Animals and humans are able to acquire and automatize new sequences of movements, hence allowing them to expand and update their repertoire of complex goal-oriented motor actions for long-term use. To investigate the mechanisms underlying this type of procedural memory in humans, a large body of behavioral studies has used motor sequence learning (MSL) tasks designed to test the ability to perform temporally ordered and coordinated movements, learned either implicitly or explicitly and has assessed their performances in different phases of the acquisition process (Korman et al., 2003; Abrahamse et al., 2013; Diedrichsen and Kornysheva, 2015; Verwey et al., 2015). While practice of an explicit MSL task leads to substantial within-session execution improvements, there is now ample evidence indicating that between-session maintenance of, and even increases in, performance can be observed after a night of sleep (Landry et al., 2016; Nettersheim et al., 2015), while performance is unstable and tends to decay during an equal period of wakefulness (Doyon et al., 2009a; Brawn et al., 2010; Nettersheim et al., 2015; Landry et al., 2016). Therefore, it is thought that sleep favors reprocessing of the motor memory trace, thus promoting its consolidation for long-term skill proficiency (Fischer et al., 2002, see King et al., 2017; Doyon et al., 2018 for recent in-depth reviews).

Functional magnetic resonance imaging (fMRI) studies using General-Linear-Model (GLM) contrasts of activation have also revealed that MSL is associated with the recruitment of an extended network of cerebral (Hardwick et al., 2013), cerebellar and spinal regions (Vahdat et al., 2015a), whose contributions differentiate as learning progresses (Karni et al., 1998; Dayan and Cohen, 2011; Doyon et al., 2018). In fact, plastic changes (Ungerleider et al., 2002; Doyon and Benali, 2005) are known to occur within the initial training session, as well as during the offline consolidation phase, the latter being characterized by a functional ‘reorganization’ of the nervous system structures supporting this type of procedural memory function (Rasch and Born, 2008; Born and Wilhelm, 2012; Albouy et al., 2013b; Dudai et al., 2015; Bassett et al., 2015; Fogel et al., 2017; Vahdat et al., 2017b). More specifically, MSL is known to activate a cortical, associative striatal and cerebellar motor network, which is assisted by the hippocampus during the initial ‘fast-learning’ phase (Albouy et al., 2013b). Yet, when approaching asymptotic behavioral performance after longer periods of practice, activity within the hippocampus and cerebellum decreases while activity within the sensorimotor striatum increases (Doyon et al., 2002), both effects conveying the transition to the ‘slow-learning’ phase. Other studies have then shown that the same striatal regions are reactivated during sleep spindles (Fogel et al., 2017), hence contributing to the progressive emergence of a reorganized network (Debas et al., 2010; Vahdat et al., 2017b) that is further stabilized when additional MSL practice is extended over multiple days and weeks (Lehéricy et al., 2005).

A critical issue typically overlooked by previous MSL neuroimaging research using GLM-based activation contrasts, however, is that learning-related changes in brain activity do reflect the temporal evolution of recruited processes during blocks of practice, only some of which may be specifically related to brain plasticity induced by MSL. For instance, increases in activity could not only signal a greater implication of the circuits specialized in movement sequential learning per se, but could also result from the inherent faster execution of the motor task. Likewise, a decrease in activity could either indicate some form of optimization and greater efficiency of the circuits involved in executing the task (Wu et al., 2004), or could show the reduced recruitment of non-specific networks supporting the acquisition process. Therefore, even with the use of control conditions to dissociate sequence-specific from non-specific processes (Orban et al., 2010), the observed large-scale activation differences associated with different learning phases do not necessarily provide direct evidence of plasticity related to the processing of a motor sequence-specific representation (Berlot et al., 2018). Furthermore, it is also conceivable that these plastic changes could even occur locally without significant changes in the GLM-based regional activity level. Finally, in most studies investigating the neural substrate mediating the consolidation process of explicit MSL, the neural changes associated with this mnemonic mechanism are assessed by contrasting the brain activity level of novice participants between their initial training and a delayed practice session. Therefore, they measure not only plasticity for sequence-specific (e.g. optimized chunks), but also task-related competence (e.g. habituation to experimental apparatus, optimized execution strategies, attentional processes). The latter proficiency is notably observed when participants practice two motor sequences in succession and the initial performance during sequence execution is significantly better for the subsequent than for the first sequence.

