Benefits in long-term memory retention and generalization have been shown to be related to sleep-dependent processes, which correlate with neural oscillations as measured by changes in electric potential. The specificity and causal role of these oscillations, however, are still poorly understood. Here, we investigated the potential for augmenting endogenous Slow-Wave (SW) oscillations in humans with closed-loop transcranial alternating current stimulation (tACS) with an aim towards enhancing the consolidation of recent experiences into long-term memory. Sixteen (three female) participants were trained pre-sleep on a Target Detection task identifying targets hidden in complex visual scenes. During post-training sleep closed-loop SW detection and stimulation was used to deliver tACS matching the phase and frequency of the dominant oscillation in the range of 0.5 to 1.2 Hz. Changes in performance were assessed the following day using test images that were identical to the training (‘Repeated9), and images generated from training scenes but with novel viewpoints (‘Generalized9). Results showed that active SW tACS during sleep enhanced the post vs. pre-sleep target detection accuracy for the Generalized images compared to sham nights, while no significant change was found for Repeated images. Using a frequency agnostic clustering approach sensitive to stimulation-induced spectral power changes in scalp EEG, this behavioral enhancement significantly correlated with both a post-stimulation increase and subsequent decrease in measured spectral power within the SW band, which in turn showed increased coupling with spindle amplitude. These results suggest that augmenting endogenous SW oscillations can enhance consolidation by specifically improving generalization over recognition or cued recall. SIGNIFICANCE STATEMENT This human study demonstrates the use of a closed-loop noninvasive brain stimulation method to enhance endogenous neural oscillations during sleep with the effect of improving consolidation of recent experiences into long-term memory. Here we show that transient slow oscillatory transcranial Alternating Current Stimulation (tACS) triggered by endogenous slow oscillations and matching their frequency and phase can increase slow-wave power and coupling with spindles. Further this increase correlates with over night improvements in generalization of recent training to facilitate performance in a target detection task. We also provide novel evidence for a tACS induced refractory period following the tACS induced increase. Here slow-wave power is temporarily reduced relative to sham stimulation, which nonetheless maintains a positive relationship with behavioral improvements.

Background: Poor sleep quality is a common complaint, affecting over one third of people in the United States. While sleep quality is thought to be related to slow-wave sleep (SWS), there has been little investigation to address whether modulating slow-wave oscillations (SWOs) that characterize SWS could impact sleep quality. Here we examined whether closed-loop transcranial alternating current stimulation (CL-tACS) applied during sleep impacts sleep quality and efficiency. Methods: CL-tACS was used in 21 participants delivered at the same frequency and in phase with endogenous SWOs during sleep. Sleep quality was assessed in the morning following either verum or sham control stimulation during sleep, with order counterbalanced within participants. Results: Higher sleep quality and efficiency were found after verum stimulation nights compared to control. The largest effects on sleep quality were found immediately following an adaptation night in the laboratory for which sleep quality was reduced. Conclusions: Applying CL-tACS at the same frequency and phase as endogenous SWOs may offer a novel method to improve subjective sleep quality after a night with poor quality sleep. CL-tACS might be helpful for increasing sleep quality and efficiency in otherwise healthy people, and in patients with clinical disorders that involve sleep deficits.

Impaired P50 gating is thought to reflect a core deficit in schizophrenia, but the relevant neural network is not well understood. The present study used EEG and MEG to assess sensory gating and volumetric MRI to measure hippocampal volume to investigate relationships between them in 22 normal controls and 22 patients with schizophrenia. In the schizophrenia group, anterior but not posterior hippocampal volume was smaller, and both the P50 and M50 gating ratios were larger (worse) than in controls. Independent of group, left‐hemisphere M50 gating ratio correlated negatively with left anterior hippocampal volume, and right‐hemisphere M50 gating ratio correlated negatively with right anterior hippocampal volume. Schizophrenia diagnosis predicted M50 gating independent of hippocampal volume. These results are consistent with the finding that hippocampus is a critical part of a fronto‐temporal circuit involved in auditory gating.

Mental state monitoring is a critical component of current and future human-machine interfaces, including semi-autonomous driving and flying, air traffic control, decision aids, training systems, and will soon be integrated into ubiquitous products like cell phones and laptops. Current mental state assessment approaches supply quantitative measures, but their only frame of reference is generic population-level ranges. What is needed are physiological biometrics that are validated in the context of task performance of individuals. Using curated intake experiments, we are able to generate personalized models of three key biometrics as useful indicators of mental state; namely, mental fatigue, stress, and attention. We demonstrate improvements to existing approaches through the introduction of new features. Furthermore, addressing the current limitations in assessing the efficacy of biometrics for individual subjects, we propose and employ a multi-level validation scheme for the biometric models by means of k-fold cross-validation for discrete classification and regression testing for continuous prediction. The paper not only provides a unified pipeline for extracting a comprehensive mental state evaluation from a parsimonious set of sensors (only EEG and ECG), but also demonstrates the use of validation techniques in the absence of empirical data. Furthermore, as an example of the application of these models to novel situations, we evaluate the significance of correlations of personalized biometrics to the dynamic fluctuations of accuracy and reaction time on an unrelated threat detection task using a permutation test. Our results provide a path toward integrating biometrics into augmented human-machine interfaces in a judicious way that can help to maximize task performance.

Previous studies have found a benefit of closed-loop transcranial alternating current stimulation (CL-tACS) matched to ongoing slow-wave oscillations (SWO) during sleep on memory consolidation for words in a paired associates task (PAT). Here, we examined the effects of CL-tACS in a retroactive interference PAT (ri-PAT) paradigm, where additional stimuli were presented to increase interference and reduce memory performance. Thirty-one participants were tested on a PAT before sleep, and CL-tACS was applied over the right and left DLPFC (F3 and F4) vs. mastoids for five cycles after detection of the onset of each discrete event of SWO during sleep. Participants were awoken the following morning, learned a new PAT list, and then were tested on the original list. There was a significant effect of stimulation condition (p = 0.04297; Cohen’s d = 0.768), where verum stimulation resulted in reduced retroactive interference compared with sham and a significant interaction of encoding strength and stimulation condition (p = 0.03591). Planned simple effects testing within levels of encoding revealed a significant effect of stimulation only for low-encoders (p = 0.0066; Cohen’s d = 1.075) but not high-encoders. We demonstrate here for the first time that CL-tACS during sleep can enhance the protective benefits on retroactive interference in participants who have lower encoding aptitude.