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Neuroplasticity of Glioma Patients: Brain Structure and Topological Network
Glioma is the most common primary malignant brain tumor in adults. It accounts for about 75% of such tumors and occurs more commonly in men. The incidence rate has been increasing in the past 30 years. Moreover, the 5-year overall survival rate of glioma patients is < 35%. Different locations, gr...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9136300/ https://www.ncbi.nlm.nih.gov/pubmed/35645982 http://dx.doi.org/10.3389/fneur.2022.871613 |
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author | Lv, Kun Cao, Xin Wang, Rong Du, Peng Fu, Junyan Geng, Daoying Zhang, Jun |
author_facet | Lv, Kun Cao, Xin Wang, Rong Du, Peng Fu, Junyan Geng, Daoying Zhang, Jun |
author_sort | Lv, Kun |
collection | PubMed |
description | Glioma is the most common primary malignant brain tumor in adults. It accounts for about 75% of such tumors and occurs more commonly in men. The incidence rate has been increasing in the past 30 years. Moreover, the 5-year overall survival rate of glioma patients is < 35%. Different locations, grades, and molecular characteristics of gliomas can lead to different behavioral deficits and prognosis, which are closely related to patients' quality of life and associated with neuroplasticity. Some advanced magnetic resonance imaging (MRI) technologies can explore the neuroplasticity of structural, topological, biochemical metabolism, and related mechanisms, which may contribute to the improvement of prognosis and function in glioma patients. In this review, we summarized the studies conducted on structural and topological plasticity of glioma patients through different MRI technologies and discussed future research directions. Previous studies have found that glioma itself and related functional impairments can lead to structural and topological plasticity using multimodal MRI. However, neuroplasticity caused by highly heterogeneous gliomas is not fully understood, and should be further explored through multimodal MRI. In addition, the individualized prediction of functional prognosis of glioma patients from the functional level based on machine learning (ML) is promising. These approaches and the introduction of ML can further shed light on the neuroplasticity and related mechanism of the brain, which will be helpful for management of glioma patients. |
format | Online Article Text |
id | pubmed-9136300 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91363002022-05-28 Neuroplasticity of Glioma Patients: Brain Structure and Topological Network Lv, Kun Cao, Xin Wang, Rong Du, Peng Fu, Junyan Geng, Daoying Zhang, Jun Front Neurol Neurology Glioma is the most common primary malignant brain tumor in adults. It accounts for about 75% of such tumors and occurs more commonly in men. The incidence rate has been increasing in the past 30 years. Moreover, the 5-year overall survival rate of glioma patients is < 35%. Different locations, grades, and molecular characteristics of gliomas can lead to different behavioral deficits and prognosis, which are closely related to patients' quality of life and associated with neuroplasticity. Some advanced magnetic resonance imaging (MRI) technologies can explore the neuroplasticity of structural, topological, biochemical metabolism, and related mechanisms, which may contribute to the improvement of prognosis and function in glioma patients. In this review, we summarized the studies conducted on structural and topological plasticity of glioma patients through different MRI technologies and discussed future research directions. Previous studies have found that glioma itself and related functional impairments can lead to structural and topological plasticity using multimodal MRI. However, neuroplasticity caused by highly heterogeneous gliomas is not fully understood, and should be further explored through multimodal MRI. In addition, the individualized prediction of functional prognosis of glioma patients from the functional level based on machine learning (ML) is promising. These approaches and the introduction of ML can further shed light on the neuroplasticity and related mechanism of the brain, which will be helpful for management of glioma patients. Frontiers Media S.A. 2022-05-13 /pmc/articles/PMC9136300/ /pubmed/35645982 http://dx.doi.org/10.3389/fneur.2022.871613 Text en Copyright © 2022 Lv, Cao, Wang, Du, Fu, Geng and Zhang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neurology Lv, Kun Cao, Xin Wang, Rong Du, Peng Fu, Junyan Geng, Daoying Zhang, Jun Neuroplasticity of Glioma Patients: Brain Structure and Topological Network |
title | Neuroplasticity of Glioma Patients: Brain Structure and Topological Network |
title_full | Neuroplasticity of Glioma Patients: Brain Structure and Topological Network |
title_fullStr | Neuroplasticity of Glioma Patients: Brain Structure and Topological Network |
title_full_unstemmed | Neuroplasticity of Glioma Patients: Brain Structure and Topological Network |
title_short | Neuroplasticity of Glioma Patients: Brain Structure and Topological Network |
title_sort | neuroplasticity of glioma patients: brain structure and topological network |
topic | Neurology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9136300/ https://www.ncbi.nlm.nih.gov/pubmed/35645982 http://dx.doi.org/10.3389/fneur.2022.871613 |
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