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Resting-state functional connectivity predicts individual language impairment of patients with left hemispheric gliomas involving language network

Language deficits following brain tumors should consider the dynamic interactions between different tumor growth kinetics and functional network reorganization. We measured the resting-state functional connectivity of 126 patients with left cerebral gliomas involving language network areas, includin...

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Autores principales: Yuan, Binke, Zhang, Nan, Yan, Jing, Cheng, Jingliang, Lu, Junfeng, Wu, Jinsong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6838935/
https://www.ncbi.nlm.nih.gov/pubmed/31693978
http://dx.doi.org/10.1016/j.nicl.2019.102023
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author Yuan, Binke
Zhang, Nan
Yan, Jing
Cheng, Jingliang
Lu, Junfeng
Wu, Jinsong
author_facet Yuan, Binke
Zhang, Nan
Yan, Jing
Cheng, Jingliang
Lu, Junfeng
Wu, Jinsong
author_sort Yuan, Binke
collection PubMed
description Language deficits following brain tumors should consider the dynamic interactions between different tumor growth kinetics and functional network reorganization. We measured the resting-state functional connectivity of 126 patients with left cerebral gliomas involving language network areas, including 77 patients with low-grade gliomas (LGG) and 49 patients with high-grade gliomas (HGG). Functional network mapping for language was performed by construction of a multivariate machine learning-based prediction model of individual aphasia quotient (AQ), a summary score that indicates overall severity of language impairment. We found that the AQ scores for HGG patients were significantly lower than those of LGG patients. The prediction accuracy of HGG patients (R(2) = 0.27, permutation P = 0.007) was much higher than that of LGG patients (R(2) = 0.09, permutation P = 0.032). The rsFC regions predictive of LGG's AQ involved the bilateral frontal, temporal, and parietal lobes, subcortical regions, and bilateral cerebro-cerebellar connections, mainly in regions belonging to the canonical language network. The functional network of language processing for HGG patients showed strong dependence on connections of the left cerebro-cerebellar connections, limbic system, and the temporal, occipital, and prefrontal lobes. Together, our findings suggested that individual language processing of glioma patients links large-scale, bilateral, cortico-subcortical, and cerebro-cerebellar functional networks with different network reorganizational mechanisms underlying the different levels of language impairments in LGG and HGG patients.
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spelling pubmed-68389352019-11-12 Resting-state functional connectivity predicts individual language impairment of patients with left hemispheric gliomas involving language network Yuan, Binke Zhang, Nan Yan, Jing Cheng, Jingliang Lu, Junfeng Wu, Jinsong Neuroimage Clin Regular Article Language deficits following brain tumors should consider the dynamic interactions between different tumor growth kinetics and functional network reorganization. We measured the resting-state functional connectivity of 126 patients with left cerebral gliomas involving language network areas, including 77 patients with low-grade gliomas (LGG) and 49 patients with high-grade gliomas (HGG). Functional network mapping for language was performed by construction of a multivariate machine learning-based prediction model of individual aphasia quotient (AQ), a summary score that indicates overall severity of language impairment. We found that the AQ scores for HGG patients were significantly lower than those of LGG patients. The prediction accuracy of HGG patients (R(2) = 0.27, permutation P = 0.007) was much higher than that of LGG patients (R(2) = 0.09, permutation P = 0.032). The rsFC regions predictive of LGG's AQ involved the bilateral frontal, temporal, and parietal lobes, subcortical regions, and bilateral cerebro-cerebellar connections, mainly in regions belonging to the canonical language network. The functional network of language processing for HGG patients showed strong dependence on connections of the left cerebro-cerebellar connections, limbic system, and the temporal, occipital, and prefrontal lobes. Together, our findings suggested that individual language processing of glioma patients links large-scale, bilateral, cortico-subcortical, and cerebro-cerebellar functional networks with different network reorganizational mechanisms underlying the different levels of language impairments in LGG and HGG patients. Elsevier 2019-10-19 /pmc/articles/PMC6838935/ /pubmed/31693978 http://dx.doi.org/10.1016/j.nicl.2019.102023 Text en © 2019 The Author(s) http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Regular Article
Yuan, Binke
Zhang, Nan
Yan, Jing
Cheng, Jingliang
Lu, Junfeng
Wu, Jinsong
Resting-state functional connectivity predicts individual language impairment of patients with left hemispheric gliomas involving language network
title Resting-state functional connectivity predicts individual language impairment of patients with left hemispheric gliomas involving language network
title_full Resting-state functional connectivity predicts individual language impairment of patients with left hemispheric gliomas involving language network
title_fullStr Resting-state functional connectivity predicts individual language impairment of patients with left hemispheric gliomas involving language network
title_full_unstemmed Resting-state functional connectivity predicts individual language impairment of patients with left hemispheric gliomas involving language network
title_short Resting-state functional connectivity predicts individual language impairment of patients with left hemispheric gliomas involving language network
title_sort resting-state functional connectivity predicts individual language impairment of patients with left hemispheric gliomas involving language network
topic Regular Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6838935/
https://www.ncbi.nlm.nih.gov/pubmed/31693978
http://dx.doi.org/10.1016/j.nicl.2019.102023
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