Objective: Vagal Nerve Stimulation (VNS) is an effective treatment for Drug-Resistant (DR) epilepsy. Albeit the corroborated effectiveness of VNS, little is known about how VNS works. We aim to leverage quantitative Electroencephalography (qEEG) to study how the brain responds to VNS cycles. Methods: Eighteen subjects with DR epilepsy were enrolled in our study. 64-channel EEG was recorded during VNS stimulation. Periods of stimulation (VNS), preceding (preVNS) and following stimulation (postVNS) were identified via an electrode placed on the stimulator. We used qEEG analysis to assess changes in spectral and network activity that characterize these conditions. Graph theory metrics were used to calculate differences in network connectivity. Results: No differences were found in spectral activity between preVNS, VNS, and postVNS. Graph theory showed consistent changes in network organization expressed by Small World Index (SWI), Betweenness Centrality (BtwC), and Global Efficiency (gE). These changes were most significant in the slow EEG bands. Conclusions: In DR epilepsy, VNS has a significant effect on brain network activity, as assessed by EEG connectivity, acting on widespread network distribution rather than band-power. Significance: Our findings support the hypothesis that VNS acts on epilepsy by influencing diffuse net -work connectivity in the brain. (c) 2022 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Lanzone, J., Boscarino, M., Tufo, T., Di Lorenzo, G., Ricci, L., Colicchio, G., et al. (2022). Vagal nerve stimulation cycles alter EEG connectivity in drug-resistant epileptic patients: A study with graph theory metrics. CLINICAL NEUROPHYSIOLOGY, 142, 59-67 [10.1016/j.clinph.2022.07.503].

Vagal nerve stimulation cycles alter EEG connectivity in drug-resistant epileptic patients: A study with graph theory metrics

Di Lorenzo, G;
2022-10-01

Abstract

Objective: Vagal Nerve Stimulation (VNS) is an effective treatment for Drug-Resistant (DR) epilepsy. Albeit the corroborated effectiveness of VNS, little is known about how VNS works. We aim to leverage quantitative Electroencephalography (qEEG) to study how the brain responds to VNS cycles. Methods: Eighteen subjects with DR epilepsy were enrolled in our study. 64-channel EEG was recorded during VNS stimulation. Periods of stimulation (VNS), preceding (preVNS) and following stimulation (postVNS) were identified via an electrode placed on the stimulator. We used qEEG analysis to assess changes in spectral and network activity that characterize these conditions. Graph theory metrics were used to calculate differences in network connectivity. Results: No differences were found in spectral activity between preVNS, VNS, and postVNS. Graph theory showed consistent changes in network organization expressed by Small World Index (SWI), Betweenness Centrality (BtwC), and Global Efficiency (gE). These changes were most significant in the slow EEG bands. Conclusions: In DR epilepsy, VNS has a significant effect on brain network activity, as assessed by EEG connectivity, acting on widespread network distribution rather than band-power. Significance: Our findings support the hypothesis that VNS acts on epilepsy by influencing diffuse net -work connectivity in the brain. (c) 2022 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore MED/25
English
Connectivity
EEG
Epilepsy
Graph Theory
Vagal Nerve Stimulation
qEEG
Electroencephalography
Humans
Treatment Outcome
Drug Resistant Epilepsy
Epilepsy
Vagus Nerve Stimulation
Lanzone, J., Boscarino, M., Tufo, T., Di Lorenzo, G., Ricci, L., Colicchio, G., et al. (2022). Vagal nerve stimulation cycles alter EEG connectivity in drug-resistant epileptic patients: A study with graph theory metrics. CLINICAL NEUROPHYSIOLOGY, 142, 59-67 [10.1016/j.clinph.2022.07.503].
Lanzone, J; Boscarino, M; Tufo, T; Di Lorenzo, G; Ricci, L; Colicchio, G; Di Lazzaro, V; Tombini, M; Assenza, G
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/309300
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