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CRCNS Research Proposal: Collaborative Research: Modeling and Manipulating Dynamic Network Activity in the Brain

CRCNS 研究提案：协作研究：建模和操纵大脑中的动态网络活动

基本信息

批准号：
1822606
负责人：
Shella Keilholz
金额：
$ 23.7万
依托单位：
Emory University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-15 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1822606&HistoricalAwards=false
关键词：
CRCNS Research Proposal Collaborative Modeling

项目摘要

CRCNS Research Proposal: Collaborative Research: Modeling and Manipulating Dynamic Network Activity in the BrainConnectome-based Dynamic Network Modeling (CDNM) is a recent approach in computational neuroscience, made possible by the availability of structural and functional brain connectivity data. This project aims to understand how the interaction between structure and dynamics of neural populations leads to brain functional networks and brain states. Understanding mechanistically and being able to predict how the combination of macroscale structure and local neural activity leads to complex whole-brain dynamics is a major research goal for every aspect of brain science, ranging from basic neuroscience to clinical psychiatry and neurology. This project can also have an important impact in understanding both how Major Depressive Disorder emerges from specific structural abnormalities, and the conditions under which Deep Brain Stimulation is an effective treatment. The developed methods can be also applied to numerous other mental and neurological disorders. The project will also develop and openly disseminate new computational models, and optimization methods for speeding up the simulation of complex CDNMs. The project consists of three Aims: 1) Leverage dynamic functional connectivity to further constrain and evaluate CDNM: The first goal is to clearly separate the parameterization of a CDNM from the evaluation of its accuracy. It is possible that several models, or parameterizations of the same model, lead to realistic average functional connectivity. However, not all of these models may be able to reproduce the more complex, dynamic functional connectivity patterns observed in practice. The project relies on state-of-the-art methods that infer dynamic functional connectivity between brain regions, applying these methods to both empirical data and CDNM-based simulation results. Each candidate CDNM model will be evaluated in terms of how well it can reproduce the dynamic FC patterns observed in empirical data. 2) Using CDNM to understand the connection between structural and functional connectivity in Major Depression Disorder: The ultimate test for any model is its predictive power. The project will utilize structural and functional connectivity data for a patient group that exhibits known and significant differences from healthy controls. Starting with the best model from Aim-1, that CDNM will be run on a perturbed connectome that captures the major structural abnormalities in depression. Then, the CDNM results will be analyzed to determine if the model can reproduce the FC abnormalities observed in the group of patients. 3) Modeling the effects of interventions such as deep brain stimulation: The use of this experimental treatment in depression is a ?network intervention?. CDNM can play a significant role in understanding how and when it works as an effective treatment. The effect of deep brain stimulation will be modeled by modifying either the local dynamics of certain regions or the weights of specific connections in the model, such as increasing or decreasing the weight of the connection. The project will investigate whether there is a specific weight adjustment with which the stimulated model produces dynamics that resemble the normal FC of healthy subjects. If that adjustment needs to be in a very narrow range, it might explain why deep brain stimulation is unsuccessful in some patients.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

CRCNS研究提案：合作研究：基于脑连接组的动态网络建模（CDNM）是计算神经科学中的一种新方法，由于结构和功能脑连接数据的可用性而成为可能。该项目旨在了解神经群体的结构和动力学之间的相互作用如何导致大脑功能网络和大脑状态。从机制上理解并能够预测宏观结构和局部神经活动的组合如何导致复杂的全脑动力学是脑科学各个方面的主要研究目标，从基础神经科学到临床精神病学和神经学。这个项目也可以在理解重度抑郁症如何从特定的结构异常中出现，以及脑深部电刺激是有效治疗的条件下产生重要影响。开发的方法也可以应用于许多其他精神和神经疾病。该项目还将开发和公开传播新的计算模型和优化方法，以加速复杂CDNM的仿真。该项目由三个目标组成：1）利用动态功能连接来进一步约束和评估CDNM：第一个目标是将CDNM的参数化与其准确性的评估明确分开。有可能几个模型，或同一模型的参数化，导致现实的平均功能连接。然而，并非所有这些模型都能够重现在实践中观察到的更复杂、动态的功能连接模式。该项目依赖于最先进的方法来推断大脑区域之间的动态功能连接，将这些方法应用于经验数据和基于CDNM的模拟结果。每个候选的CDNM模型将在如何以及它可以重现在经验数据中观察到的动态FC模式方面进行评估。2)使用CDNM来理解重性抑郁症的结构和功能连接之间的联系：任何模型的最终测试都是其预测能力。该项目将利用结构和功能连接数据的患者组，表现出已知的和显着的差异，从健康对照。从Aim-1的最佳模型开始，CDNM将在一个扰动的连接体上运行，该连接体捕获抑郁症的主要结构异常。然后，将分析CDNM结果，以确定模型是否可以重现患者组中观察到的FC异常。3)模拟干预措施的影响，如脑深部电刺激：使用这种实验性治疗抑郁症是一个？网络干预？CDNM可以在了解它如何以及何时作为有效治疗发挥作用方面发挥重要作用。脑深部电刺激的效果将通过修改某些区域的局部动态或模型中特定连接的权重来建模，例如增加或减少连接的权重。该项目将研究是否有一个特定的重量调整，刺激模型产生类似于健康受试者的正常FC的动态。如果这种调整需要在一个非常窄的范围内，这可能可以解释为什么脑深部电刺激在某些患者中不成功。该奖项反映了NSF的法定使命，并且通过使用基金会的智力价值和更广泛的评估被认为值得支持影响审查标准。