The brain strengthens memories through consolidation, defined as resistance to interference (stabilization) or performance improvements between the end of a practice session and the beginning of the next (offline gains) []. Typically, consolidation has been measured hours or days after the completion of training [], but the same concept may apply to periods of rest that occur interspersed in a series of practice bouts within the same session. Here, we took an unprecedented close look at the within-seconds time course of early human procedural learning over alternating short periods of practice and rest that constitute a typical online training session. We found that performance did not markedly change over short periods of practice. On the other hand, performance improvements in between practice periods, when subjects were at rest, were significant and accounted for early procedural learning. These offline improvements were more prominent in early training trials when the learning curve was steep and no performance decrements during preceding practice periods were present. At the neural level, simultaneous magnetoencephalographic recordings showed an anatomically defined signature of this phenomenon. Beta-band brain oscillatory activity in a predominantly contralateral frontoparietal network predicted rest-period performance improvements. Consistent with its role in sensorimotor engagement [], modulation of beta activity may reflect replay of task processes during rest periods. We report a rapid form of offline consolidation that substantially contributes to early skill learning and may extend the concept of consolidation to a time scale in the order of seconds, rather than the hours or days traditionally accepted.

The beta rhythm emerges as transient high-powered events instead of as a sustained signal []. Functionally relevant differences in time-averaged power can reflect changes in event characteristics like number, amplitude, or duration. We investigated the predictive value of beta event characteristics on micro-offline learning. Beta event characteristics (number, maximum amplitude, duration) all inversely predicted micro-offline learning similarly to trial average beta power (LME, n = 10 trials × 27 participants, p < 0.05, Figure S4 ). Neither theta, alpha, or gamma rhythms during rest nor any rhythm during practice periods predicted micro-offline learning ( Figures S3 C and S3E). No brain oscillatory activity during practice or rest periods predicted micro-online learning ( Figures S3 B and S3D), learning over all day-1 trials, or overnight improvement from the end of training on day 1 to test on day 2.

In order to test whether beta rhythm amplitude predicted micro-offline gains during specific segments of the 10-s-long rest period, we estimated the same model for 5 consecutive 2-s-long segments of the rest period. The inverse prediction was stable across the entire rest period ( Figure 4 B). Throughout early learning, the beta rhythm power during rest periods was lower than it was during resting-state baseline (−0.1 ± 0.02, mean ± SEM, two-tailed one-sample t test, T = −4.6, p = 0.001, n = 10 trials).

This inverse relationship was confirmed by modeling micro-offline learning within participant (mean ± SEM, model coefficient, −1.23 ± 2.41, n = 27 model coefficients, T = −2.7, p = 0.01, two-tailed one-sample t test) and within trial (−0.90 ± 0.89, n = 10 model coefficients, T = −3.2, p = 0.01). The correlation between micro-offline learning and beta power during rest periods was not driven by performance improvements during early learning. First, including trial-by-trial performance in the predictive model as an additional factor (LME model with micro-offline learning as the dependent variable, beta power during rest periods and performance as fixed effects, participants as random effect, n = 10 trials × 27 participants) did not improve the model fit (likelihood ratio test, p = 0.55). Second, the linear partial correlation coefficient between micro-offline learning and beta power during rest periods was virtually identical with and without partialling out performance (linear partial correlation coefficient, ρ = −0.25, n = 10 trials × 27 participants, p = 3.8 × 10; linear correlation coefficient, ρ = −0.26, n = 10 trials – 27 participants, p = 2.4 × 10, respectively). Also, exclusion of the first second of the rest period (i.e., the beta rhythm amplitude rebound [], which may include beta amplitude increase as a physiologic stop signal) from analysis ( Figure S3 A) did not modify this result.

How could learning manifest itself within 10 s rest periods? To gain insight into the systems-level possible mechanisms supporting this rapid form of offline learning, we recorded magnetoencephalographic activity during the task and in a resting-state baseline ( Figure 1 A). We spectrally decomposed trial-by-trial brain activity projected on the entire cortical sheet spanning rhythms that support cognitive and motor function []. At each location (548 edges on a cortical grid) and frequency (1–90 Hz, Figure 3 A), a linear mixed effects (LME) model was estimated using oscillatory activity to predict micro-online and micro-offline learning. Micro-offline learning was inversely predicted by beta-band (16–22 Hz) brain oscillatory activity during rest periods in a predominantly contralateral frontoparietal network ( Figures 3 and 4 Table S1 ).

