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

CRCNS研究建议：协作研究：在大脑中建模和操纵动态网络活动基于Connectome的动态网络建模(CDNM)是计算神经科学中的一种最新方法，通过结构和功能大脑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大尺度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从基础神经科学到临床精神病学和神经学的脑科学各个方面的主要研究目标。这个项目也可以对理解严重抑郁症是如何从特定的结构异常中出现的，以及在什么情况下深部脑刺激是有效的治疗产生重要影响。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，以加快复杂CDNMS的模拟。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会导致实际的平均功能连接性。然而，并非所有这些模型都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模型将根据其能否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)使用CDNM来理解严重抑郁障碍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。该项目将利用患者组的结构和功能连接数据，该患者组显示出与健康对照组已知的和显著的差异。从AIM-1的最佳模型开始，CDNM将在一个受扰的连接体上运行，该连接体捕捉到抑郁症的主要结构异常。然后，将对CDNM结果进行分析，以确定该模型是否可以再现在患者组中观察到的FC异常。3)模拟脑深部刺激等干预措施的效果：这种实验性治疗在抑郁症中的使用是一种网络干预。CDNM在理解它如何以及何时作为一种有效的治疗方法方面可以发挥重要作用。脑深部刺激的效果将通过修改模型中某些区域的局部动力学或特定连接的权重来建模，例如增加或减少连接的权重。该项目将调查是否存在特定的权重调整，通过该调整，受刺激的模型产生类似于健康受试者正常FC的动力学。如果需要在非常窄的范围内进行调整，这可能解释了为什么深部脑刺激在一些患者中不成功。这一裁决反映了NSF的法定使命，并通过使用基金会的智力优势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.