Friday, October 10, 2014

Dean Freestone: October 15th

Data-Driven Mean Field Neural Modeling

Abstract: This research provides an overview of new methods for functional brain mapping via a process of model inversion. By estimating parameters of a computational model, we demonstrate a method for tracking functional connectivity and other parameters that influence neural dynamics. The estimation results provide an imaging modality of neural processes that cannot be directly measured using electrophysiological measurements alone.
The method is based on approximating brain networks using an interconnected neural mass model. Neural mass models describe the functional activity of the brain from a top-down perspective, capturing particular important experimental phenomena. The models can be related to biology by lumped quantities, where for example, the resting-membrane potentials, reversal potentials and firing thresholds are all lumped into one parameter. The lumping of parameters is a result of a trade-off between biological realism, where insights into brain mechanisms can still be gained, and parsimony, where models can be inverted and fit to patient-specific data.
The ability to track the hidden aspects of neurophysiology will have a profound impact on the way we understand and treat epilepsy. For example, the framework will provide insights into seizure initiation and termination on a patient-specific basis. It will enable investigation into the effect a particular drug has on specific neural populations and connectivity structures using minimally invasive measurements.

Bio: Dr Freestone is currently a Senior Research Fellow in the Department of Medicine for St. Vincent’s Hospital at the University of Melbourne, Australia, and Fulbright Post-Doctoral Scholar at Columbia University, USA. He has previously completed a Post-Doc position at the University of Melbourne in the NeuroEngineering Research Group. He completed his PhD at the University of Melbourne, Australia and the University of Edinburgh, UK. His work has focused on developing methods for epileptic seizure prediction and control.

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