Consolidation of human skill linked to waking hippocampo-neocortical replay

Abstract

The introduction of rest intervals interspersed with practice strengthens wakeful consolidation of skill. The mechanisms by which the brain binds discrete action representations into consolidated, highly temporally resolved skill sequences during waking rest are not known. To address this question, we recorded magnetoencephalography (MEG) during acquisition and rapid consolidation of a sequential motor skill. We report the presence of prominent, fast waking neural replay during the same rest periods in which rapid consolidation occurs. The observed replay is temporally compressed by approximately 20-fold relative to the acquired skill, is selective for the trained sequence, and predicts the magnitude of skill consolidation. Replay representations extend beyond the hippocampus and entorhinal cortex to the contralateral sensorimotor cortex. These results document the presence of robust hippocampo-neocortical replay supporting rapid wakeful consolidation of skill.

Keywords: MEG; consolidation; entorhinal cortex; generalization; hippocampus; human motor learning; offline learning; replay; sensorimotor cortex; skill learning.

Figure 1.. Skill learning task and behavioral performance

(A) Keypress sequence motor skill task. Subjects acquired a novel motor skill over a single training session. They were instructed to repeatedly type a trained sequence, 41324, with the left non-dominant hand as fast and as accurately as possible. Keypress 4 was performed with the left index finger, keypress 3 with the left middle finger, keypress 2 with the left ring finger, and keypress 1 with the left little finger. Following task instructions, a 5-min MEG waking rest recording was acquired prior to commencement of training (pre-training rest). Subjects then performed the task over 36 individual practice periods lasting 10 s each. Practice periods were interleaved with 10-s waking rest intervals (inter-practice rest). MEG was acquired continuously during training (12 min). A second, 5-min waking rest MEG recording was acquired after training concluded (post-training rest). (B) Performance curve. Skill was measured as the correct sequence typing speed (sequences [seq]/s). Mean average performance (mean ± SEM) increased rapidly during early learning (the set of trials within which 95% of total learning occurred, trials 1–11; vertical dashed line). (C) Instantaneous correct sequence typing speed (group mean ± SEM shown) was used to quantify micro-online performance gains during practice (cyan) and micro-offline gains during interleaved rest (magenta) for the early learning period. (D) Micro-online (cyan), micro-offline (magenta), and total early learning (green) for each subject. Note that skill increases occur during intervening periods of waking rest, and not during active practice. Thick horizontal lines and associated boxes indicate the group mean and 95% confidence interval, respectively.

Figure 2.. Replay detections

(A) Replay detection analysis pipeline. Raw MEG data were pre-processed, source-localized, and parcellated into 216 brain regions using the Brainnetome Atlas (Fan et al., 2016). Practice data segments centered on individual keypress events were used to train four one-versus-all radial basis support vector machine keypress state decoders (i.e., one decoder per finger). We then used the trained keypress state decoders to interrogate resting-state MEG data (preceding training, during rest periods interspersed with practice, and after the end of training) at each time point, T, for replay of keypress state sequences over a range of durations (25–2,500 ms) corresponding to temporal compression ranges (0.4–40×) previously reported (Davidson et al., 2009; Marchesotti et al., 2016; Papaxanthis et al., 2002). Decoder performance evaluation (Figure S1) was used to threshold minimum detectable sequence replay probabilities. Permutation testing against probabilities for all possible sequences (n = 1,024) at time point T was used to determine statistically significant detection of keypress sequence replay events. (B) Neural replay was detected during waking rest. The maximum forward trained sequence replay rate was observed for events of 50-ms duration. This was consistent with observed replay for the reverse trained sequence (Figure S2A). Shaded regions indicate the 95% confidence interval of the group mean (thick lines). (C) Waking replay rates (50-ms duration) for the forward trained sequence more than tripled during inter-practice rest (2.44 ± 0.29, mean ± SEM) relative to pretraining rest (0.75 ± 0.21). Similar magnitude rate changes were also observed for the reverse trained sequence (Figure S2B). Replay rates then receded during post-training rest (0.88 ± 0.21) to levels similar to pre-training. Thick horizontal lines and associated boxes indicate the group mean and 95% confidence interval, respectively. (D) Within individuals, waking replay of the trained sequence increased more prominently (one-way ANOVA_{[forward, reverse, control]}: F_2,87 = 6.42; p = 0.002; forward versus control: p = 0.0062, Bonferroni-corrected; reverse versus control: p = 0.0099, Bonferroni-corrected; see also Figure S2C) for the trained sequence than for a control sequence (33433; right). This control was selected on the basis that it was the only one out of 1,022 possible alternate sequences sharing no common ordinal position or transition structure with the forward (41324) and reverse (42314) trained sequence. Pre-training rest replay rates did not significantly differ between these sequences (F_2,53.94 = 2.431; p 0.098). Of note, this same trend was observed even after expanding these criteria to consider additional sequences sharing either a single common ordinal position or a single common transition with the practice sequence (Figure S2C).

Figure 3.. Spatial features of detected replay events

Principal-component analysis (PCA) was used to characterize networks with covarying power changes in parcellated MEG source-space during group average replay events. Together, PC1 and PC2 explain 86.6% of the total power-related variance during replay. PC1, shown here, explains 67.8% of the total variance alone and is characterized by a positively correlated sensorimotor-entorhinal-hippocampal network. PC2 displays a similar set of regions (Figure S4). Surface maps are thresholded to display the highest loading parcels (see STAR methods).

Figure 4.. Forward and reverse neural replay of the trained sequence predict rapid wakeful skill consolidation

The scatterplots depict inter-individual replay rates over all early learning inter-practice rest periods (abscissa) relative to cumulative micro-offline gains (ordinate) over the same time window (trials 1–11). Shaded regions indicated the 95% confidence interval. (A) A significant correlation was observed for forward replay of the trained sequence (r = 0.451, p = 0.012). A similar finding was observed for reverse replay of the trained sequence (r = 0.521, p = 0.003). Thus, higher rates of replay related to the trained sequence predicted greater micro-offline gains and rapid wakeful skill consolidation in individuals. (B) Conversely, replay during inter-practice rest of the control sequence (33433) did not display a significant correlation with rapid wakeful skill consolidation (r = 0.165, p = 0.38) during early learning.