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Freestone, D.R., Karoly, P.J., Nesic, D. et al. (3 more authors) (2014) Estimation of effective connectivity via data-driven neural modeling. Frontiers in Neuroscience, 8. 383. ISSN 1662-4548
Abstract
This research introduces a new method for functional brain imaging via a process of model inversion. By estimating parameters of a computational model, we are able to track effective connectivity and mean membrane potential dynamics that cannot be directly measured using electrophysiological measurements alone. The ability to track the hidden aspects of neurophysiology will have a profound impact on the way we understand and treat epilepsy. For example, under the assumption the model captures the key features of the cortical circuits of interest, 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. The method is based on approximating brain networks using an interconnected neural population model. The neural population model is based on a neural mass model that describes the functional activity of the brain, capturing the mesoscopic biophysics and anatomical structure. The model is made subject-specific by estimating the strength of intra-cortical connections within a region and inter-cortical connections between regions using a novel Kalman filtering method. We demonstrate through simulation how the framework can be used to track the mechanisms involved in seizure initiation and termination.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2014 Freestone, Karoly, Nešić Aram, Cook and Grayden. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
Keywords: | functional connectivity, neural mass model, model inversion, Kalman filter, epilepsy, seizures, parameter estimation, effective connectivity |
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) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 18 Feb 2015 11:09 |
Last Modified: | 23 Jun 2023 21:44 |
Published Version: | http://dx.doi.org/10.3389/fnins.2014.00383 |
Status: | Published |
Publisher: | Frontiers |
Refereed: | Yes |
Identification Number: | 10.3389/fnins.2014.00383 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:83546 |
Available Versions of this Item
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Estimation of effective connectivity via data-driven neural modeling. (deposited 22 Dec 2014 11:11)
- Estimation of effective connectivity via data-driven neural modeling. (deposited 18 Feb 2015 11:09) [Currently Displayed]