EEG Reactivity Predicts Individual Efficacy of Vagal Nerve Stimulation in Intractable Epileptics

Publikace nespadá pod Lékařskou fakultu, ale pod Středoevropský technologický institut. Oficiální stránka publikace je na webu muni.cz.

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BRÁZDIL Milan DOLEŽALOVÁ Irena KORIŤÁKOVÁ Eva CHLÁDEK Jan ROMAN Robert PAIL Martin JURAK Pavel SHAW Daniel J. CHRASTINA Jan

Druh Článek v odborném periodiku
Časopis / Zdroj FRONTIERS IN NEUROLOGY
Fakulta / Pracoviště MU

Středoevropský technologický institut

Citace
Doi http://dx.doi.org/10.3389/fneur.2019.00392
Klíčová slova vagal nerve stimulation; neurostimulation; epilepsy; efficacy prediction; EEG reactivity; epilepsy treatment
Popis Background: Chronic vagal nerve stimulation (VNS) is a well-established non-pharmacological treatment option for drug-resistant epilepsy. This study sought to develop a statistical model for prediction of VNS efficacy. We hypothesized that reactivity of the electroencephalogram (EEG) to external stimuli measured during routine preoperative evaluation differs between VNS responders and non-responders. Materials and Methods: Power spectral analyses were computed retrospectively on pre-operative EEG recordings from 60 epileptic patients with VNS. Thirty five responders and 25 non-responders were compared on the relative power values in four standard frequency bands and eight conditions of clinical assessment-eyes opening/closing, photic stimulation, and hyperventilation. Using logistic regression, groups of electrodes within anatomical areas identified as maximally discriminative by n leave-one-out iterations were used to classify patients. The reliability of the predictive model was verified with an independent data-set from 22 additional patients. Results: Power spectral analyses revealed significant differences in EEG reactivity between responders and non-responders; specifically, the dynamics of alpha and gamma activity strongly reflected VNS efficacy. Using individual EEG reactivity to develop and validate a predictive model, we discriminated between responders and non-responders with 86% accuracy, 83% sensitivity, and 90% specificity. Conclusion: We present a new statistical model with which EEG reactivity to external stimuli during routine presurgical evaluation can be seen as a promising avenue for the identification of patients with favorable VNS outcome. This novel method for the prediction of VNS efficacy might represent a breakthrough in the management of drug-resistant epilepsy, with wide-reaching medical and economic implications.
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