Probing Neural Connectivity at Multiple Temporal Scales

nsf_101x102.jpg

Professor Laleh Najafizadeh is PI on a new NSF grant from the Biomedical Engineering (BME) Program of the Division of Chemical, Bioengineering, Environmental and Transport Systems (CBET).

The title of the project is "Probing Neural Connectivity at Multiple Temporal Scales" and the award amount is $363,273. This project is in collaboration with David Margolis (Co-PI), Assistant Professor at the Department of Cell Biology and Neuroscience (CBN) from the School of Arts and Sciences at Rutgers.

This project will develop a new comprehensive framework that 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 brain is a highly complex dynamic system in which neural functional connections are continuously changing at multiple time scales. These changes can occur at 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, 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 comprehensive data-driven framework that will enable quantitative characterization of 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 the dynamics of brain functional interactions. The success of this project, by including the temporal dimension into the analysis, will have a transformative impact in the field of neuroscience. This 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.