A structured narrative review on VNS-treated drug-resistant epilepsy: EEG markers, neurochemical mechanisms, and future biomarker-driven computational directions

Vagus nerve stimulation (VNS) is an established adjunctive therapy for drug-resistant epilepsy. Experimental and clinical evidence indicates that its therapeutic effects involve distributed brain networks and multiple neurochemical pathways. Electroencephalography (EEG) has been widely used to characterize VNS-related neurophysiological changes, including alterations in conventional oscillatory activity, functional connectivity, and, more recently, aperiodic spectral components such as the spectral exponent and spectral offset. However, these EEG findings are often interpreted without sufficient consideration of the neurochemical intermediates that may contribute to the observed electrophysiological changes. In this structured narrative review, primarily focused on epilepsy, we examine how VNS-related EEG findings can be interpreted in light of noradrenergic, serotonergic, cholinergic, GABAergic, and neurotrophic mechanisms. We also discuss methodological challenges in the analysis of periodic and aperiodic EEG components and outline how machine learning approaches and adaptive closed-loop neuromodulation strategies may support the development of clinically useful VNS biomarkers.

Authors

Institutions

Publication Details

Journal
Frontiers in Neuroscience
Published
2026-04-28
DOI
https://doi.org/10.3389/fnins.2026.1813014
Primary Topic
Vagus Nerve Stimulation Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A structured narrative review on VNS-treated drug-resistant epilepsy: EEG markers, neurochemical mechanisms, and future biomarker-driven computational directions

Francesco Marrosu, Antonella Muroni, Alberto Maleci, Krishna Moorthi Bhat
Frontiers in Neuroscience
Vagus Nerve Stimulation Research
article

A structured narrative review on VNS-treated drug-resistant epilepsy: EEG markers, neurochemical mechanisms, and future biomarker-driven computational directions

Francesco Marrosu, Antonella Muroni, Alberto Maleci, Krishna Moorthi Bhat
article en

Abstract

Vagus nerve stimulation (VNS) is an established adjunctive therapy for drug-resistant epilepsy. Experimental and clinical evidence indicates that its therapeutic effects involve distributed brain networks and multiple neurochemical pathways. Electroencephalography (EEG) has been widely used to characterize VNS-related neurophysiological changes, including alterations in conventional oscillatory activity, functional connectivity, and, more recently, aperiodic spectral components such as the spectral exponent and spectral offset. However, these EEG findings are often interpreted without sufficient consideration of the neurochemical intermediates that may contribute to the observed electrophysiological changes. In this structured narrative review, primarily focused on epilepsy, we examine how VNS-related EEG findings can be interpreted in light of noradrenergic, serotonergic, cholinergic, GABAergic, and neurotrophic mechanisms. We also discuss methodological challenges in the analysis of periodic and aperiodic EEG components and outline how machine learning approaches and adaptive closed-loop neuromodulation strategies may support the development of clinically useful VNS biomarkers.

Frontiers in NeuroscienceVol. 20
University of Cagliari (IT), University of Alabama at Birmingham (US), Azienda Ospedaliero-Universitaria Cagliari (IT)
Good health and well-being
Openalex Percentile: Top 10%
Vagus Nerve Stimulation Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.