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Brain Functional Connectivity in Low- and High-Grade Gliomas: Differences in Network Dynamics Associated with Tumor Grade and Location
SIMPLE SUMMARY: This study investigates brain network modifications related to tumor grade and location using resting-state functional magnetic resonance imaging and graph theory. We demonstrated that low-grade gliomas (LGG) lead to increased efficiency of the surrounding functional network, while h...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9324249/ https://www.ncbi.nlm.nih.gov/pubmed/35884387 http://dx.doi.org/10.3390/cancers14143327 |
Sumario: | SIMPLE SUMMARY: This study investigates brain network modifications related to tumor grade and location using resting-state functional magnetic resonance imaging and graph theory. We demonstrated that low-grade gliomas (LGG) lead to increased efficiency of the surrounding functional network, while high-grade gliomas (HGG) seem to disrupt brain connectivity in remote areas. Tumor location appears to influence the pattern of reorganization, including the recruitment of the contralateral hemisphere. Overall, LGG may show more favorable connectivity changes than HGG. If confirmed by future studies, the ability to discriminate between ‘maladaptive’ (detrimental) and ‘adaptive’ (beneficial) functional reorganization based on graph theory metrics may provide biomarkers to select patients for surgery and monitor recovery. ABSTRACT: Brain tumors lead to modifications of brain networks. Graph theory plays an important role in clarifying the principles of brain connectivity. Our objective was to investigate network modifications related to tumor grade and location using resting-state functional magnetic resonance imaging (fMRI) and graph theory. We retrospectively studied 30 low-grade (LGG), 30 high-grade (HGG) left-hemispheric glioma patients and 20 healthy controls (HC) with rs-fMRI. Tumor location was labeled as: frontal, temporal, parietal, insular or occipital. We collected patients’ clinical data from records. We analyzed whole-brain and hemispheric networks in all patients and HC. Subsequently, we studied lobar networks in subgroups of patients divided by tumor location. Seven graph-theoretical metrics were calculated (FDR p < 0.05). Connectograms were computed for significant nodes. The two-tailed Student t-test or Mann–Whitney U-test (p < 0.05) were used to compare graph metrics and clinical data. The hemispheric network analysis showed increased ipsilateral connectivity for LGG (global efficiency p = 0.03) and decreased contralateral connectivity for HGG (degree/cost p = 0.028). Frontal and temporal tumors showed bilateral modifications; parietal and insular tumors showed only local effects. Temporal tumors led to a bilateral decrease in all graph metrics. Tumor grade and location influence the pattern of network reorganization. LGG may show more favorable network changes than HGG, reflecting fewer clinical deficits. |
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