ArticleHuman brain mapping2020
Lateralization of epilepsy using intra-hemispheric brain networks based on resting-state MEG data.
Article in Human brain mapping, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 21 citations in OpenAlex.
- Dynamic evolution of frontal-temporal network connectivity in temporal lobe epilepsy: A magnetoencephalography study.Human brain mapping · 2024Article
- Genetic fingerprinting with heritable phenotypes of the resting-state brain network topology.Communications biology · 2024Article
- Hub overload and failure as a final common pathway in neurological brain network disorders.Network neuroscience (Cambridge, Mass.) · 2024Review
- Article
- MEG Node Degree for Focus Localization: Comparison with Invasive EEG.Biomedicines · 2023Article
- The role of epidemic spreading in seizure dynamics and epilepsy surgery.Network neuroscience (Cambridge, Mass.) · 2023Article
- Reproducibility of graph measures derived from resting-state MEG functional connectivity metrics in sensor and source spaces.Human brain mapping · 2022Article
- Presurgical Evaluation of Epilepsy Using Resting-State MEG Functional Connectivity.Frontiers in human neuroscience · 2021Review
- Lateralization of epilepsy using intra-hemispheric brain networks based on resting-state MEG data.Human brain mapping · 2020Article
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Authors and funding
3 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Focal epilepsy originates within networks in one hemisphere. However, previous studies have investigated network topologies for the entire brain. In this study, magnetoencephalography (MEG) was used to investigate functional intra-hemispheric networks of healthy controls (HCs) and patients with left- or right-hemispheric temporal lobe or temporal plus extra-temporal lobe epilepsy. 22 HCs, 25 left patients (LPs), and 16 right patients (RPs) were enrolled. The debiased weighted phase lag index was used to calculate functional connectivity between 246 brain regions in six frequency bands. Global efficiency, characteristic path length, and transitivity were computed for left and right intra-hemispheric networks. The right global graph measures (GGMs) in the theta band were significantly different (p < .005) between RPs and both LPs and HCs. Right and left GGMs in higher frequency bands were significantly different (p < .05) between HCs and the patients. Right GGMs were used as input features of a Naïve-Bayes classifier to classify LPs and RPs (78.0% accuracy) and all three groups (75.5% accuracy). The complete theta band brain networks were compared between LPs and RPs with network-based statistics (NBS) and with the clustering coefficient (CC), nodal efficiency (NE), betweenness centrality (BC), and eigenvector centrality (EVC). NBS identified a subnetwork primarily composed of right intra-hemispheric connections. Significantly different (p < .05) nodes were primarily in the right hemisphere for the CC and NE and primarily in the left hemisphere for the BC and EVC. These results indicate that intra-hemispheric MEG networks may be incorporated in the diagnosis and lateralization of focal epilepsy.
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