Temporal cluster-based organisation of sleep spindles underlies motor memory consolidation
Data files
Jan 02, 2024 version files 21.27 KB
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Dataset_SpindleClustering.xlsx
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README.md
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Abstract
Sleep benefits motor memory consolidation, which is mediated by sleep spindle activity and associated memory reactivations during non-rapid eye movement (NREM) sleep. However, the particular role of NREM2 and NREM3 sleep spindles and the mechanisms triggering this memory consolidation process remain unclear. Here, simultaneous electroencephalographic and functional magnetic resonance imaging (EEG-fMRI) recordings were collected during night-time sleep following the learning of a motor sequence task. Adopting a time-based clustering approach, we provide evidence that spindles iteratively occur within clustered and temporally organised patterns during both NREM2 and NREM3 sleep. However, the clustering of spindles in trains is related to motor memory consolidation during NREM2 sleep only. Altogether, our findings suggest that spindles’ clustering and rhythmic occurrence during NREM2 sleep may serve as an intrinsic rhythmic sleep mechanism for the timed reactivation and subsequent consolidation of motor memories, through synchronised oscillatory activity within a subcortical-cortical network involved during learning.
README
Dataset related to the article Temporal cluster-based organisation of sleep spindles underlies motor memory consolidation
## Description of the experimental context and task
In this study, participants were required to practice a motor sequence learning (MSL) task during training (14 blocks) and two retention sessions (1 block each) while lying supine in the MRI scanner. The MSL task required participants to perform an explicitly known 5-element finger movement sequence as rapidly and accurately as possible using a response pad. Each practice block was composed of twelve repetitions of the motor sequence (i.e., 60 key presses per block). Participants underwent the training session about 30 minutes before going to sleep. They were then administered two delayed retention tests, performed 15 minutes (test) and 24 hours (retest) after the end of training. Each of the two retention tests consisted of only one practice block to minimise the effects of additional practice on the MSL task.
## Description of the data and file structure
Dataset includes the changes in motor sequence performance from the delayed 15min test to the 24hr retest (in %), as a behavioural index of motor memory consolidation.
The NREM2 tab contains the information of all sleep spindle characteristics extracted at the Pz derivation during NREM2 sleep, including:
- LengthTrain_N2: length of spindle trains (mean number of spindles per train)
- ISI_N2: inter-spindle interval (in second)
- ITI_N2: inter-train interval (in second)
- GlobalDensity_N2: global density (number of spindles per minute)
- LocalDensity_N2: local density (number of spindles within a spindle-centred sliding window of 60 seconds)
- TotalSpindle_N2: total number of spindles
- Ratio_N2: proportion of grouped over isolated spindles (in percentage)
The NREM3 tab contains the information of all sleep spindle characteristics extracted at the Pz derivation during NREM3 sleep, including similar metrics as detailed above.
The iCoherence_N2 tab contains the information related to functional connectivity analysis between specific subcortical regions, by estimating the imaginary part of coherency: Putamen-Hippocampus coherence, Putamen-Thalamus coherence, and Hippocampus-Thalamus coherence (refers to the Figure 4 in the main manuscript).
The sleep stages tab contains all the information related to time of sleep in the different stages, including:
- WAKE
- REM: Rapid Eye Movement
- NREM1: Non-Rapid Eye Movement stage1
- NREM2: Non-Rapid Eye Movement stage2
- NREM3: Non-Rapid Eye Movement stage3
- Total Sleep Time
## Code/Software
Sleep EEG data were processed using the MATLAB R2019b software from The MathWorks (Natick, MA) and the open-source Brainstorm and EEGLAB softwares. The codes for the detection and clustering of sleep spindles are available at the following GitHub repositories: https://github.com/labdoyon/spindlesDetection and https://github.com/arnaudboutin/Spindle-clustering
Methods
Sleep EEG data were processed using the MATLAB R2019b software from The MathWorks (Natick, MA) and the open-source Brainstorm and EEGLAB softwares. The codes for the detection and clustering of sleep spindles are available at the following GitHub repositories: https://github.com/labdoyon/spindlesDetection and https://github.com/arnaudboutin/Spindle-clustering