(B) Frontoparietal beta activity predicted micro-offline gains throughout the duration of early-learning rest periods (averaged in each of 5 consecutive 2 s segments, LME model coefficient ± SEM, n = 10 trials × 27 participants).

(A) Topography of the predominantly contralateral beta oscillatory activity during rest periods predictive of micro-offline learning, indicated by the LME model coefficient ( Table S1 ).

(B) Inverse relationship between frontoparietal beta oscillatory activity during rest periods and micro-offline learning (n = 10 trials × 27 participants). See also Figure S3 for predictive oscillatory activity for micro-scale learning. See also Table S1

(A) Brain oscillatory activity during rest periods predictive of micro-offline learning. The horizontal plane depicts the relative power during rest periods compared to resting-state baseline across spectra (x axis, 1–90 Hz) and cortex (y axis, 548 locations clustered at frontal [Fro], parietal [Par], temporal [Temp], occipital [Occ], and cerebellar [Post] lobes). Warm yellow colors depict significant power increases during rest periods compared to resting-state baseline, cold blue colors significant power decreases (two-tailed one-sample t tests, n = 27). The z axis depicts the strength of the inverse relationship between oscillatory power and micro-offline learning (linear mixed effects (LME) model coefficient, n = 10 trials × 27 participants) at the significant frequencies and locations (magenta). All p < 0.05im, FDR-corrected for multiple comparisons. Note that only beta oscillatory activity at 16-22 Hz in frontoparietal areas was predictive of micro-offline learning.

Learning over all day-1 trials (2.73 ± 0.22 key presses/s, mean ± SEM, two-tailed one-sample t test, T = 12.15, p < 0.001) was larger than overnight improvement from the end of training on day 1 to test on day 2 (0.73 ± 0.10 key presses/s, T = 6.92, p < 0.001), consistent with previous reports []. Overnight offline learning did not correlate with micro-offline gains during early learning (linear model, p = 0.83), suggesting different mechanisms at play. Accuracy was comparably high during early (trials 1–11; 0.89 ± 0.02, mean ± SEM) and late (trials 12–36; 0.90 ± 0.01) learning trials ( Figure S1 B).

We probed the robustness of these findings by defining micro-scale learning in alternative ways: (A) tapping speed of correct sequences in the first and last 2 s of each practice period, (B) tapping speed of correct sequences of the first and last second of each practice period, and (C) the difference in the intersection at the beginning and the end of a least-squares fit line to the performance of each practice period. All measurements rendered comparable results: early learning was evidenced during rest periods rather than during practice periods (see also Figures S1 C–S1F). Performance measurements allowing within-practice-period temporal resolution of errors could conceivably provide additional information.

Total early learning was calculated as the sum of single-trial performance changes and amounted to 2.37 ± 0.24 key presses/s (mean ± SEM, two-tailed one-sample t test, T = 9.76, p < 0.001). To assess the micro-online and micro-offline contribution to early learning, we summed performance differences in each participant over all 11 practice or 10 rest periods. Comparing each contribution to total early learning, we found that all early learning was accounted for by performance increases during rest periods rather than during practice periods ( Figure 2 C). Indeed, on average, micro-online changes were nil (−0.32 ± 0.75 key presses/s, T = −0.41, p = 0.68), whereas micro-offline gains were substantial (2.69 ± 0.63 key presses/s, T = 4.19, p < 0.001, Figures 2 B and 2C).

During early learning, performance improved rapidly within the first few minutes of practice ( Figure 1 B) before reaching a performance ceiling []. Observation of the learning curve at a high temporal within-trial resolution unveiled clear performance increments between practice periods ( Figure 2 A). We then proceeded to dissect learning into performance improvements occurring during the practice and rest periods. Micro-online learning was defined as the difference in tapping speed (key presses/s) between the beginning and end of each practice period. Micro-offline learning was defined as the difference in tapping speed between the end of each practice period and the beginning of the next one ( Figure 2 A; STAR Methods ). During practice periods, performance either slightly increased, decreased, or stagnated, whereas during rest periods, we detected micro-offline gains that closely tracked total learning at a trial-by-trial basis. Micro-offline gains were maximal in early trials when performance during practice periods neither improved nor worsened ( Figure 2 B).

