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Computational modeling of dynamic causal brain circuits underlying cognitive dysfunction in Alzheimer's disease

阿尔茨海默病认知功能障碍的动态因果脑回路的计算模型

基本信息

批准号：
10301331
负责人：
VINOD MENON
金额：
$ 200.62万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-07-01 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10301331
关键词：
Address Affect Algorithmic Software Alzheimer&apos s Disease American Amyloid beta-Protein Area Bayesian Analysis Bayesian Modeling Brain Brain region Characteristics Clinical Cognition Cognitive Communities Computer Models Data Development Disease Episodic memory Foundations Functional Magnetic Resonance Imaging Functional disorder Funding Goals Human Impaired cognition Knowledge Link Medial Memory Memory impairment Methods Mission Monte Carlo Method Nerve Degeneration Neurosciences Pathology Pattern Positron-Emission Tomography Progressive Disease Reproducibility Research Resource Sharing Risk Rodent Sampling Short-Term Memory Signal Transduction Software Tools System Techniques Temporal Lobe United States National Institutes of Health Validation Wisconsin behavioral impairment brain research clinical phenotype computerized tools connectome disability dynamic system human disease in vivo innovation mortality risk negative affect neuropathology novel optogenetics tau Proteins temporal measurement tool

项目摘要

Project Abstract Alzheimer’s disease (AD) affects over 5.7 million Americans and is expected to rise to nearly 14 million people by 2060, as the number of people living with this disease doubles every 5 years. AD is a progressive and severely debilitating disease that negatively affects cognitive and memory function and is linked to increased disability in everyday functioning and risk of mortality. Neurodegeneration of focal brain areas in AD progressively impacts large-scale brain circuits, leading to significant cognitive and behavioral impairments. However, little is known regarding aberrant context-dependent dynamic causal interactions between distributed brain regions, and their links to cognitive and memory impairments and neuropathology, across AD clinical stages. Leveraging a productive and high-impact line of research in the current project period, we now propose to address critical gaps in our knowledge of functional circuit mechanisms underlying cognitive dysfunction in AD using innovative computational tools. Our first major goal is to continue to address critical unmet needs in human brain research by developing and validating novel computational tools for identifying context-dependent dynamic causal interactions between distributed brain regions. Building on progress in the current project period, we will further develop novel Multivariate Dynamic Systems Identification-Hamiltonian Monte Carlo techniques taking advantage of recent advances in Bayesian modeling and inferencing. Our computational tools will be validated using optogenetic stimulation with whole-brain fMRI, and stability analysis of normative Human Connectome Project data. Our second major goal is to use MDSI-HMC to investigate aberrancies in dynamic causal circuits underlying cognitive and memory impairment in AD. Our system neuroscience approach will target four key brain systems implicated in AD: default mode network, medial temporal lobe, and two frontal control systems anchored in the frontoparietal and salience networks. To achieve our goals, we will leverage clinical, phenotypic, cognitive, experimental, and state-of-the-art fMRI, and beta amyloid (Aβ) and tau PET, data from multiple NIH-funded AD-specific Human Connectome Projects. Our proposed studies will advance foundational knowledge of cognitive and memory-related circuits across AD clinical stages and their links to neuropathology. More generally, our proposed studies will also contribute novel tools for examining dynamical causal circuits underlying human brain function and dysfunction. The proposed studies are highly relevant to the NIH Focus on AD and PAR- 10-070 which call for innovative characterization of functional brain circuits altered in AD. More broadly, the proposed studies are relevant to the mission of the NIH to encourage development and dissemination of innovative advanced computational tools for clinical neuroscience. We will disseminate our algorithms and software tools to the research community as we have done in the current project period.

项目摘要阿尔茨海默病（AD）影响着超过570万美国人，预计将增加到近1400万人到2060年，因为患有这种疾病的人数每5年翻一番。AD是一种渐进的，严重衰弱性疾病，对认知和记忆功能产生负面影响，并与增加日常功能残疾和死亡风险。AD患者局灶性脑区的神经退行性变逐渐影响大规模的大脑回路，导致严重的认知和行为障碍。然而，很少有人知道异常的上下文相关的动态因果关系之间的相互作用，分布的大脑区域，以及它们与认知和记忆障碍和神经病理学的联系， AD临床分期在当前项目中利用富有成效且影响力大的研究系列在此期间，我们现在建议解决我们对功能电路机制的知识中的关键空白，认知功能障碍的AD使用创新计算工具。我们的第一个主要目标是继续通过开发和验证新的计算工具，解决人类大脑研究中未满足的关键需求用于识别分布式大脑区域之间依赖于上下文的动态因果相互作用。建筑在当前项目期间，我们将进一步开发新的多变量动态系统利用贝叶斯建模最新进展的哈密尔顿蒙特卡罗技术和推理。我们的计算工具将使用全脑光遗传学刺激进行验证。 fMRI和规范人类连接组计划数据的稳定性分析。我们的第二个主要目标是利用 MDSI-HMC研究认知和记忆基础的动态因果回路中的异常在AD中受损。我们的系统神经科学方法将针对与AD相关的四个关键大脑系统：默认模式网络，内侧颞叶，和两个额叶控制系统锚定在额顶叶和显着网络。为了实现我们的目标，我们将利用临床、表型、认知、实验和最先进的功能磁共振成像，β淀粉样蛋白（Aβ）和tau PET，数据来自多个NIH资助的AD特异性人类连接组计划。我们提出的研究将推进认知和 AD临床阶段的记忆相关回路及其与神经病理学的联系。一般来说，我们拟议的研究也将有助于新的工具，检查动态因果电路背后的人类脑功能和功能障碍。拟议的研究与NIH对AD和PAR的关注高度相关， 10-070，其要求对AD中改变的功能性脑回路进行创新表征。更广泛地说，建议的研究与NIH的使命有关，即鼓励开发和传播临床神经科学的创新先进计算工具。我们将传播我们的算法，软件工具的研究社区，因为我们已经在目前的项目期间所做的。

项目成果

期刊论文数量（35）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Dynamic causal brain circuits during working memory and their functional controllability.

DOI：
10.1038/s41467-021-23509-x
发表时间：
2021-06-29
期刊：
Nature communications
影响因子：
16.6
作者：
Cai W;Ryali S;Pasumarthy R;Talasila V;Menon V
通讯作者：
Menon V

Time-Varying Functional Connectivity Decreases as a Function of Acute Nicotine Abstinence.

DOI：
10.1016/j.bpsc.2020.10.004
发表时间：
2021-04
期刊：
Biological psychiatry. Cognitive neuroscience and neuroimaging
影响因子：
0
作者：
Fedota JR;Ross TJ;Castillo J;McKenna MR;Matous AL;Salmeron BJ;Menon V;Stein EA
通讯作者：
Stein EA

Temporal Dynamics and Developmental Maturation of Salience, Default and Central-Executive Network Interactions Revealed by Variational Bayes Hidden Markov Modeling.

差异贝叶斯隐藏的马尔可夫建模揭示的显着性，默认和中央连续网络相互作用的时间动力和发展成熟。