Management and outcome of vagus nerve stimulator implantation: experience of an otolaryngeal/neuropediatric cooperation.

OBJECTIVE: Vagus nerve stimulator (VNS) implantation is an established therapy for pharmacoresistant epilepsy that is not amenable to curative epilepsy surgery. Historically, VNS implantation has been performed by neurosurgeons, but otolaryngologist involvement is increasingly common. In this retrospective study, we aimed to evaluate the efficacy and safety of VNS implantation in children and adolescents from the otolaryngologists' perspective. METHODS: This study included children and adolescents who had undergone VNS implantation at the study center between 2014 and 2018. Patient files were analyzed with regards to the durations of device implantation and hospitalization, postoperative complications, and clinical outcome, including seizure frequency, clinical global impression of improvement (CGI-I) score, and quality of life (QoL). RESULTS: A total of 73 children underwent VNS surgery. The median age at implantation was 9.3 ± 4.6 years, and median epilepsy duration before VNS surgery was 6 ± 4 years. Lennox-Gastaut syndrome was the most common syndrome diagnosis (62.3%), and structural abnormalities (49.3%) the most frequent etiology. Operation times ranged from 30 to 200 min, and median postoperative hospitalization length was 2 ± 0.9 days. No complications occurred, except for four revisions and two explantations due to local infections (2.7%). Among our patients, 76.7% were responders (≥ 50% reduction in seizure frequency), 72.1% showed improved CGI-I scores, and 18.6-60.5% exhibited considerable improvements in the QoL categories energy, emotional health, and cognitive functions. CONCLUSION: Our results indicate that VNS implantation is a highly effective and safe treatment option for children and adolescents with AED-refractory epilepsies who are not candidates for curative epilepsy surgery.

Volitional modification of brain activity in adolescents with Autism Spectrum Disorder: A Bayesian analysis of Slow Cortical Potential neurofeedback.

Autism spectrum disorder is (ASD) characterized by a persisting triad of impairments of social interaction, language as well as inflexible, stereotyped and ritualistic behaviors. Increasingly, scientific evidence suggests a neurobiological basis of these emotional, social and cognitive deficits in individuals with ASD. The aim of this randomized controlled brain self-regulation intervention study was to investigate whether the core symptomatology of ASD could be reduced via an electroencephalography (EEG) based brain self-regulation training of Slow Cortical Potentials (SCP). 41 male adolescents with ASD were recruited and allocated to a) an experimental group undergoing 24 sessions of EEG-based brain training (n1 = 21), or to b) an active control group undergoing conventional treatment (n2 = 20), that is, clinical counseling during a 3-months intervention period. We employed real-time neurofeedback training recorded from a fronto-central electrode intended to enable participants to volitionally regulate their brain activity. Core autistic symptomatology was measured at six time points during the intervention and analyzed with Bayesian multilevel approach to characterize changes in core symptomatology. Additional Bayesian models were formulated to describe the neural dynamics of the training process as indexed by SCP (time-domain) and power density (PSD, frequency-domain) measures. The analysis revealed a substantial improvement in the core symptomatology of ASD in the experimental group (reduction of 21.38 points on the Social Responsiveness Scale, SD = 5.29), which was slightly superior to that observed in the control group (evidence Ratio = 5.79). Changes in SCP manifested themselves as different trajectories depending on the different feedback conditions and tasks. Further, the model of PSD revealed a continuous decrease in delta power, parallel to an increase in alpha power. Most notably, a non-linear (quadratic) model turned out to be better at predicting the data than a linear model across all analyses. Taken together, our analyses suggest that behavioral and neural processes of change related to neurofeedback training are complex and non-linear. Moreover, they have implications for the design of future trials and training protocols.