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

基于脑连接体的动态网络建模（CDNM）是计算神经科学领域的一种最新方法，由于大脑结构和功能连接数据的可用性而成为可能。该项目旨在了解神经种群的结构和动态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结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病学。这个项目也可以对理解重度抑郁症是如何从特定的结构异常中产生的，以及在什么情况下深部脑刺激是一种有效的治疗方法有重要的影响。所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，以及加快复杂CDNMs模拟的优化方法。该项目包括三个目标：1)利用动态功能连通性来进一步约束和评估CDNM：第一个目标是明确地将CDNM的参数化与其准确性评估分开。几个模型或同一模型的参数化可能导致实际的平均功能连通性。然而，并非所有这些模型都能够重现实践中观察到的更复杂的动态功能连接模式。该项目依靠最先进的方法来推断大脑区域之间的动态功能连接，并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CDNM模型将根据其如何很好地再现在经验数据中观察到的动态FC模式进行评估。2)利用CDNM来理解重度抑郁症中结构和功能连接之间的联系：对任何模型的最终检验是其预测能力。该项目将利用结构和功能连通性数据，研究与健康对照组存在已知和显著差异的患者组。从Aim-1的最佳模型开始，CDNM将在一个被干扰的连接体上运行，该连接体捕获了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)模拟脑深部刺激等干预措施的效果：在抑郁症中使用这种实验性治疗是一种？网络干预?。CDNM可以在理解它如何以及何时作为一种有效的治疗方法起作用方面发挥重要作用。脑深部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。该项目将调查是否有一个特定的体重调整，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，这也许可以解释为什么深部脑刺激对一些患者不成功。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.