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Network phenotypes and their clinical significance in temporal lobe epilepsy using machine learning applications to morphological and functional graph theory metrics

Machine learning analyses were performed on graph theory (GT) metrics extracted from brain functional and morphological data from temporal lobe epilepsy (TLE) patients in order to identify intrinsic network phenotypes and characterize their clinical significance. Participants were 97 TLE and 36 heal...

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Autores principales: Garcia-Ramos, Camille, Nair, Veena, Maganti, Rama, Mathis, Jedidiah, Conant, Lisa L., Prabhakaran, Vivek, Binder, Jeffrey R., Meyerand, Beth, Hermann, Bruce, Struck, Aaron F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9402557/
https://www.ncbi.nlm.nih.gov/pubmed/36002603
http://dx.doi.org/10.1038/s41598-022-18495-z
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author Garcia-Ramos, Camille
Nair, Veena
Maganti, Rama
Mathis, Jedidiah
Conant, Lisa L.
Prabhakaran, Vivek
Binder, Jeffrey R.
Meyerand, Beth
Hermann, Bruce
Struck, Aaron F.
author_facet Garcia-Ramos, Camille
Nair, Veena
Maganti, Rama
Mathis, Jedidiah
Conant, Lisa L.
Prabhakaran, Vivek
Binder, Jeffrey R.
Meyerand, Beth
Hermann, Bruce
Struck, Aaron F.
author_sort Garcia-Ramos, Camille
collection PubMed
description Machine learning analyses were performed on graph theory (GT) metrics extracted from brain functional and morphological data from temporal lobe epilepsy (TLE) patients in order to identify intrinsic network phenotypes and characterize their clinical significance. Participants were 97 TLE and 36 healthy controls from the Epilepsy Connectome Project. Each imaging modality (i.e., Resting-state functional Magnetic Resonance Imaging (RS-fMRI), and structural MRI) rendered 2 clusters: one comparable to controls and one deviating from controls. Participants were minimally overlapping across the identified clusters, suggesting that an abnormal functional GT phenotype did not necessarily mean an abnormal morphological GT phenotype for the same subject. Morphological clusters were associated with a significant difference in the estimated lifetime number of generalized tonic–clonic seizures and functional cluster membership was associated with age. Furthermore, controls exhibited significant correlations between functional GT metrics and cognition, while for TLE participants morphological GT metrics were linked to cognition, suggesting a dissociation between higher cognitive abilities and GT-derived network measures. Overall, these findings demonstrate the existence of clinically meaningful minimally overlapping phenotypes of morphological and functional GT networks. Functional network properties may underlie variance in cognition in healthy brains, but in the pathological state of epilepsy the cognitive limits might be primarily related to structural cerebral network properties.
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spelling pubmed-94025572022-08-26 Network phenotypes and their clinical significance in temporal lobe epilepsy using machine learning applications to morphological and functional graph theory metrics Garcia-Ramos, Camille Nair, Veena Maganti, Rama Mathis, Jedidiah Conant, Lisa L. Prabhakaran, Vivek Binder, Jeffrey R. Meyerand, Beth Hermann, Bruce Struck, Aaron F. Sci Rep Article Machine learning analyses were performed on graph theory (GT) metrics extracted from brain functional and morphological data from temporal lobe epilepsy (TLE) patients in order to identify intrinsic network phenotypes and characterize their clinical significance. Participants were 97 TLE and 36 healthy controls from the Epilepsy Connectome Project. Each imaging modality (i.e., Resting-state functional Magnetic Resonance Imaging (RS-fMRI), and structural MRI) rendered 2 clusters: one comparable to controls and one deviating from controls. Participants were minimally overlapping across the identified clusters, suggesting that an abnormal functional GT phenotype did not necessarily mean an abnormal morphological GT phenotype for the same subject. Morphological clusters were associated with a significant difference in the estimated lifetime number of generalized tonic–clonic seizures and functional cluster membership was associated with age. Furthermore, controls exhibited significant correlations between functional GT metrics and cognition, while for TLE participants morphological GT metrics were linked to cognition, suggesting a dissociation between higher cognitive abilities and GT-derived network measures. Overall, these findings demonstrate the existence of clinically meaningful minimally overlapping phenotypes of morphological and functional GT networks. Functional network properties may underlie variance in cognition in healthy brains, but in the pathological state of epilepsy the cognitive limits might be primarily related to structural cerebral network properties. Nature Publishing Group UK 2022-08-24 /pmc/articles/PMC9402557/ /pubmed/36002603 http://dx.doi.org/10.1038/s41598-022-18495-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Garcia-Ramos, Camille
Nair, Veena
Maganti, Rama
Mathis, Jedidiah
Conant, Lisa L.
Prabhakaran, Vivek
Binder, Jeffrey R.
Meyerand, Beth
Hermann, Bruce
Struck, Aaron F.
Network phenotypes and their clinical significance in temporal lobe epilepsy using machine learning applications to morphological and functional graph theory metrics
title Network phenotypes and their clinical significance in temporal lobe epilepsy using machine learning applications to morphological and functional graph theory metrics
title_full Network phenotypes and their clinical significance in temporal lobe epilepsy using machine learning applications to morphological and functional graph theory metrics
title_fullStr Network phenotypes and their clinical significance in temporal lobe epilepsy using machine learning applications to morphological and functional graph theory metrics
title_full_unstemmed Network phenotypes and their clinical significance in temporal lobe epilepsy using machine learning applications to morphological and functional graph theory metrics
title_short Network phenotypes and their clinical significance in temporal lobe epilepsy using machine learning applications to morphological and functional graph theory metrics
title_sort network phenotypes and their clinical significance in temporal lobe epilepsy using machine learning applications to morphological and functional graph theory metrics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9402557/
https://www.ncbi.nlm.nih.gov/pubmed/36002603
http://dx.doi.org/10.1038/s41598-022-18495-z
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