(C) Data points in the violin plot depict the sum of changes in performance over early learning trials in each participant. Note that total early learning is accounted for by performance improvements during rest periods but not during practice periods (two-tailed one-sample t test for each learning partition,p < 0.001, FDR-corrected for multiple comparisons). See also Figure S1

(B) Trial-wise early learning. Each line depicts performance changes (micro-offline in red, micro-online in blue, total in black) per trial (mean + SEM). Total learning is closely accounted for by micro-offline gains (black and red lines), whereas micro-online performance changes fluctuate around 0. Note the presence of large micro-offline gains and total early learning in the initial trials in the absence of micro-online performance decrements. Subsequently, within-practice performance decrements manifested gradually as learning slowed down.

(A) Microscale early learning reveals performance increments over rest periods. Micro-online changes were calculated as the difference in tapping speed (key presses/s) of the first and last correct sequence within a practice period (blue in inset), and micro-offline changes were calculated as the difference between the last correct sequence within a practice period compared to the first of the next practice period (red in inset).

Initial training on a new motor skill consists of short periods of active practice alternating with short periods of rest, a pattern that results in significant early learning []. Here, we studied the relative contribution of practice and rest to early learning. 27 healthy humans practiced a well-characterized motor-skill task comprising a series of sequential key presses [], which is widely used in the study of procedural memory formation []. They trained over 36 trials consisting of 10 s practice (reduced duration []) and 10 s rest periods for a total of 12 min (day 1 in Figure 1 A). In each practice period, participants were asked to repetitively tap a 5-item sequence indicated on the screen as quickly and accurately as possible using their left, non-dominant hand. Participants returned the following day for a test session. Performance was measured as the tapping speed (key presses/s) for correctly performed sequences []. We defined early learning as the window of practice trials required to reach 95% of the total day-1 learning. Modeling the group average learning curve showed that this performance level was achieved by trial 11 ( Figure 1 B).

(B) Skill was measured as the average inter-tap interval within correct sequences (tapping speed measured in key presses/s) []. The average number of correct sequences per trial is shown as green dots. The performance curve of day 1 (mean + SEM) and the modeled group average performance (overlaid) showed that 95% of learning occurred within the first 11 trials (vertical line, early learning) before reaching maximal performance. See also Figure S1 for supplemental behavioral data and Figure S2 for individual data.

(A) Task: participants learned the motor-skill task [] over 36 trials (inset shows a single trial) consisting of alternating practice and rest periods of 10 s duration for a total of 12 min. In each practice period, participants were asked to repetitively tap the sequence indicated on the screen as quickly and accurately as possible using their left, non-dominant hand. The next day, performance was tested over 9 trials. Brain oscillatory activity was recorded with magnetoencephalography (MEG) for 5 min before (resting-state baseline) and during the task on day 1.

Discussion

The main finding of this study was that performance improvements during online procedural motor learning develop during rest instead of during practice periods. Early trials showed strongest micro-offline and total learning in the absence of preceding within-practice performance decrements. Downregulation of predominantly contralateral beta oscillatory activity during rest periods was identified as an intrinsic neural signature that predicted micro-offline gains.

14 Hotermans C.

Peigneux P.

Maertens de Noordhout A.

Moonen G.

Maquet P. Early boost and slow consolidation in motor skill learning. 15 Doyon J.

Korman M.

Morin A.

Dostie V.

Hadj Tahar A.

Benali H.

Karni A.

Ungerleider L.G.

Carrier J. Contribution of night and day sleep vs. simple passage of time to the consolidation of motor sequence and visuomotor adaptation learning. 4 Dayan E.

