic functional networks (DFN) that transiently link distributed brain regions. Progress in achieving this goal has been limited due to a lack of theoretical frameworks for characterizing DFNs and appropriate computational methods to test them. Devising and validating computational methods for investigating DFNs in the human brain is thus of great significance. The first major goal of this proposal is to address a critical need in human brain research by developing novel algorithms for identifying DFNs and characterizing dynamic network interactions between distributed brain regions. To achieve this goal, we will develop and validate novel computational methods within the framework of Bayesian switching linear dynamical systems (BSDS) with vector autoregressive models (VAR) and factor analysis (FA) that overcome major limitations of existing methods for investigating dynamic interactions in the human brain. The second major goal of this proposal is to use BSDS to investigate DFNs underlying cognitive function in healthy adults, and in patients with Parkinson's disease (PD). Severe cognitive impairment is one of the most devastating behavioral outcomes in patients with PD, yet little is known about the temporal properties of dysfunctional neurocognitive systems in this debilitating disorder. The computational algorithms we propose to develop, validate, and apply will allow us to rigorously investigate brain dynamics that support critical cognitive functions and significantly advance our understanding of dynamic processes underlying human brain function and dysfunction. Our proposed studies will also, for the first time, investigate DFNs in simulated, rodent in vivo optogenetic fMRI, as well as human data using state-of-the-art (sub- second) high-temporal resolution fMRI data generated by the NIH-funded Stanford Alzheimer's Disease Research Center (ADRC), highlighting critical translational applications of our proposed methods. Our proposed studies will provide novel tools for investigating dynamic functional networks in the human brain, with innovative applications to the Human Connectome Project (HCP) and the study of neurological disorders and clinical neuroscience more broadly. The proposed studies are highly relevant to the mission of the BRAIN Initiative (RFA-EB-15-006), which calls for the development and dissemination of innovative computational tools for probing human brain function and dysfunction. Our computational tools will be widely disseminated to facilitate research into the dynamical aspects of human brain function.

项目摘要/摘要 了解人脑如何产生认知最终取决于精确的定量 瞬时链接分布的上下文依赖性动态功能网络（DFN）的表征 大脑区域。由于缺乏理论框架，实现这一目标的进展受到限制 表征DFN和适当的计算方法来测试它们。设计和验证 因此，用于研究人脑中DFN的计算方法具有重要意义。第一个专业 该建议的目标是通过开发新颖算法来满足人脑研究的关键需求 识别DFN并表征分布式大脑区域之间的动态网络相互作用。到 实现这一目标，我们将在贝叶斯框架内开发和验证新颖的计算方法 使用矢量自回归模型（VAR）和因子切换线性动力系统（BSD） 分析（FA）克服了现有方法用于研究动态相互作用的主要局限性 人脑。该提案的第二个主要目标是使用BSD调查基础DFN 健康成年人以及帕金森氏病（PD）的认知功能。严重的认知 损伤是PD患者中最具破坏性的行为结果之一，但对 这种衰弱障碍中神经认知系统功能失调的时间特性。计算 我们建议开发，验证和应用的算法将使我们能够严格研究大脑动态 支持关键的认知功能，并显着提高我们对动态过程的理解 潜在的人脑功能和功能障碍。我们提出的研究也将首次调查 模拟的，啮齿动物的体内光学遗传fMRI以及人类数据中的DFN以及使用最先进的人类数据（sub-- 第二）由NIH资助的斯坦福大学阿尔茨海默氏病产生的高阶段分辨率fMRI数据 研究中心（ADRC），强调了我们提出的方法的关键翻译应用。 我们提出的研究将为研究人类的动态功能网络提供新的工具 大脑，对人类连接项目（HCP）的创新应用和神经学研究 疾病和临床神经科学更广泛。拟议的研究与 大脑倡议（RFA-EB-15-006），呼吁开发和传播创新 用于探测人脑功能和功能障碍的计算工具。我们的计算工具将被广泛 传播以促进对人脑功能的动态方面的研究。