Jourde, H. | Search bioRxiv

bioRxiv2026

Sleep Spindle-Locked Targeted Memory Reactivation Enhances Declarative Memory Consolidation

Study ObjectivesSleep spindles are implicated in memory consolidation. Yet direct evidence linking spindle dynamics to declarative memory outcomes remains limited. We thus tested whether targeted memory reactivation (TMR) time-locked to sleep spindles enhances declarative memory, and whether the temporal organization of stimulated spindles-trains versus isolated events-is selectively associated with distinct memory outcomes. MethodsTwenty-eight healthy young adults learned image locations from two categories (animals, clothing) in a grid, each paired with a distinct auditory cue. During overnight NREM sleep, one cue was replayed time-locked to spindles detected in real-time using a closed-loop system (TMR condition); the other served as the non-reactivated control (No-TMR condition). Category-cue assignment was counterbalanced. Post-sleep recall, recognition accuracy, and movement time were assessed. ResultsRecall accuracy was significantly higher in the TMR than the No-TMR condition (93.96% vs. 90.61%, p = .024), whereas recognition accuracy (p = .139) and movement time (p = .651) did not differ. Stimulation intensity within spindle trains correlated with the TMR effect on recall (Spearman {rho} = .531, p = .004), whereas the proportion of isolated spindle stimulations correlated with the TMR effect on recognition ({rho} = .563, p = .002). Cross-associations were not significant. ConclusionsSpindle-locked TMR enhances recall-based declarative memory retention. The selective association between spindle temporal clustering and memory outcomes suggests that train-embedded and isolated spindles support different aspects of memory consolidation, highlighting spindle temporal context as a functionally relevant dimension of sleep-dependent memory processing.

neuroscience↗

bioRxiv2019

The Dreem Headband as an Alternative to Polysomnography for EEG Signal Acquisition and Sleep Staging

Despite the central role of sleep in our lives and the high prevalence of sleep disorders, sleep is still poorly understood. The development of ambulatory technologies capable of monitoring brain activity during sleep longitudinally is critical to advancing sleep science and facilitating the diagnosis of sleep disorders. We introduced the Dreem headband (DH) as an affordable, comfortable, and user-friendly alternative to polysomnography (PSG). The purpose of this study was to assess the signal acquisition of the DH and the performance of its embedded automatic sleep staging algorithms compared to the gold-standard clinical PSG scored by 5 sleep experts. Thirty-one subjects completed an over-night sleep study at a sleep center while wearing both a PSG and the DH simultaneously. We assessed 1) the EEG signal quality between the DH and the PSG, 2) the heart rate, breathing frequency, and respiration rate variability (RRV) agreement between the DH and the PSG, and 3) the performance of the DHs automatic sleep staging according to AASM guidelines vs. PSG sleep experts manual scoring. Results demonstrate a strong correlation between the EEG signals acquired by the DH and those from the PSG, and the signals acquired by the DH enable monitoring of alpha (r= 0.71 {+/-} 0.13), beta (r= 0.71 {+/-} 0.18), delta (r = 0.76 {+/-} 0.14), and theta (r = 0.61 {+/-} 0.12) frequencies during sleep. The mean absolute error for heart rate, breathing frequency and RRV was 1.2 {+/-} 0.5 bpm, 0.3 {+/-} 0.2 cpm and 3.2 {+/-} 0.6 %, respectively. Automatic Sleep Staging reached an overall accuracy of 83.5 {+/-} 6.4% (F1 score : 83.8 {+/-} 6.3) for the DH to be compared with an average of 86.4 {+/-} 8.0% (F1 score: 86.3 {+/-} 7.4) for the five sleep experts. These results demonstrate the capacity of the DH to both precisely monitor sleep-related physiological signals and process them accurately into sleep stages. This device paves the way for high-quality, large-scale, longitudinal sleep studies.

neuroscience↗