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-h 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.EEG/EMG signal tanks: we acquired data using a Multichannel Neurophysiology Recording System (Tucker Davis Technologies, TDT, USA). We sampled all EEG/EMG signal at 610.35 Hz, amplified (PZ5 NeuroDigitizer preamplifier, TDT, USA) after applying an anti-aliasing low-pass filter (45% of sampling frequency), synchronously digitized (RZ2 BIOAMP processor, TDT, USA), recorded using SYNAPSE software (TDT, USA) . We filtered real‑time EEG between 0.1 – 36.0 Hz (2nd order biquad filter, TDT, USA), and EMG between 5.0 – 525.0 Hz (2nd order biquad filter and 40-dB notch filter centered at 50 Hz, TDT, USA).After converting all signal blocks into .edf format, we scored all recording files using the online computational tool SPINDLE (Sleep phase identification with neural networks for domain-invariant learning) (Miladinović et al., 2019) for animal sleep data. In short, each .edf file, consisting of two parietal EEG and one nuchal EMG channels, was uploaded to SPINDLE to retrieved vigilance states with 4-second epoch resolution. The algorithm classified three vigilance states: wakefulness (w), NREM sleep (n), and REM sleep (r). Additionally, unclear epochs or interfering signals were labeled as artifacts in wakefulness (1), NREM (2), and REM sleep (3). SINGLE PELLET REACHING TASK: individual counts included in this paper were registered in testing sessions of 1h, divided in bins of 12 minutes, for the last 5 days of the single-pellet reaching task protocol (P-T8 to M-T4), as well as LTM day. We defined success rate (SR) and fail rate (FR) as the number of successes or fails (excluding drop-ins) out of 100 possible attempts. We calculated the intra-session change as the log2 of the fold-change of the number of successes of the last bin to the first bin of a session (-1 represents a 50% drop in successes, while 1 represents twice more successes from first bin)