dc.contributor.author |
Dadok, VM |
en |
dc.contributor.author |
Kirsch, HE |
en |
dc.contributor.author |
Sleigh, James |
en |
dc.contributor.author |
Lopour, BA |
en |
dc.contributor.author |
Szeri, AJ |
en |
dc.date.accessioned |
2017-03-08T22:49:22Z |
en |
dc.date.available |
2015-03-12 |
en |
dc.date.issued |
2015-06 |
en |
dc.identifier.citation |
Journal of Computational Neuroscience, June 2015, 38 (3), 559 - 575 |
en |
dc.identifier.issn |
0929-5313 |
en |
dc.identifier.uri |
http://hdl.handle.net/2292/32096 |
en |
dc.description.abstract |
This work presents a probabilistic method for inferring the parameter ranges in a biologically relevant mathematical model of the cortex most likely to be producing seizures observed in an electrocorticogram (ECoG) signal from a human subject. Additionally, this method produces a probabilistic pathway of the temporal evolution of physiological state in the cortex over the course of individual seizures, leveraging a model of the cortex that describes cortical physiology. We describe ways in which these methods and results offer insights into seizure etiology and have the potential to suggest new treatment options. To directly account for the stochastic and noisy nature of the mathematical model and the ECoG signal, we use a probabilistic Bayesian framework to map features of ECoG segments onto a distribution of likelihoods over physiologically-relevant parameter states. A Hidden Markov Model (HMM) is then introduced to incorporate the belief that cortical physiology has both temporal continuity and also a degree of reproducibility between individual seizures. By inspecting the ratio of likelihoods between HMMs run under two possible parameter regions, both of which produce seizures in the model, we determine which physiological parameter regions are more likely to be causing the observed seizures. We show that between individual seizures, there is consistency in these likelihood ratios between hypothesized regions, in the temporal pathways calculated, and in the separation of seizure from non-seizure time segment likelihood maps. |
en |
dc.description.uri |
https://www.ncbi.nlm.nih.gov/pubmed/25851500 |
en |
dc.format.medium |
Print-Electronic |
en |
dc.language |
English |
en |
dc.publisher |
Springer |
en |
dc.relation.ispartofseries |
Journal of Computational Neuroscience |
en |
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. Details obtained from http://www.sherpa.ac.uk/romeo/issn/0929-5313/
http://www.springer.com/gp/open-access/authors-rights/self-archiving-policy/2124 |
en |
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
en |
dc.subject |
Cerebral Cortex |
en |
dc.subject |
Humans |
en |
dc.subject |
Seizures |
en |
dc.subject |
Electroencephalography |
en |
dc.subject |
Models, Statistical |
en |
dc.subject |
Likelihood Functions |
en |
dc.subject |
Bayes Theorem |
en |
dc.subject |
Markov Chains |
en |
dc.subject |
Stochastic Processes |
en |
dc.subject |
Algorithms |
en |
dc.subject |
Models, Theoretical |
en |
dc.subject |
Models, Neurological |
en |
dc.title |
A probabilistic method for determining cortical dynamics during seizures |
en |
dc.type |
Journal Article |
en |
dc.identifier.doi |
10.1007/s10827-015-0554-8 |
en |
pubs.issue |
3 |
en |
pubs.begin-page |
559 |
en |
pubs.volume |
38 |
en |
dc.description.version |
VoR - Version of Record |
en |
dc.identifier.pmid |
25851500 |
en |
pubs.author-url |
https://link.springer.com/article/10.1007/s10827-015-0554-8 |
en |
pubs.end-page |
575 |
en |
pubs.publication-status |
Published |
en |
dc.rights.accessrights |
http://purl.org/eprint/accessRights/RestrictedAccess |
en |
pubs.subtype |
Article |
en |
pubs.elements-id |
486013 |
en |
pubs.org-id |
Medical and Health Sciences |
en |
pubs.org-id |
School of Medicine |
en |
pubs.org-id |
Anaesthesiology |
en |
dc.identifier.eissn |
1573-6873 |
en |
pubs.record-created-at-source-date |
2017-03-09 |
en |
pubs.online-publication-date |
2015-04-08 |
en |
pubs.dimensions-id |
25851500 |
en |