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

Georgia Tech Research Corporation, Atlanta GA

Investigators

Abstract

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