Hosted by the Department of Electrical and Systems Engineering (ESE)

Abstract: A fundamental challenge in neural engineering and human brain imaging has been the translation of multimodal data into formal mathematical and computational models that can reveal biophysical mechanisms in neural circuits and their connection to behavior. In this presentation, I will describe our recent efforts to develop methods for the construction of computational models of whole-brain network dynamics from indirect measurements, such as those related to blood oxygenation (i.e., fMRI) and scalp potentials (e.g., EEG). Specifically, I will describe how we have adapted tools from Bayesian filtering and algorithmic optimization toward the problem of parametrically learning high-dimensional, biophysically interpretable models of network interactions involving hundreds to thousands of neural populations. These techniques place a particular emphasis on model-building at the level of individuals, which in turn provides leverage on revealing idiosyncrasies in brain mechanisms. In this regard, I will highlight two ways in which we are leveraging the obtained models. First, I will discuss our newly developed methods to interrogate the intrinsic dynamics within models, toward assessing topological similarity across individual (brain) dynamics and the functional salience thereof. Second, I will describe how we are using models to predict input-output relationships within brain networks and their responses to exogenous, causal perturbations. In addition to basic mechanistic insights, these approaches enable us to design brain stimulation protocols that are tailored to individuals and defined in terms of dynamical targets that can be linked to specific functional endpoints.

Bio: ShiNung Ching is an associate professor in the Department of Electrical and Systems Engineering at Washington University in St. Louis. His research interests lie at the intersection of engineering and computational neuroscience, particularly in using systems and control theory concepts to study the dynamics and function of neuronal networks. His research includes efforts to provide new scientific characterizations of brain function from data and models, as well as clinical work aimed at improving neural technology, including neurostimulation for cognitive enhancement.

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For inquiries contact Aaron Beagle.