DOI: 10.5061/dryad.bvq83bk99
title: Closed-loop auditory stimulation method to modulate sleep slow waves and motor learning performance in rats

description: 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.
description: 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 (2<sup>nd</sup>
 order biquad filter, TDT, USA), and EMG between 5.0 – 525.0 Hz
 (2<sup>nd</sup> 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
 log<sub>2</sub> 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).