J 2019

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

BRÁZDIL, Milan, Irena DOLEŽALOVÁ, Eva KORIŤÁKOVÁ, Jan CHLÁDEK, Robert ROMAN et. al.

Základní údaje

Originální název

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

Autoři

BRÁZDIL, Milan (203 Česká republika, garant, domácí), Irena DOLEŽALOVÁ (203 Česká republika, domácí), Eva KORIŤÁKOVÁ (203 Česká republika, domácí), Jan CHLÁDEK (203 Česká republika, domácí), Robert ROMAN (203 Česká republika, domácí), Martin PAIL (203 Česká republika, domácí), Pavel JURAK (203 Česká republika), Daniel Joel SHAW (826 Velká Británie a Severní Irsko, domácí) a Jan CHRASTINA (203 Česká republika, domácí)

Vydání

FRONTIERS IN NEUROLOGY, LAUSANNE, FRONTIERS MEDIA SA, 2019, 1664-2295

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

30210 Clinical neurology

Stát vydavatele

Švýcarsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

Impakt faktor

Impact factor: 2.889

Kód RIV

RIV/00216224:14110/19:00108497

Organizační jednotka

Lékařská fakulta

UT WoS

000466518800001

Klíčová slova anglicky

vagal nerve stimulation; neurostimulation; epilepsy; efficacy prediction; EEG reactivity; epilepsy treatment

Příznaky

Mezinárodní význam, Recenzováno
Změněno: 11. 5. 2020 13:04, Mgr. Tereza Miškechová

Anotace

V originále

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.

Návaznosti

LQ1601, projekt VaV
Název: CEITEC 2020 (Akronym: CEITEC2020)
Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, CEITEC 2020
NV19-04-00343, projekt VaV
Název: Predikce Efektu Stimulace u pacientů s Epilepsií (PRESEnCE) (Akronym: PRESEnCE)
Investor: Ministerstvo zdravotnictví ČR, Prediction of Stimulation Efficacy in Epilepsy