High Frequency Oscillations (HFOs) in the brain have been associated with different physiological and pathological processes. In epilepsy, HFOs might reflect a mechanism of epileptic phenomena, serving as a biomarker of epileptogenesis and epileptogenicity. Despite the valuable information provided by HFOs, their correct identification is a challenging task. A comprehensive application, RIPPLELAB, was developed to facilitate the analysis of HFOs. RIPPLELAB provides a wide range of tools for HFOs manual and automatic detection and visual validation; all of them are accessible from an intuitive graphical user interface. Four methods for automated detection—as well as several options for visualization and validation of detected events—were implemented and integrated in the application. Analysis of multiple files and channels is possible, and new options can be added by users. All features and capabilities implemented in RIPPLELAB for automatic detection were tested through the analysis of simulated signals and intracranial EEG recordings from epileptic patients (n = 16; 3,471 analyzed hours). Visual validation was also tested, and detected events were classified into different categories. Unlike other available software packages for EEG analysis, RIPPLELAB uniquely provides the appropriate graphical and algorithmic environment for HFOs detection (visual and automatic) and validation, in such a way that the power of elaborated detection methods are available to a wide range of users (experts and non-experts) through the use of this application. We believe that this open-source tool will facilitate and promote the collaboration between clinical and research centers working on the HFOs field. The tool is available under public license and is accessible through a dedicated web site.

Study Objectives Closed-loop auditory stimulation (CLAS) is a method for enhancing slow oscillations (SOs) through the presentation of auditory clicks during sleep. CLAS boosts SOs amplitude and sleep spindle power, but the optimal timing for click delivery remains unclear. Here, we determine the optimal time to present auditory clicks to maximize the enhancement of SO amplitude and spindle likelihood. Methods We examined the main factors predicting SO amplitude and sleep spindles in a dataset of 21 young and 17 older subjects. The participants received CLAS during slow-wave-sleep in two experimental conditions: sham and auditory stimulation. Post-stimulus SOs and spindles were evaluated according to the click phase on the SOs and compared between and within conditions. Results We revealed that auditory clicks applied anywhere on the positive portion of the SO increased SO amplitudes and spindle likelihood, although the interval of opportunity was shorter in the older group. For both groups, analyses showed that the optimal timing for click delivery is close to the SO peak phase. Click phase on the SO wave was the main factor determining the impact of auditory stimulation on spindle likelihood for young subjects, whereas for older participants, the temporal lag since the last spindle was a better predictor of spindle likelihood. Conclusions Our data suggest that CLAS can more effectively boost SOs during specific phase windows, and these differ between young and older participants. It is possible that this is due to the fluctuation of sensory inputs modulated by the thalamocortical networks during the SO.

Seizure detection is a routine process in epilepsy units requiring manual intervention of well-trained specialists. This process could be extensive, inefficient and time-consuming, especially for long term recordings. We proposed an automatic method to detect epileptic seizures using an imaged-EEG representation of brain signals. To accomplish this, we analyzed EEG signals from two different datasets: the CHB-MIT Scalp EEG database and the EPILEPSIAE project that includes scalp and intracranial recordings. We used fully convolutional neural networks to automatically detect seizures. For our best model, we reached average accuracy and specificity values of 99.3% and 99.6%, respectively, for the CHB-MIT dataset, and corresponding values of 98.0% and 98.3% for the EPILEPSIAE patients. For these patients, the inclusion of intracranial electrodes together with scalp ones increased the average accuracy and specificity values to 99.6% and 58.3%, respectively. Regarding the other metrics, our best model reached average precision of 62.7%, recall of 58.3%, F-measure of 59.0% and AP of 54.5% on the CHB-MIT recordings, and comparatively lowers performances for the EPILEPSIAE dataset. For both databases, the number of false alarms per hour reached values less than 0.5/h for 92% of the CHB-MIT patients and less than 1.0/h for 80% of the EPILEPSIAE patients. Compared to recent studies, our lightweight approach does not need any estimation of pre-selected features and demonstrates high performances with promising possibilities for the introduction of such automatic methods in the clinical practice.

Highlights A single night of TMR benefits procedural memories up to 10 days later. Spindle density and SO-spindle coupling strength increase immediately upon cue onset. Time spent in N2 but not N3 predicts cueing benefit.

Memory reactivation during Non-Rapid Eye Movement (NREM) sleep is important for memory consolidation but it remains unclear exactly how such activity promotes the development of a stable memory representation. We used Targeted Memory Reactivation (TMR) in combination with longitudinal structural and functional MRI to track the evolution of a motor memory trace over 20 days. We show that repeated reactivation of motor memory during sleep leads to increased precuneus activation 24 h post-TMR. Interestingly, a decrease in precuneus activity over the next 10 days predicts longer-term cueing benefit. We also find both functional and structural changes in sensorimotor cortex in association with effects of TMR 20 days post-encoding. These findings demonstrate that TMR can engage precuneus in the short-term while also impacting on task-related structure and function over longer timescales.

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Memory consolidation during slow-wave sleep is supported by slow oscillations (SOs), spindles, and hippocampal ripples. Recent evidence in both rodents and humans has demonstrated that consolidation is mediated by a bidirectional hippocampal-cortical loop. Here, we discuss oscillatory mechanisms by which the interaction of these non-REM oscillations may provide an appropriate neural framework for both the TOP-DOWN and the BOTTOM-UP processes in this loop. We also discuss how non-REM oscillations promote cortical plasticity for new memories, while simultaneously downregulating the representations of information in hippocampal networks. Finally, we point out that not all individual instances of non-REM oscillations play a role in the consolidation process. Instead, the capacity of these rhythms to support memory is determined by a triple SOspindle-ripple coupling provided by thalamocortical dynamics. Importantly, large, spatially synchronised SOs promote thalamic downstates, and spindles, boosting the probability of this triple coupling.

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