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

• 批准号: 1822553
• 负责人: Constantinos Dovrolis
• 金额: $ 24.5万
• 依托单位: Georgia Tech Research Corporation
• 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=1822553&HistoricalAwards=false
• 关键词: CRCNS; Research; Proposal; Collaborative; Modeling

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研究建议：协作研究：基于脑部连接组的动态网络建模（CDNM）中的建模和操纵动态网络活动是计算神经科学的最新方法，这是通过结构和功能性大脑连接性数据的可用性而成为可能的。该项目旨在了解神经种群的结构与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。理论上理解并能够预测宏观结构和局部神经活动的结合如何导致复杂的全脑动力学，这是脑科学各个方面的主要研究目标，从基本的神经科学到临床精神病学和神经病学。该项目还可以在理解主要抑郁症如何从特定的结构异常中出现，以及深脑刺激是一种有效治疗的条件。开发的方法也可以应用于许多其他心理和神经系统疾病。该项目还将开发并公开传播新的计算模型，以及用于加快复杂CDNM的模拟的优化方法。该项目由三个目标组成：1）利用动态功能连接性，以进一步限制和评估CDNM：第一个目标是将CDNM的参数化与评估其准确性的评估。几个模型或同一模型的参数化可能会导致现实的平均功能连接性。但是，并非所有这些模型都可能能够再现在实践中观察到的更复杂，动态的功能连接模式。该项目依赖于最新的方法，这些方法推断大脑区域之间的动态功能连通性，将这些方法应用于经验数据和基于CDNM的仿真结果。每个候选CDNM模型将根据其在经验数据中观察到的动态FC模式的能力进行评估。 2）使用CDNM了解重度抑郁症中的结构和功能连通性之间的联系：任何模型的最终测试是其预测能力。该项目将为患者组利用结构和功能连接数据，这些数据表现出已知和与健康对照的显着差异。从AIM-1的最佳模型开始，该CDNM将以扰动的连接组进行运行，该连接符捕获抑郁症的主要结构异常。然后，将分析CDNM结果，以确定该模型是否可以重现患者组中观察到的FC异常。 3）对诸如深脑刺激之类的干预措施的影响进行建模：在抑郁症中使用这种实验治疗方法是？网络干预吗？ CDNM可以在理解如何以及何时作为有效治疗的方式中发挥重要作用。深脑刺激的效果将通过修改某些区域的局部动力学或模型中特定连接的权重，例如增加或减少连接的重量。该项目将调查是否有特定的重量调整，而刺激模型会产生类似于健康受试者FC的动力学。如果这种调整需要在非常狭窄的范围内，它可以解释为什么深度大脑刺激在某些患者中是不成功的。该奖项反映了NSF的法定任务，并且使用基金会的智力优点和更广泛的影响评估标准，认为值得通过评估来获得支持。