Cohen L.G. Neuroplasticity subserving motor skill learning. Consolidation, measured as offline performance gains, has been tested at different time intervals following the end of a practice session []. Here, we studied early performance improvements over periods of rest that occur within a series of practice bouts within the same session when naive subjects practice a new motor skill for the first time. Our results documented a substantial contribution of micro-offline performance improvements to early learning during these seconds-long rest periods in the absence of within-practice performance decrements ( Figure 2 B). The sum of these improvements in performance during rest periods was four times larger than it was during overnight offline learning (difference in performance between the end of training in day 1 and test on day 2), accounted for virtually all early procedural learning ( Figure 2 C), and represented approximately 95% of overall day-1 learning for this task ( Figure 1 B). Thus, micro-offline gains made a sizable contribution to early motor-skill learning and to what is often referred to as initial online learning when acquiring a new motor skill [].

16 Ptak R.

Schnider A.

Fellrath J. The dorsal frontoparietal network: a core system for emulated action. 17 Eysenck H.J. A three-factor theory of reminiscence. 18 Eysenck H.J. An experimental test of the “inhibition” and “consolidation” theories of reminiscence. 19 Rachman S.

Grassi J. Reminiscence, inhibition and consolidation. ( G) [ 9 Pan S.C.

Rickard T.C. Sleep and motor learning: is there room for consolidation?. 20 Rickard T.C.

Cai D.J.

Rieth C.A.

Jones J.

Ard M.C. Sleep does not enhance motor sequence learning. The findings that micro-offline gains in this period were substantial and largest at trials with no discernible evidence of within-practice performance decrements ( Figure 2 B) are consistent with the interpretation that early-learning micro-offline gains may represent a rapid form of consolidation. In these early trials, micro-offline gains could conceivably result from unmasking of inhibitory effects like low-level fatigue or reactive inhibition []. However, previous work on rapid improvements after a few minutes of rest in the rotor pursuit task [] have been interpreted as reflecting “the need for rest on the part of the organism in order to consolidate the memory trace” [] rather than recovery from inhibitory effects []. After performance maximum was reached (i.e., following trial 11), within-practice performance decrements robustly expressed, likely signaling either fatigue or reactive inhibition Figure S1 G) []. Optimal rest- and practice-period duration for this rapid consolidation remain to be determined.

10 Vahdat S.

Fogel S.

Benali H.

Doyon J. Network-wide reorganization of procedural memory during NREM sleep revealed by fMRI. 21 Orban P.

Peigneux P.

Lungu O.

Albouy G.

Breton E.

Laberenne F.

Benali H.

Maquet P.

Doyon J. The multifaceted nature of the relationship between performance and brain activity in motor sequence learning. 10 Vahdat S.

Fogel S.

Benali H.

Doyon J. Network-wide reorganization of procedural memory during NREM sleep revealed by fMRI. 22 Albouy G.

Fogel S.

Pottiez H.

Nguyen V.A.

Ray L.

Lungu O.

Carrier J.

Robertson E.

Doyon J. Daytime sleep enhances consolidation of the spatial but not motoric representation of motor sequence memory. 23 Albouy G.

King B.R.

Maquet P.

Doyon J. Hippocampus and striatum: dynamics and interaction during acquisition and sleep-related motor sequence memory consolidation. 24 Fioravante D.

Regehr W.G. Short-term forms of presynaptic plasticity. 2 Squire L.R.

Genzel L.

Wixted J.T.

Morris R.G. Memory consolidation. Classically studied offline improvements in skill over extended periods of time that manifest after the end of a training session contrast with micro-offline improvements that occur early within a training session. Accordingly, we found no correlation between micro-online or micro-offline learning and overnight behavioral gains. Overnight improvements in motor skill have been linked to a topological shift of task-related neural activity from cortical to subcortical regions [] supported by a dynamic interaction between declarative (hippocampus) and procedural (striatum) memory systems []. On the other hand, the brief time window of this rapid form of consolidation points to short-term plasticity [] rather than long-term potentiation or structural reorganization relevant for longer forms of consolidation [].

16 Ptak R.

Schnider A.

Fellrath J. The dorsal frontoparietal network: a core system for emulated action. 25 Zanos S.

Rembado I.

Chen D.

Fetz E.E. Phase-locked stimulation during cortical beta oscillations produces bidirectional synaptic plasticity in awake monkeys. 3 Engel A.K.

