Aram, P., Freestone, D.R., Cook, M.J. et al. (2 more authors) (2015) Model-based estimation of intra-cortical connectivity using electrophysiological data. NeuroImage, 118. 563 - 575. ISSN 1053-8119
Abstract
This paper provides a new method for model-based estimation of intra-cortical connectivity from electrophysiological measurements. A novel closed-form solution for the connectivity function of the Amari neural field equations is derived as a function of electrophysiological observations. The resultant intra-cortical connectivity estimate is driven from experimental data, but constrained by the mesoscopic neurodynamics that are encoded in the computational model. A demonstration is provided to show how the method can be used to image physiological mechanisms that govern cortical dynamics, which are normally hidden in clinical data from epilepsy patients. Accurate estimation performance is demonstrated using synthetic data. Following the computational testing, results from patient data are obtained that indicate a dominant increase in surround inhibition prior to seizure onset that subsides in the cases when the seizures spread.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2015 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) |
Keywords: | Neural field model; Intra-cortical connectivity; Intracranial electroencephalogram (iEEG); Dynamic spatiotemporal modeling; Integro-differential equation (IDE) |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 04 Nov 2015 17:19 |
Last Modified: | 04 Nov 2015 17:19 |
Published Version: | https://dx.doi.org/10.1016/j.neuroimage.2015.06.04... |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.neuroimage.2015.06.048 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:89842 |