Somatosensory evoked fields predict response to vagus nerve stimulation.
Adolescent
Afferent Pathways
/ physiopathology
Child
Connectome
/ methods
Drug Resistant Epilepsy
/ physiopathology
Evoked Potentials, Somatosensory
/ physiology
Female
Humans
Magnetoencephalography
/ methods
Male
Median Nerve
/ physiology
Retrospective Studies
Somatosensory Cortex
/ physiopathology
Support Vector Machine
Treatment Outcome
Vagus Nerve Stimulation
/ methods
Connectomics
Evoked potentials
Machine learning
SEF
VNS
Journal
NeuroImage. Clinical
ISSN: 2213-1582
Titre abrégé: Neuroimage Clin
Pays: Netherlands
ID NLM: 101597070
Informations de publication
Date de publication:
2020
2020
Historique:
received:
12
11
2019
revised:
02
02
2020
accepted:
03
02
2020
pubmed:
20
2
2020
medline:
27
3
2021
entrez:
20
2
2020
Statut:
ppublish
Résumé
There is an unmet need to develop robust predictive algorithms to preoperatively identify pediatric epilepsy patients who will respond to vagus nerve stimulation (VNS). Given the similarity in the neural circuitry between vagus and median nerve afferent projections to the primary somatosensory cortex, the current study hypothesized that median nerve somatosensory evoked field(s) (SEFs) could be used to predict seizure response to VNS. Retrospective data from forty-eight pediatric patients who underwent VNS at two different institutions were used in this study. Thirty-six patients ("Discovery Cohort") underwent preoperative electrical median nerve stimulation during magnetoencephalography (MEG) recordings and 12 patients ("Validation Cohort") underwent preoperative pneumatic stimulation during MEG. SEFs and their spatial deviation, waveform amplitude and latency, and event-related connectivity were calculated for all patients. A support vector machine (SVM) classifier was trained on the Discovery Cohort to differentiate responders from non-responders based on these input features and tested on the Validation Cohort by comparing the model-predicted response to VNS to the known response. We found that responders to VNS had significantly more widespread SEF localization and greater functional connectivity within limbic and sensorimotor networks in response to median nerve stimulation. No difference in SEF amplitude or latencies was observed between the two cohorts. The SVM classifier demonstrated 88.9% accuracy (0.93 area under the receiver operator characteristics curve) on cross-validation, which decreased to 67% in the Validation cohort. By leveraging overlapping neural circuitry, we found that median nerve SEF characteristics and functional connectivity could identify responders to VNS.
Identifiants
pubmed: 32070812
pii: S2213-1582(20)30042-5
doi: 10.1016/j.nicl.2020.102205
pmc: PMC7026289
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
102205Subventions
Organisme : CIHR
Pays : Canada
Informations de copyright
Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest None
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