Abstract: In this talk, a mesoscopic model of a cortical column, known as a Duffing Neural Mass Model (DNMM), is developed to emulate stochastic mechanisms of initiation and termination of seizures in intracranial electroencephalogram (iEEG) recordings. The DNMM is constructed by applying perturbations to linear models of synaptic transmission in the Jansen and Rit [1] neural mass model. Random input (noise) can cause switches between normal activity and pathological activity similar to seizures in the DNMM. A bifurcation analysis and simulations are presented to provide insights into the behaviour of the model and to motivate questions for discussion. To replicate the pathological dynamics of ion currents, the model is extended to a slow-fast DNMM by considering a slow dynamics model (relative to the membrane potentials and firing rates) for some internal model parameters. The slow-fast DNMM can replicate initiation and termination of seizures that are caused by both random input fluctuations and pathological dynamics. Model comparison and the most likely to capture the underlying dynamics of recorded iEEG is sought through measuring a likelihood function estimated using a continuous-discrete unscented Kalman filter.
Reference:
[1]. B. Jansen and V. Rit. Electroencephalogram and visual evoked potential generation in
a mathematical model of coupled cortical columns. Biological Cybernetics, 73:357-366,
1995. ISSN 0340-1200.
Biography: Amir is currently with Wellcome Centre for Human neuroimaging, University College London as a research associate. He obtained a Ph.D. from the University of Melbourne where he developed mathematical models as well as data assimilation methods to explore biological mechanisms underlying initiation and termination of epileptic seizures. Amir Ph.D. research was funded by the Australian Research Council (Linkage Project LP100200571) and the University of Melbourne.
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