Replacement of traditional vagus nerve stimulation with cardiac-based device and seizure reduction: A systematic review and meta-analysis.

INTRODUCTION: For patients with drug-resistant epilepsy (DRE) who are not suitable for surgical resection, neuromodulation with vagus nerve stimulation (VNS) is an established approach. However, there is limited evidence of seizure reduction when replacing traditional VNS (tVNS) device with a cardiac-based one (cbVNS). This meta-analysis compares the seizure reduction achieved by replacing tVNS with cbVNS in a population with DRE. METHODS: We systematically searched PubMed, Embase, and Cochrane Central following PRISMA guidelines. The main outcomes were number of patients experiencing a ≥ 50 % and ≥80 % reduction in seizures, as defined by the McHugh scale. Additionally, we assessed the number of patients achieving freedom from seizures. RESULTS: We included 178 patients with DRE from 7 studies who were initially treated with tVNS and subsequently had it replaced by cbVNS. The follow-up for cbVNS ranged from 6 to 37.5 months. There was a statistically significant reduction in seizure frequency with the replacement of tVNS by cbVNS, using a ≥ 50 % (OR 1.79; 95 % CI 1.07 to 2.97; I²=0 %; p = 0.03) and a ≥ 80 % (OR 2.06; 95 % CI 1.17 to 3.62; I²=0 %; p = 0.01) reduction threshold. Nineteen (13 %) participants achieved freedom from seizures after switching to cbVNS. There was no difference in the rate of freedom from seizures between groups (OR 1.85; 95 % CI 0.81 to 4.21; I²=0 %; p = 0.14). CONCLUSION: In patients with DRE undergoing battery replacement, cbVNS might be associated with seizure reduction (≥50 % and ≥80 % threshold) after switching from tVNS. Randomised controlled trials are necessary to validate these findings.

Response of sleep slow oscillations to acoustic stimulation is evidenced by distinctive synchronization processes.

Closed-loop acoustic stimulation (CLAS) during sleep has shown to boost slow wave (SW) amplitude and spindle power. Moreover, sleep SW have been classified based on different processes of neuronal synchronization. Thus, different types of SW events may have distinct functional roles and be differentially affected by external stimuli. However, the SW synchronization processes affected by CLAS are not well understood. Here, we studied the effect of CLAS on the dissociation of SW events based on two features of neuronal synchronization in the electroencephalogram (topological spread and wave slope). We evaluated and classified individual SW events of 14 healthy subjects during a CLAS stimulated (STM) and a control night (CNT). Three main categories of SW events were found denoting (C1) steep slope SW with global spread, (C2) flat-slope waves with localized spread and homeostatic decline, and (C3) multipeaked flat-slope events with global spread. Comparing between conditions, we found a consistent increase of event proportion and trough amplitudes for C1 events during the time of stimulation. Furthermore, we found similar increases in post-stimulus spectral power in θ, ß, and σ frequencies for CNT vs STIM condition independently of sleep stage or SW categories. However, topological analysis showed differentiated spatial dynamics in N2 and N3 for SW categories and the co-occurrence with spindle events. Our findings support the existence of multiple types of SW with differential response to external stimuli and possible distinct neuronal mechanisms.

Auditory closed-loop stimulation on sleep slow oscillations using in-ear EEG sensors.

Acoustic stimulation synchronized to slow oscillations in scalp electroencephalograms has been shown to enhance sleep features, which makes it promising in boosting cognitive functions as well as in the treatment of some sleep disturbances. Nevertheless, scalp electrode sensors are resource intensive and poorly tolerated by sleeping patients. The aim of this study was to investigate the potential usability of in-the-ear electroencephalography to implement auditory closed-loop stimulation during sleep. For this, we evaluated the agreement between slow oscillation recordings obtained through the in-ear sensor and those obtained simultaneously from standard scalp electrodes during naps of 13 healthy subjects. We found that in-ear activity provided enough information to automatically detect sleep slow oscillations in real-time. Based on this, we successfully enhanced scalp slow oscillations using auditory single-cycle closed-loop brain-state-dependent stimulation based on in-ear signals acquired in 11 further subjects. We conclude that in-ear sensors provide a feasible technology for the enhancement of sleep patterns, and could pave the way for new clinical applications in the near future.