Probing Neural Connectivity at Multiple Temporal Scales

1605646-纳贾菲扎德大脑是一个高度复杂的动态系统，其中神经功能连接在多个时间尺度上不断变化。这些变化可以发生在很短的范围内，例如由于学习一项简单的任务，也可以发生在相对较长的范围内，因为各种原因，如学习复杂的概念、与大脑相关的疾病(例如阿尔茨海默氏症和抑郁症)以及进行康复。目前，我们对神经功能相互作用如何形成和随时间变化的了解非常有限，因为缺乏1)能够可靠地表征这些变化在不同时间尺度上的定量测量，以及2)连续监测和记录不同时间尺度上的大脑活动的能力，从毫秒到数天和数周。该项目旨在通过采取理论和实验相结合的方法来建立一个数据驱动的计算框架，并采用新的量化措施来表征大脑功能网络动力学的时间演变，跨越短时间和长时间尺度，从而解决这些限制。为了进一步扩大该框架的功能，该项目将进一步考虑大脑网络结构被操纵的场景，并将通过计算和实验研究大脑网络如何重组和随时间变化，以响应对网络结构的瞬时、局部操纵。该项目的结果将在神经科学领域产生革命性的影响，因为它引入了一个强大的计算框架，用于量化在各种条件下经过实验评估的大脑网络的动力学。该项目还将为研究生和本科生提供一个独特的机会，在信号处理、统计学、神经生物学和成像的交叉领域获得多学科的专业知识，从而为培养下一代工程师和神经科学家提供一个理想的平台。神经科学和脑成像领域的基本问题之一是缺乏一个通用的计算框架和可靠的量化措施来表征在多个时间尺度上大脑网络之间的功能相互作用发生的变化。这个为期三年的项目的重点是建立一个全面的数据驱动的量化框架，这将使研究和定量表征脑功能网络在多个时间尺度上的动态特性成为可能。以体感学习为重点，本研究将使用提出的框架以及GCaMP6f报告小鼠的慢性成像，定量检测1)任务绩效如何改变功能脑网络之间的互动(短期变化)，2)当小鼠最终成为执行任务的专家时，这些互动有何不同(长期变化)，以及3)操纵网络中的不同节点将如何改变脑功能互动的动力学。该项目通过理论和实验相结合的研究，将在三个方面引入创新方法。首先，将建立一个系统的数据驱动的方法，用于定量分析不同时间尺度上的大脑功能网络动力学的时间演化。其目的是用一个多层网络来表示大脑网络的功能，其中层用时间来标记。其次，该项目将在GCaMP6f报告老鼠身上使用最先进的慢性成像，监测几个时间尺度上的网络活动，从快毫秒到发生在几天到几周的较慢时间尺度上的网络活动。定量分析与活体成像的结合为理解大脑皮层网络中与行为相关的动态变化提供了一种强有力的方法。第三，为了在定量和实验上加强框架的能力(在成像过程中使用模拟和失活方法)，该项目调查了功能网络如何通过对网络结构的瞬时、局部操作而改变。这一结果对于促进对中风或损伤后大脑网络功能重组动态的理解将特别重要。该项目的成功，将时间维度纳入分析，将对神经科学领域产生变革性的影响。新的综合框架将使研究短期和长期网络变化的量化评估成为可能，以促进我们对大脑功能重组动力学的理解。在这个项目中，通过最先进的成像系统和框架将产生独特的丰富的数据集，将提供关于大脑网络活动如何在与学习相关的快、慢时间尺度上变化的重要信息。本研究成果将对临床领域产生重大影响，有助于下一代个性化康复技术的发展。此外，拟议的研究工作是多学科的，因为它涵盖了从信号处理、统计学和工程学到神经生物学、成像学和神经科学的主题，从而为参与该项目的研究生和本科生提供了一个获得新学习经验的独特机会。

1605646 - NajafizadehThe brain is a highly complex dynamic system in which neural functional connections are continuously changing at multiple time scales. These changes can occur at very short scales, for example due to learning a simple task, or at relatively longer scales, due to wide range of reasons, such as learning complex concepts, brain-related diseases (e.g. Alzheimer's and depression), and going through rehabilitation. Currently, our understanding of how the neural functional interactions form and change with time has been very limited because of lack of 1) quantitative measures that can reliably characterize these changes at different time scales, and 2) the ability to continuously monitor and record brain activities at different time scales, from millisecond to days and weeks. This project aims to address these limitations by taking a combined theoretical-experimental approach to establish a data-driven computational framework with new quantitative measures that can characterize the temporal evolution of dynamics of brain functional networks, across short-and long time scales. To further broaden the capabilities of the framework, this project will take a step further to consider scenarios where the brain network structure has been manipulated, and will computationally and experimentally investigate how brain networks reorganize and change with time in response to transient, localized manipulations of the network structure. The outcome of this project will have a transformative impact in the field of neuroscience by introducing a powerful computational framework for quantifying the dynamics of brain networks that have been evaluated experimentally under various conditions. The project will also provide a unique opportunity for the graduate and undergraduate students to obtain multidisciplinary expertise at the intersection of signal processing, statistics, neurobiology and imaging, thus providing an ideal platform for the training of the next generation engineers and neuroscientists.One of the fundamental problems in the field of neuroscience and brain mapping is the lack of a generalized computational framework with reliable quantitative measures for characterizing the changes that occur in the functional interactions among brain networks at multiple temporal scales. The focus of this three-year project is to establish a comprehensive data-driven quantitative framework, which will enable studying and quantitatively characterizing the dynamic properties of brain functional networks at multiple temporal scales. With a focus on somatosensory learning, this research will use the proposed framework along with chronic imaging in GCaMP6f reporter mice, to quantitatively examine 1) how the interactions among functional brain networks are modified by task performance (short term changes), 2) how such interactions differ over days when mice finally becomes expert in performing the task (long term changes), and 3) how manipulating different nodes in the network, will change dynamics of brain functional interactions. This project, via combined theoretical-experimental studies, will introduce innovative approaches at three frontends. First, a systematic data-driven approach for quantitatively analyzing the temporal evolution of dynamics of brain functional networks at different temporal scales will be established. The aim is to represent the functionality of the brain networks with a multi-layer network, in which layers are labeled by time. Second, using state-of-the-art chronic imaging in GCaMP6f reporter mice, the project will monitor network activities at several temporal scales, from fast millisecond to those that occur on a slower time scale of days to weeks. The joining of quantitative analysis with in vivo imaging brings a powerful approach to the understanding of behavior-related dynamic changes in the cortical network. Third, to strengthen the capabilities of the framework, quantitatively and experimentally (using simulations as well as inactivation methods during imaging), the project investigates how the functional network is altered by transient, localized manipulations of network structure. The outcomes will be particularly important for advancing understanding of the dynamics of functional reorganization of the brain networks after stroke or injuries. The success of this project, by including the temporal dimension into the analysis, will have a transformative impact in the field of neuroscience. The new comprehensive framework will enable quantitative assessment of studying short-term and long-term network changes to advance our understanding of the dynamics of functional reorganization of the brain. The unique rich data set that will be generated through the state-of-art imaging system in this project along with the framework will provide important information on how cortical network activity changes across fast and slow time scales as related to learning. The outcomes of this research will have a significant impact in the clinical domain, helping the development of the next generation of personalized rehabilitation technology. In addition, the proposed research work is multidisciplinary as it covers topics from signal processing, statistics and engineering to neurobiology, imaging and neuroscience, thus providing a unique opportunity for the graduate and undergraduate students involved in the project to gain new learning experiences.