Preregistration of a study examining the influence of auditory closed-loop targeted memory reactivation during sleep on the neural correlates of motor memory consolidation.
Bibliographic record
Abstract
Motor memory consolidation is the process by which labile motor memory traces become more robust. This process has been extensively studied using motor sequence learning (MSL) tasks that involve integrating the temporal structure of a series of stereotyped movements into a unitary, well-rehearsed sequence. Both the behavioral and neural correlates of motor sequence memory consolidation have been extensively characterized in young adults. Briefly, initial acquisition of a movement sequence, occurring during online practice of the task and characterized by a substantial improvement in performance, is followed by a slower phase in which smaller improvements emerge across multiple practice sessions. Importantly, the consolidation phase, occurring offline, between practice sessions, offers a privileged time window for the acquired memory to be transformed into a more stable, robust memory trace. Post-learning sleep, and specifically non-rapid eye movement sleep (NREM) (Geneviève Albouy et al., 2008) (Geneviève Albouy, Fogel, et al., 2013), is known to further enhance this consolidation process. The sleep-related motor memory consolidation process has been shown to be augmented by experimental interventions referred to as targeted memory reactivation (TMR). TMR consists of replaying offline (i.e., during the consolidation period) sensory stimuli that were associated to the task during learning (Ngo et al., 2013). Auditory TMR applied during post-learning Slow Wave Sleep (SWS) has been consistently used to boost motor memory consolidation in healthy young adults (Cousins et al., 2016) (Schönauer et al., 2014). Furthermore, reinforcing the synchronization of cerebral oscillations during sleep using auditory stimuli (Ngo et al., 2015) specifically presented during SW up-states (optimal period of neuronal excitability) has been shown to optimize declarative memory consolidation ( Ngo et al., 2013) (Batterink et al., 2016) (Göldi et al., 2019). However, studies causally linking the specific phase of the SW to enhancement or impairment of memory consolidation processes are rather sparse in the declarative memory field and non-existent in the motor memory domain. Additionally, the neurophysiological processes supporting these effects are poorly understood. In this project, we will combine TMR and Closed-Loop (CL) stimulation of specific phases of SWs in order to modulate motor memory consolidation. The algorithm we will use allows precise time-locking of the stimulation to the trough and the peak of the SW. The aim of the present study is twofold. First, our design allows to causally link the different phases of the SW to consolidation processes and, second, to investigate the influence of CL-TMR on the neural correlates of motor memory using sleep electro-encephalography (EEG) recordings and functional magnetic resonance imaging (fMRI) during task performance. To do so, participants will be trained, in the fMRI scanner on a motor sequence learning (MSL) task involving 3 different sequences. Each of these sequences will be associated to one specific sound during learning. During the subsequent post-learning sleep monitored with EEG, one sound will be played at the peak of the SW (reactivated-up condition), another sound will be played at the trough of the SW (reactivated-down condition) while the last sound will not be replayed (control condition). To assess consolidation, motor task performance will be retested, in the fMRI scanner, after the night episode (post-night retest).
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".