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Using network dynamic fMRI for detection of epileptogenic foci

BACKGROUND: Epilepsy is one of the most prevalent neurological disorders. It remains medically intractable for about one-third of patients with focal epilepsy, for whom precise localization of the epileptogenic zone responsible for seizure initiation may be critical for successful surgery. Existing...

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Autores principales: Nedic, Sanja, Stufflebeam, Steven M., Rondinoni, Carlo, Velasco, Tonicarlo R., dos Santos, Antonio C., Leite, Joao P., Gargaro, Ana C., Mujica-Parodi, Lilianne R., Ide, Jaime S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4687299/
https://www.ncbi.nlm.nih.gov/pubmed/26689596
http://dx.doi.org/10.1186/s12883-015-0514-y
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author Nedic, Sanja
Stufflebeam, Steven M.
Rondinoni, Carlo
Velasco, Tonicarlo R.
dos Santos, Antonio C.
Leite, Joao P.
Gargaro, Ana C.
Mujica-Parodi, Lilianne R.
Ide, Jaime S.
author_facet Nedic, Sanja
Stufflebeam, Steven M.
Rondinoni, Carlo
Velasco, Tonicarlo R.
dos Santos, Antonio C.
Leite, Joao P.
Gargaro, Ana C.
Mujica-Parodi, Lilianne R.
Ide, Jaime S.
author_sort Nedic, Sanja
collection PubMed
description BACKGROUND: Epilepsy is one of the most prevalent neurological disorders. It remains medically intractable for about one-third of patients with focal epilepsy, for whom precise localization of the epileptogenic zone responsible for seizure initiation may be critical for successful surgery. Existing fMRI literature points to widespread network disturbances in functional connectivity. Per previous scalp and intracranial EEG studies and consistent with excessive local synchronization during interictal discharges, we hypothesized that, relative to same regions in healthy controls, epileptogenic foci would exhibit less chaotic dynamics, identifiable via entropic analyses of resting state fMRI time series. METHODS: In order to first validate this hypothesis on a cohort of patients with known ground truth, here we test individuals with well-defined epileptogenic foci (left mesial temporal lobe epilepsy). We analyzed voxel-wise resting-state fMRI time-series using the autocorrelation function (ACF), an entropic measure of regulation and feedback, and performed follow-up seed-to-voxel functional connectivity analysis. Disruptions in connectivity of the region exhibiting abnormal dynamics were examined in relation to duration of epilepsy and patients’ cognitive performance using a delayed verbal memory recall task. RESULTS: ACF analysis revealed constrained (less chaotic) functional dynamics in left temporal lobe epilepsy patients, primarily localized to ipsilateral temporal pole, proximal to presumed focal points. Autocorrelation decay rates differentiated, with 100 % accuracy, between patients and healthy controls on a subject-by-subject basis within a leave-one-subject out classification framework. Regions identified via ACF analysis formed a less efficient network in patients, as compared to controls. Constrained dynamics were linked with locally increased and long-range decreased connectivity that, in turn, correlated significantly with impaired memory (local left temporal connectivity) and epilepsy duration (left temporal – posterior cingulate cortex connectivity). CONCLUSIONS: Our current results suggest that data driven functional MRI methods that target network dynamics hold promise in providing clinically valuable tools for identification of epileptic regions.
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spelling pubmed-46872992015-12-23 Using network dynamic fMRI for detection of epileptogenic foci Nedic, Sanja Stufflebeam, Steven M. Rondinoni, Carlo Velasco, Tonicarlo R. dos Santos, Antonio C. Leite, Joao P. Gargaro, Ana C. Mujica-Parodi, Lilianne R. Ide, Jaime S. BMC Neurol Research Article BACKGROUND: Epilepsy is one of the most prevalent neurological disorders. It remains medically intractable for about one-third of patients with focal epilepsy, for whom precise localization of the epileptogenic zone responsible for seizure initiation may be critical for successful surgery. Existing fMRI literature points to widespread network disturbances in functional connectivity. Per previous scalp and intracranial EEG studies and consistent with excessive local synchronization during interictal discharges, we hypothesized that, relative to same regions in healthy controls, epileptogenic foci would exhibit less chaotic dynamics, identifiable via entropic analyses of resting state fMRI time series. METHODS: In order to first validate this hypothesis on a cohort of patients with known ground truth, here we test individuals with well-defined epileptogenic foci (left mesial temporal lobe epilepsy). We analyzed voxel-wise resting-state fMRI time-series using the autocorrelation function (ACF), an entropic measure of regulation and feedback, and performed follow-up seed-to-voxel functional connectivity analysis. Disruptions in connectivity of the region exhibiting abnormal dynamics were examined in relation to duration of epilepsy and patients’ cognitive performance using a delayed verbal memory recall task. RESULTS: ACF analysis revealed constrained (less chaotic) functional dynamics in left temporal lobe epilepsy patients, primarily localized to ipsilateral temporal pole, proximal to presumed focal points. Autocorrelation decay rates differentiated, with 100 % accuracy, between patients and healthy controls on a subject-by-subject basis within a leave-one-subject out classification framework. Regions identified via ACF analysis formed a less efficient network in patients, as compared to controls. Constrained dynamics were linked with locally increased and long-range decreased connectivity that, in turn, correlated significantly with impaired memory (local left temporal connectivity) and epilepsy duration (left temporal – posterior cingulate cortex connectivity). CONCLUSIONS: Our current results suggest that data driven functional MRI methods that target network dynamics hold promise in providing clinically valuable tools for identification of epileptic regions. BioMed Central 2015-12-21 /pmc/articles/PMC4687299/ /pubmed/26689596 http://dx.doi.org/10.1186/s12883-015-0514-y Text en © Nedic et al. 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Nedic, Sanja
Stufflebeam, Steven M.
Rondinoni, Carlo
Velasco, Tonicarlo R.
dos Santos, Antonio C.
Leite, Joao P.
Gargaro, Ana C.
Mujica-Parodi, Lilianne R.
Ide, Jaime S.
Using network dynamic fMRI for detection of epileptogenic foci
title Using network dynamic fMRI for detection of epileptogenic foci
title_full Using network dynamic fMRI for detection of epileptogenic foci
title_fullStr Using network dynamic fMRI for detection of epileptogenic foci
title_full_unstemmed Using network dynamic fMRI for detection of epileptogenic foci
title_short Using network dynamic fMRI for detection of epileptogenic foci
title_sort using network dynamic fmri for detection of epileptogenic foci
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4687299/
https://www.ncbi.nlm.nih.gov/pubmed/26689596
http://dx.doi.org/10.1186/s12883-015-0514-y
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