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Identifying the neural network for neuromodulation in epilepsy through connectomics and graphs
Deep brain stimulation is a treatment option for patients with drug-resistant epilepsy. The precise mechanism of neuromodulation in epilepsy is unknown, and biomarkers are needed for optimizing treatment. The aim of this study was to describe the neural network associated with deep brain stimulation...
Autores principales: | , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9123846/ https://www.ncbi.nlm.nih.gov/pubmed/35611305 http://dx.doi.org/10.1093/braincomms/fcac092 |
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author | Vetkas, Artur Germann, Jürgen Elias, Gavin Loh, Aaron Boutet, Alexandre Yamamoto, Kazuaki Sarica, Can Samuel, Nardin Milano, Vanessa Fomenko, Anton Santyr, Brendan Tasserie, Jordy Gwun, Dave Jung, Hyun Ho Valiante, Taufik Ibrahim, George M Wennberg, Richard Kalia, Suneil K Lozano, Andres M |
author_facet | Vetkas, Artur Germann, Jürgen Elias, Gavin Loh, Aaron Boutet, Alexandre Yamamoto, Kazuaki Sarica, Can Samuel, Nardin Milano, Vanessa Fomenko, Anton Santyr, Brendan Tasserie, Jordy Gwun, Dave Jung, Hyun Ho Valiante, Taufik Ibrahim, George M Wennberg, Richard Kalia, Suneil K Lozano, Andres M |
author_sort | Vetkas, Artur |
collection | PubMed |
description | Deep brain stimulation is a treatment option for patients with drug-resistant epilepsy. The precise mechanism of neuromodulation in epilepsy is unknown, and biomarkers are needed for optimizing treatment. The aim of this study was to describe the neural network associated with deep brain stimulation targets for epilepsy and to explore its potential application as a novel biomarker for neuromodulation. Using seed-to-voxel functional connectivity maps, weighted by seizure outcomes, brain areas associated with stimulation were identified in normative resting state functional scans of 1000 individuals. To pinpoint specific regions in the normative epilepsy deep brain stimulation network, we examined overlapping areas of functional connectivity between the anterior thalamic nucleus, centromedian thalamic nucleus, hippocampus and less studied epilepsy deep brain stimulation targets. Graph network analysis was used to describe the relationship between regions in the identified network. Furthermore, we examined the associations of the epilepsy deep brain stimulation network with disease pathophysiology, canonical resting state networks and findings from a systematic review of resting state functional MRI studies in epilepsy deep brain stimulation patients. Cortical nodes identified in the normative epilepsy deep brain stimulation network were in the anterior and posterior cingulate, medial frontal and sensorimotor cortices, frontal operculum and bilateral insulae. Subcortical nodes of the network were in the basal ganglia, mesencephalon, basal forebrain and cerebellum. Anterior thalamic nucleus was identified as a central hub in the network with the highest betweenness and closeness values, while centromedian thalamic nucleus and hippocampus showed average centrality values. The caudate nucleus and mammillothalamic tract also displayed high centrality values. The anterior cingulate cortex was identified as an important cortical hub associated with the effect of deep brain stimulation in epilepsy. The neural network of deep brain stimulation targets shared hubs with known epileptic networks and brain regions involved in seizure propagation and generalization. Two cortical clusters identified in the epilepsy deep brain stimulation network included regions corresponding to resting state networks, mainly the default mode and salience networks. Our results were concordant with findings from a systematic review of resting state functional MRI studies in patients with deep brain stimulation for epilepsy. Our findings suggest that the various epilepsy deep brain stimulation targets share a common cortico-subcortical network, which might in part underpin the antiseizure effects of stimulation. Interindividual differences in this network functional connectivity could potentially be used as biomarkers in selection of patients, stimulation parameters and neuromodulation targets. |
format | Online Article Text |