Fries P. Beta-band oscillations--signalling the status quo?. 26 Maquet P.

Laureys S.

Peigneux P.

Fuchs S.

Petiau C.

Phillips C.

Aerts J.

Del Fiore G.

Degueldre C.

Meulemans T.

et al. Experience-dependent changes in cerebral activation during human REM sleep. 27 Ramanathan D.S.

Gulati T.

Ganguly K. Sleep-dependent reactivation of ensembles in motor cortex promotes skill consolidation. 28 Cohen N.

Pell L.

Edelson M.G.

Ben-Yakov A.

Pine A.

Dudai Y. Peri-encoding predictors of memory encoding and consolidation. 20 Rickard T.C.

Cai D.J.

Rieth C.A.

Jones J.

Ard M.C. Sleep does not enhance motor sequence learning. 29 Tambini A.

Ketz N.

Davachi L. Enhanced brain correlations during rest are related to memory for recent experiences. 30 Foster D.J.

Wilson M.A. Reverse replay of behavioural sequences in hippocampal place cells during the awake state. 31 Tambini A.

Davachi L. Persistence of hippocampal multivoxel patterns into postencoding rest is related to memory. 32 Yang G.

Lai C.S.

Cichon J.

Ma L.

Li W.

Gan W.B. Sleep promotes branch-specific formation of dendritic spines after learning. 33 Brodt S.

Pöhlchen D.

Flanagin V.L.

Glasauer S.

Gais S.

Schönauer M. Rapid and independent memory formation in the parietal cortex. 34 Euston D.R.

Tatsuno M.

McNaughton B.L. Fast-forward playback of recent memory sequences in prefrontal cortex during sleep. 30 Foster D.J.

Wilson M.A. Reverse replay of behavioural sequences in hippocampal place cells during the awake state. 4 Dayan E.

Cohen L.G. Neuroplasticity subserving motor skill learning. 5 Censor N.

Horovitz S.G.

Cohen L.G. Interference with existing memories alters offline intrinsic functional brain connectivity. 8 Censor N.

Sagi D.

Cohen L.G. Common mechanisms of human perceptual and motor learning. 35 Chen S.X.

Kim A.N.

Peters A.J.

Komiyama T. Subtype-specific plasticity of inhibitory circuits in motor cortex during motor learning. 36 Benali A.

Weiler E.

Benali Y.

Dinse H.R.

Eysel U.T. Excitation and inhibition jointly regulate cortical reorganization in adult rats. 37 Yamawaki N.

Stanford I.M.

Hall S.D.

Woodhall G.L. Pharmacologically induced and stimulus evoked rhythmic neuronal oscillatory activity in the primary motor cortex in vitro. 38 Nutt D.

Wilson S.

Lingford-Hughes A.

Myers J.

Papadopoulos A.

Muthukumaraswamy S. Differences between magnetoencephalographic (MEG) spectral profiles of drugs acting on GABA at synaptic and extrasynaptic sites: a study in healthy volunteers. Our finding that frontoparietal beta (16–22 Hz) oscillatory activity during rest periods predicted micro-offline learning is consistent with the involvement of the dorsal frontoparietal network in encoding offline representations of movement kinematics []. Recently, the beta rhythm was found to play a role in structuring short-term activity-dependent plasticity [], qualifying it as a possible neural signature for this fast form of consolidation. A reduction of the beta rhythm amplitude is present during brain states that mediate movement preparation, execution, and imagery as well as somatosensation []. Thus, a low-amplitude beta rhythm reflects a state of sensorimotor engagement. It is possible that beta-related activity during rest periods may contribute to micro-offline learning through reactivation of previous practice-related activity [] or memory replay []. Memory replay has been documented in humans [] during awake states [], at hippocampal [] as well as neocortical sites [], and it may develop at a far faster rate than does the pattern of activity during memory formation [] either in forward or reverse order []. This idea is consistent with observations suggesting that the reactivations involved in reconsolidation ultimately strengthen memories after an initial period of vulnerability []. GABAergic signaling, a key determinant of plasticity related to early learning [] and beta oscillations [], could possibly contribute to micro-offline gains as well. Identification of this oscillatory signature of micro-offline learning will allow future experiments to address the question of causality.