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Network topology in brain tumor patients with and without structural epilepsy: a prospective MEG study
BACKGROUND: It was proposed that network topology is altered in brain tumor patients. However, there is no consensus on the pattern of these changes and evidence on potential drivers is lacking. OBJECTIVES: We aimed to characterize neurooncological patients’ network topology by analyzing glial brain...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10467269/ https://www.ncbi.nlm.nih.gov/pubmed/37655227 http://dx.doi.org/10.1177/17562864231190298 |
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author | Ladisich, Barbara Rampp, Stefan Trinka, Eugen Weisz, Nathan Schwartz, Christoph Kraus, Theo Sherif, Camillo Marhold, Franz Demarchi, Gianpaolo |
author_facet | Ladisich, Barbara Rampp, Stefan Trinka, Eugen Weisz, Nathan Schwartz, Christoph Kraus, Theo Sherif, Camillo Marhold, Franz Demarchi, Gianpaolo |
author_sort | Ladisich, Barbara |
collection | PubMed |
description | BACKGROUND: It was proposed that network topology is altered in brain tumor patients. However, there is no consensus on the pattern of these changes and evidence on potential drivers is lacking. OBJECTIVES: We aimed to characterize neurooncological patients’ network topology by analyzing glial brain tumors (GBTs) and brain metastases (BMs) with respect to the presence of structural epilepsy. METHODS: Network topology derived from resting state magnetoencephalography was compared between (1) patients and controls, (2) GBTs and BMs, and (3) patients with (PSEs) and without structural epilepsy (PNSEs). Eligible patients were investigated from February 2019 to March 2021. We calculated whole brain (WB) connectivity in six frequency bands, network topological parameters (node degree, average shortest path length, local clustering coefficient) and performed a stratification, where differences in power were identified. For data analysis, we used Fieldtrip, Brain Connectivity MATLAB toolboxes, and in-house built scripts. RESULTS: We included 41 patients (21 men), with a mean age of 60.1 years (range 23–82), of those were: GBTs (n = 23), BMs (n = 14), and other histologies (n = 4). Statistical analysis revealed a significantly decreased WB node degree in patients versus controls in every frequency range at the corrected level (p(1–30Hz) = 0.002, p(γ) = 0.002, p(β) = 0.002, p(α) = 0.002, p(θ) = 0.024, and p(δ) = 0.002). At the descriptive level, we found a significant augmentation for WB local clustering coefficient (p(1–30Hz) = 0.031, p(δ) = 0.013) in patients compared to controls, which did not persist the false discovery rate correction. No differences regarding networks of GBTs compared to BMs were identified. However, we found a significant increase in WB local clustering coefficient (p(θ) = 0.048) and decrease in WB node degree (p(α) = 0.039) in PSEs versus PNSEs at the uncorrected level. CONCLUSION: Our data suggest that network topology is altered in brain tumor patients. Histology per se might not, however, tumor-related epilepsy seems to influence the brain’s functional network. Longitudinal studies and analysis of possible confounders are required to substantiate these findings. |
format | Online Article Text |
id | pubmed-10467269 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-104672692023-08-31 Network topology in brain tumor patients with and without structural epilepsy: a prospective MEG study Ladisich, Barbara Rampp, Stefan Trinka, Eugen Weisz, Nathan Schwartz, Christoph Kraus, Theo Sherif, Camillo Marhold, Franz Demarchi, Gianpaolo Ther Adv Neurol Disord Original Research BACKGROUND: It was proposed that network topology is altered in brain tumor patients. However, there is no consensus on the pattern of these changes and evidence on potential drivers is lacking. OBJECTIVES: We aimed to characterize neurooncological patients’ network topology by analyzing glial brain tumors (GBTs) and brain metastases (BMs) with respect to the presence of structural epilepsy. METHODS: Network topology derived from resting state magnetoencephalography was compared between (1) patients and controls, (2) GBTs and BMs, and (3) patients with (PSEs) and without structural epilepsy (PNSEs). Eligible patients were investigated from February 2019 to March 2021. We calculated whole brain (WB) connectivity in six frequency bands, network topological parameters (node degree, average shortest path length, local clustering coefficient) and performed a stratification, where differences in power were identified. For data analysis, we used Fieldtrip, Brain Connectivity MATLAB toolboxes, and in-house built scripts. RESULTS: We included 41 patients (21 men), with a mean age of 60.1 years (range 23–82), of those were: GBTs (n = 23), BMs (n = 14), and other histologies (n = 4). Statistical analysis revealed a significantly decreased WB node degree in patients versus controls in every frequency range at the corrected level (p(1–30Hz) = 0.002, p(γ) = 0.002, p(β) = 0.002, p(α) = 0.002, p(θ) = 0.024, and p(δ) = 0.002). At the descriptive level, we found a significant augmentation for WB local clustering coefficient (p(1–30Hz) = 0.031, p(δ) = 0.013) in patients compared to controls, which did not persist the false discovery rate correction. No differences regarding networks of GBTs compared to BMs were identified. However, we found a significant increase in WB local clustering coefficient (p(θ) = 0.048) and decrease in WB node degree (p(α) = 0.039) in PSEs versus PNSEs at the uncorrected level. CONCLUSION: Our data suggest that network topology is altered in brain tumor patients. Histology per se might not, however, tumor-related epilepsy seems to influence the brain’s functional network. Longitudinal studies and analysis of possible confounders are required to substantiate these findings. SAGE Publications 2023-08-29 /pmc/articles/PMC10467269/ /pubmed/37655227 http://dx.doi.org/10.1177/17562864231190298 Text en © The Author(s), 2023 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Original Research Ladisich, Barbara Rampp, Stefan Trinka, Eugen Weisz, Nathan Schwartz, Christoph Kraus, Theo Sherif, Camillo Marhold, Franz Demarchi, Gianpaolo Network topology in brain tumor patients with and without structural epilepsy: a prospective MEG study |
title | Network topology in brain tumor patients with and without structural epilepsy: a prospective MEG study |
title_full | Network topology in brain tumor patients with and without structural epilepsy: a prospective MEG study |
title_fullStr | Network topology in brain tumor patients with and without structural epilepsy: a prospective MEG study |
title_full_unstemmed | Network topology in brain tumor patients with and without structural epilepsy: a prospective MEG study |
title_short | Network topology in brain tumor patients with and without structural epilepsy: a prospective MEG study |
title_sort | network topology in brain tumor patients with and without structural epilepsy: a prospective meg study |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10467269/ https://www.ncbi.nlm.nih.gov/pubmed/37655227 http://dx.doi.org/10.1177/17562864231190298 |
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