Large-scale Network Modeling for Brain Dynamics: Statistical Learning and Optimization

脑动力学大规模网络建模：统计学习和优化

• 批准号: 9170649
• 负责人: Xi Luo
• 金额: $ 43.01万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-30 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9170649
• 关键词: Accounting; Address; Affective; Algorithms; American; Behavior; Behavioral; Brain; Brain region; Cognitive; Collaborations; Communities; Complex; Data; Data Analyses; Data Science; Data Set; Dependence; Dimensions; Ensure; Equation; Event; Experimental Designs; Functional Magnetic Resonance Imaging; Future; Goals; Graph; Human; Investigation; Journals; Lead; Machine Learning; Mental disorders; Methods; Modeling; Motor; Network-based; Neurobiology; Neurosciences; Neurosciences Research; Non-linear Models; Nonlinear Dynamics; Pathway interactions; Process; Publishing; Scientist; Seeds; Space Models; Stimulus; System; Techniques; Testing; Therapeutic; Time; Validation; Variant; base; behavioral outcome; brain pathway; cognitive control; cohort; cost; data modeling; design; flexibility; improved; information model; information processing; insight; large scale simulation; model development; nervous system disorder; network models; neural circuit; neuroimaging; neuromechanism; novel; open source; relating to nervous system; research study; simulation; statistics; stimulus processing; tool

Summary The human brain is a large, well-connected, and dynamic network. Using functional MRI data, modeling how this network processes the stimulus information has yielded insight on some of the mechanisms of the brain. However, the past efforts, including ours, on using small-scale models yielded limited understanding of how the complete and dynamic neural system functions in task-related experiments. Such understanding cannot be recovered from the data without substantial and collaborative efforts on model development. Towards this goal, we formed a collaborative team from modelers to end-users, and we will develop large-scale methods for task related fMRI (tfMRI), including event-related fMRI, to model whole-brain network dynamics responding to task challenges. Using modern statistical learning principals and large-scale optimization algorithms, we will develop novel methods to model nonlinear, spatial-temporal dependence in high dimensional data of fMRI, stimuli, and behavior outcomes. We will primarily base our methods in the regularized, constrained graphical model (GM) framework, a promising multivariate framework for inferring brain connectivity that has been validated by simulation and anatomical studies. Using this framework, we will develop novel methods to investigate, at a large scale, how changes in connectivity and activation are driven by task challenges and how multiple brain pathways process stimulus information. We will perform comprehensive validation and assessment of the newly developed methods, using both simulated and multiple tfMRI data from large cohorts. Using the scale of modeling that previous approaches cannot readily address without substantial time penalties and maybe also inaccuracies, our collaborative team will also use these methods to investigate various novel questions and hypotheses concerning the neural basis for cognitive control as one of the use cases. We will also develop publicly available, open source implementations for a broad range of use in the neuroimaging community. These modeling efforts will lead to new insights on the networks of large-scale neural circuits, and provide pharmacological targets that may be overlooked using small-scale models.

摘要 人类的大脑是一个庞大的、连接良好的、动态的网络。使用功能磁共振数据，模拟如何 这个处理刺激信息的网络对大脑的一些机制有了深入的了解。 然而，过去的努力，包括我们在内，在使用小规模模型方面产生了有限的理解 任务相关实验中完整和动态的神经系统功能。这样的理解不能 从数据中恢复，而不需要在模型开发方面进行实质性和协作性努力。为了实现这一目标， 我们组成了一个从建模师到最终用户的协作团队，我们将开发大规模的任务方法 相关功能磁共振成像(TfMRI)，包括事件相关功能磁共振成像，用于模拟响应任务的全脑网络动力学 挑战。利用现代统计学习原理和大规模优化算法，我们将 开发新的方法对高维fMRI数据中的非线性、时空相关性进行建模， 刺激和行为结果。我们将主要将我们的方法建立在正则化的、受约束的图形基础上 模型(GM)框架，一个很有前途的多变量框架，用于推断大脑连接，已经 通过模拟和解剖学研究得到了验证。使用这个框架，我们将开发新的方法来 大规模调查任务挑战如何推动连接和激活方面的变化，以及如何 多条大脑通路处理刺激信息。我们将进行全面的验证和 对新开发的方法进行评估，使用来自大型队列的模拟和多个tfMRI数据。 使用以前的方法在没有大量时间惩罚的情况下不容易解决的建模规模 也许还有不准确的地方，我们的合作团队也会使用这些方法来调查各种小说 作为使用案例之一，关于认知控制的神经基础的问题和假设。我们会 还要为神经成像中的广泛使用开发公开可用的开放源码实现 社区。这些建模工作将导致对大规模神经电路网络的新见解，以及 提供使用小规模模型可能忽略的药理靶点。