Automated analysis of a large-scale paediatric dataset illustrates the interdependent relationship between epilepsy and sleep.

Slow waves are an electrophysiological characteristic of non-rapid eye movement sleep and a marker of the restorative function of sleep. In certain pathological conditions, such as different types of epilepsy, slow-wave sleep is affected by epileptiform discharges forming so-called "spike-waves". Previous evidence shows that the overnight change in slope of slow waves during sleep is impaired under these conditions. However, these past studies were performed in a small number of patients, considering only short segments of the recording night. Here, we screened a clinical data set of 39'179 pediatric EEG recordings acquired in the past 25 years (1994-2019) at the University Children's Hospital Zurich and identified 413 recordings of interest. We applied an automated approach based on machine learning to investigate the relationship between sleep and epileptic spikes in this large-scale data set. Our findings show that the overnight change in the slope of slow waves was correlated with the spike-wave index, indicating that the impairment of the net reduction in synaptic strength during sleep is spike dependent.

'High-Density-SleepCleaner': An open-source, semi-automatic artifact removal routine tailored to high-density sleep EEG.

BACKGROUND: With up to 256 channels, high-density electroencephalography (hd-EEG) has become essential to the sleep research field. The vast amount of data resulting from this magnitude of channels in overnight EEG recordings complicates the removal of artifacts. NEW METHOD: We present a new, semi-automatic artifact removal routine specifically designed for sleep hd-EEG recordings. By employing a graphical user interface (GUI), the user assesses epochs in regard to four sleep quality markers (SQMs). Based on their topography and underlying EEG signal, the user eventually removes artifactual values. To identify artifacts, the user is required to have basic knowledge of the typical (patho-)physiological EEG they are interested in, as well as artifactual EEG. The final output consists of a binary matrix (channels x epochs). Channels affected by artifacts can be restored in afflicted epochs using epoch-wise interpolation, a function included in the online repository. RESULTS: The routine was applied in 54 overnight sleep hd-EEG recordings. The proportion of bad epochs highly depends on the number of channels required to be artifact-free. Between 95% and 100% of bad epochs could be restored using epoch-wise interpolation. We furthermore present a detailed examination of two extreme cases (with few and many artifacts). For both nights, the topography and cyclic pattern of delta power look as expected after artifact removal. COMPARISON WITH EXISTING METHODS: Numerous artifact removal methods exist, yet their scope of application usually targets short wake EEG recordings. The proposed routine provides a transparent, practical, and efficient approach to identify artifacts in overnight sleep hd-EEG recordings. CONCLUSION: This method reliably identifies artifacts simultaneously in all channels and epochs.

Boosting Recovery During Sleep by Means of Auditory Stimulation.

Sufficient recovery during sleep is the basis of physical and psychological well-being. Understanding the physiological mechanisms underlying this restorative function is essential for developing novel approaches to promote recovery during sleep. Phase-targeted auditory stimulation (PTAS) is an increasingly popular technique for boosting the key electrophysiological marker of recovery during sleep, slow-wave activity (SWA, 1-4 Hz EEG power). However, it is unknown whether PTAS induces physiological sleep. In this study, we demonstrate that, when applied during deep sleep, PTAS accelerates SWA decline across the night which is associated with an overnight improvement in attentional performance. Thus, we provide evidence that PTAS enhances physiological sleep and demonstrate under which conditions this occurs most efficiently. These findings will be important for future translation into clinical populations suffering from insufficient recovery during sleep.

Neuromodulation by means of phase-locked auditory stimulation affects key marker of excitability and connectivity during sleep.

The propagating pattern of sleep slow waves (high-amplitude oscillations < 4.5 Hz) serves as a blueprint of cortical excitability and brain connectivity. Phase-locked auditory stimulation is a promising tool for the modulation of ongoing brain activity during sleep; however, its underlying mechanisms remain unknown. Here, eighteen healthy young adults were measured with high-density electroencephalography in three experimental conditions; one with no stimulation, one with up- and one with down-phase stimulation; ten participants were included in the analysis. We show that up-phase auditory stimulation on a right prefrontal area locally enhances cortical involvement and promotes traveling by increasing the propagating distance and duration of targeted small-amplitude waves. On the contrary, down-phase stimulation proves more efficient at perturbing large-amplitude waves and interferes with ongoing traveling by disengaging cortical regions and interrupting high synchronicity in the target area as indicated by increased traveling speed. These results point out different underlying mechanisms mediating the effects of up- and down-phase stimulation and highlight the strength of traveling wave analysis as a sensitive and informative method for the study of connectivity and cortical excitability alterations.

Closed-loop auditory stimulation method to modulate sleep slow waves and motor learning performance in rats.

Slow waves and cognitive output have been modulated in humans by phase-targeted auditory stimulation. However, to advance its technical development and further our understanding, implementation of the method in animal models is indispensable. Here, we report the successful employment of slow waves' phase-targeted closed-loop auditory stimulation (CLAS) in rats. To validate this new tool both conceptually and functionally, we tested the effects of up- and down-phase CLAS on proportions and spectral characteristics of sleep, and on learning performance in the single-pellet reaching task, respectively. Without affecting 24 hr sleep-wake behavior, CLAS specifically altered delta (slow waves) and sigma (sleep spindles) power persistently over chronic periods of stimulation. While up-phase CLAS does not elicit a significant change in behavioral performance, down-phase CLAS exerted a detrimental effect on overall engagement and success rate in the behavioral test. Overall CLAS-dependent spectral changes were positively correlated with learning performance. Altogether, our results provide proof-of-principle evidence that phase-targeted CLAS of slow waves in rodents is efficient, safe, and stable over chronic experimental periods, enabling the use of this high-specificity tool for basic and preclinical translational sleep research.

A Protocol for Comparing Dry and Wet EEG Electrodes During Sleep.

BACKGROUND: Sleep is commonly assessed by recording the electroencephalogram (EEG) of the sleeping brain. As sleep assessments in a lab environment are cumbersome for both the participant and researcher, it would be highly desirable to record sleep EEG with a user-friendly and mobile device. Dry electrodes that are reusable, low-cost, and easy to apply would be an essential component of such a device. In this study, we developed a testing protocol to investigate the performance of novel flat-type dry electrodes for sleep EEG recordings in free-living conditions. METHODS: Overnight sleep EEG, electrooculogram and electromyogram of four young and healthy participants were recorded at home. Two identical ambulatory recording devices, one using novel flat-type dry electrodes, the other using self-adhesive pre-gelled electrodes, simultaneously recorded sleep EEG. Between both electrode types, we then compared the signal quality, the incidence of artifacts, the sensitivity, specificity and inter-scoring reliability (Cohen's kappa) of sleep staging, as well as the agreement of important characteristics of sleep-specific EEG microstructure features, such as slow waves (0.5-4 Hz) and sleep spindles (10-16 Hz). RESULTS: Our testing protocol comprehensively compared the two electrode types on a macro- and microstructure level of sleep. The dry and pre-gelled electrodes both had comparable signal quality and sleep staging was feasible with both electrodes. Also, slow-wave and spindle characteristics were similar. However, sweat artifacts were more prevalent in the flat-type dry electrodes. CONCLUSION: With a reliable testing protocol, the performance of dry electrodes can be compared to reference technologies and objectively assessed also in free-living conditions.