The 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.
