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Slow-Fast Duffing Neural Mass Model

Jafarian, A; Freestone, DR; Nesic, D; Grayden, DB; (2019) Slow-Fast Duffing Neural Mass Model. In: Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). (pp. pp. 142-145). IEEE: Berlin, Germany. Green open access

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Abstract

Epileptic seizures may be initiated by random neuronal fluctuations and/or by pathological slow regulatory dynamics of ion currents. This paper presents extensions to the Jansen and Rit neural mass model (JRNMM) to replicate paroxysmal transitions in intracranial electroencephalogram (iEEG) recordings. First, the Duffing NMM (DNMM) is introduced to emulate stochastic generators of seizures. The DNMM is constructed by applying perturbations to linear models of synaptic transmission in each neural population of the JRNMM. Then, the slow-fast DNMM is introduced by considering slow dynamics (relative to membrane potential and firing rate) of some internal parameters of the DNMM to replicate pathological evolution of ion currents. Through simulation, it is illustrated that the slow-fast DNMM exhibits transitions to and from seizures with etiologies that are linked either to random input fluctuations or pathological evolution of slow states. Estimation and optimization of a log likelihood function (LLF) using a continuous-discrete unscented Kalman filter (CD-UKF) and a genetic algorithm (GA) are performed to capture dynamics of iEEG data with paroxysmal transitions.

Type: Proceedings paper
Title: Slow-Fast Duffing Neural Mass Model
Event: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Dates: 23 July 2019 - 27 July 2019
ISBN-13: 978-1-5386-1311-5
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/embc.2019.8857316
Publisher version: https://doi.org/10.1109/embc.2019.8857316
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Mathematical model, Sociology, Statistics, Brain modeling, Pathology, Genetic algorithms, Data models
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
URI: https://discovery.ucl.ac.uk/id/eprint/10083150
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