id | pubmed-9123846 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-91238462022-05-23 Identifying the neural network for neuromodulation in epilepsy through connectomics and graphs Vetkas, Artur Germann, Jürgen Elias, Gavin Loh, Aaron Boutet, Alexandre Yamamoto, Kazuaki Sarica, Can Samuel, Nardin Milano, Vanessa Fomenko, Anton Santyr, Brendan Tasserie, Jordy Gwun, Dave Jung, Hyun Ho Valiante, Taufik Ibrahim, George M Wennberg, Richard Kalia, Suneil K Lozano, Andres M Brain Commun Original Article Deep brain stimulation is a treatment option for patients with drug-resistant epilepsy. The precise mechanism of neuromodulation in epilepsy is unknown, and biomarkers are needed for optimizing treatment. The aim of this study was to describe the neural network associated with deep brain stimulation targets for epilepsy and to explore its potential application as a novel biomarker for neuromodulation. Using seed-to-voxel functional connectivity maps, weighted by seizure outcomes, brain areas associated with stimulation were identified in normative resting state functional scans of 1000 individuals. To pinpoint specific regions in the normative epilepsy deep brain stimulation network, we examined overlapping areas of functional connectivity between the anterior thalamic nucleus, centromedian thalamic nucleus, hippocampus and less studied epilepsy deep brain stimulation targets. Graph network analysis was used to describe the relationship between regions in the identified network. Furthermore, we examined the associations of the epilepsy deep brain stimulation network with disease pathophysiology, canonical resting state networks and findings from a systematic review of resting state functional MRI studies in epilepsy deep brain stimulation patients. Cortical nodes identified in the normative epilepsy deep brain stimulation network were in the anterior and posterior cingulate, medial frontal and sensorimotor cortices, frontal operculum and bilateral insulae. Subcortical nodes of the network were in the basal ganglia, mesencephalon, basal forebrain and cerebellum. Anterior thalamic nucleus was identified as a central hub in the network with the highest betweenness and closeness values, while centromedian thalamic nucleus and hippocampus showed average centrality values. The caudate nucleus and mammillothalamic tract also displayed high centrality values. The anterior cingulate cortex was identified as an important cortical hub associated with the effect of deep brain stimulation in epilepsy. The neural network of deep brain stimulation targets shared hubs with known epileptic networks and brain regions involved in seizure propagation and generalization. Two cortical clusters identified in the epilepsy deep brain stimulation network included regions corresponding to resting state networks, mainly the default mode and salience networks. Our results were concordant with findings from a systematic review of resting state functional MRI studies in patients with deep brain stimulation for epilepsy. Our findings suggest that the various epilepsy deep brain stimulation targets share a common cortico-subcortical network, which might in part underpin the antiseizure effects of stimulation. Interindividual differences in this network functional connectivity could potentially be used as biomarkers in selection of patients, stimulation parameters and neuromodulation targets. Oxford University Press 2022-04-06 /pmc/articles/PMC9123846/ /pubmed/35611305 http://dx.doi.org/10.1093/braincomms/fcac092 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of the Guarantors of Brain. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Vetkas, Artur Germann, Jürgen Elias, Gavin Loh, Aaron Boutet, Alexandre Yamamoto, Kazuaki Sarica, Can Samuel, Nardin Milano, Vanessa Fomenko, Anton Santyr, Brendan Tasserie, Jordy Gwun, Dave Jung, Hyun Ho Valiante, Taufik Ibrahim, George M Wennberg, Richard Kalia, Suneil K Lozano, Andres M Identifying the neural network for neuromodulation in epilepsy through connectomics and graphs |
title | Identifying the neural network for neuromodulation in epilepsy through connectomics and graphs |
title_full | Identifying the neural network for neuromodulation in epilepsy through connectomics and graphs |
title_fullStr | Identifying the neural network for neuromodulation in epilepsy through connectomics and graphs |
title_full_unstemmed | Identifying the neural network for neuromodulation in epilepsy through connectomics and graphs |
title_short | Identifying the neural network for neuromodulation in epilepsy through connectomics and graphs |
title_sort | identifying the neural network for neuromodulation in epilepsy through connectomics and graphs |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9123846/ https://www.ncbi.nlm.nih.gov/pubmed/35611305 http://dx.doi.org/10.1093/braincomms/fcac092 |
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