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.

项目总结/摘要 理解人类大脑如何产生认知最终取决于精确的定量 上下文相关的动态功能网络（DFN）的表征， 大脑区域。实现这一目标的进展有限，原因是缺乏理论框架， 表征DFN和适当的计算方法来测试它们。设计和验证 因此，研究人脑中DFN的计算方法具有重要意义。第一主 该提案的目标是通过开发新的算法来解决人类大脑研究的关键需求， 识别DFN和表征分布式大脑区域之间的动态网络交互。到 为了实现这一目标，我们将在贝叶斯框架内开发和验证新的计算方法。 基于向量自回归模型和因子的切换线性动态系统 分析（FA），克服了现有方法的主要局限性，研究动态相互作用， 人脑该提案的第二个主要目标是使用BSDS来研究DFN的基础 健康成人和帕金森病（PD）患者的认知功能。严重的认知 损伤是PD患者最具破坏性的行为结果之一，但对PD患者的行为损伤知之甚少。 在这种使人衰弱的疾病中，功能失调的神经认知系统的时间特性。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力学 支持关键的认知功能，并大大促进我们对动态过程的理解， 潜在的人类大脑功能和功能障碍。我们提出的研究也将首次调查 DFN在模拟的啮齿动物体内光遗传学fMRI中，以及使用最新技术（亚 第二，由美国国立卫生研究院资助的斯坦福大学阿尔茨海默病研究所生成的高时间分辨率功能磁共振成像数据 研究中心（ADRC），强调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，创新应用于人类连接组计划（HCP）和神经学研究 疾病和临床神经科学更广泛。拟议的研究与以下使命高度相关： BRAIN倡议（RFA-EB-15-006），呼吁开发和传播创新的 用于探测人类大脑功能和功能障碍的计算工具。我们的计算工具将广泛地 传播，以促进对人类大脑功能的动力学方面的研究。