To address these specificity limitations, multivariate pattern analysis (MVPA) has been proposed to evaluate how local patterns of activity are able to reliably discriminate between stimuli or evoked memories of the same type over repeated occurrences, hence allowing to test information-based hypotheses that GLM contrasts cannot inquire (Hebart and Baker, 2018). In the MSL literature, only a few studies have used such MVPA approaches to identify the regions that specialize in processing the representation of learned motor sequences (Wiestler and Diedrichsen, 2013; Nambu et al., 2015; Wiestler et al., 2011; Kornysheva and Diedrichsen, 2014; Yokoi et al., 2018). These studies, however, mainly focused on extensively practiced sequences over multiple training sessions across multiple days. For instance, in a recent study covering dorsal cerebral cortices only (Wiestler and Diedrichsen, 2013), cross-validated classification accuracy was measured separately on activity patterns evoked by the practice of trained and untrained sets of sequences. The authors showed that the extended training increased sequence discriminability in a network spanning bilaterally the primary and secondary motor as well as parietal cortices. In another study (Nambu et al., 2015) that aimed to analyze separately the preparation and execution of sequential movements, representations of extensively trained sequences were identified in the contralateral dorsal premotor and supplementary motor cortices during preparation, while representations related to the execution were found in the parietal cortex ipsilaterally, the premotor and motor cortices bilaterally as well as the cerebellum. In both studies, the regions carrying sequence-specific representations overlapped only partly with those identified using GLM-based measures, hence illustrating the fact that coarser differences in activation between novel and trained sequences does not necessarily provide evidence of plasticity for sequential information. However, the classification-based measures they used may have biased their parametric statistical results by violating both the normality assumption and theoretical null-distribution (Jamalabadi et al., 2016; Allefeld et al., 2016; Combrisson and Jerbi, 2015; Varoquaux, 2018) and may have thus been suboptimal in detecting representational changes (Walther et al., 2016).

As a part of a larger research program interested in identifying the neural substrates implicated in both consolidation and reconsolidation processes, the present study aimed to address both the critical issues overlooked by previous research investigating the early phases of MSL consolidation with GLM-based approach described above, as well as the limitations encountered when using classifier-based MVPA methods. Specifically, we employed a recently developed MVPA approach (Nili et al., 2014) that is unbiased and more sensitive to continuous representational changes (Walther et al., 2016), such as those that occur in the early stage of MSL and consolidation (Albouy et al., 2013c). Our experimental manipulation allowed us to isolate sequence-specific plasticity, by extracting patterns evoked through practice of both consolidated and new sequences at the same level of proficiency in the task and by computing this novel multivariate distance metric using a searchlight approach over the whole brain in order to cover cortical and subcortical regions critical to MSL. Based on theoretical models (Albouy et al., 2013b; Doyon et al., 2018) derived from imaging work in humans and invasive animal studies, we hypothesized that offline consolidation following training would induce greater cortical and striatal as well as weaker hippocampal sequence-specific representations.

To investigate changes in the neural representations of motor sequences occurring during learning, young, healthy participants (n = 18) practiced two five-element sequences of finger movements (executed through button presses) separately on two consecutive days. On the third day, participants were required to execute again the same two sequences, then considered to be consolidated, together with two new five-element untrained sequences. This practice session consisted of 64 pseudo-randomly ordered short blocks split in two runs, with 16 blocks of each sequence. All four sequences were executed using their non-dominant left hand while functional MRI data was acquired.

Behavioral performance

We analyzed the behavioral performance related to the four different sequences using a repeated-measure mixed-effects model (with grouping within subject). First, we tested for differences in performances between new and consolidated sequences performances (i.e., speed as average duration of correct sequences and number of errors per block), by taking as fixed-effect the learning stage (new or consolidated) and number of blocks and their interaction, and as random effect the sequences and blocks to account for between-subject differences in performance baseline and rate of change (See Supplementary files 1 and 2 respectively for speed and errors results). As expected, new sequences were performed more slowly (${\displaystyle \beta =.365,SE=0.047,p<.001}$) and less accurately (${\displaystyle \beta =-0.304,SE=0.101,p<.001}$) than the consolidated ones. Significant improvement across blocks was observed for new sequences as compared with consolidated sequences in term of change of speed (${\displaystyle \beta =-0.018,SE=0.002,p<.001}$), thus showing an expected learning curve visible in Figure 1. Yet accuracy did not show significant improvement ($\beta =0.014,SE=0.010,p=0.152$), likely explained by the limited precision of this measure that ranges discretely from 0 to 5. By contrast, the consolidated sequences did not show significant changes in speed ($\beta =-0.006,SE=0.005,p=0.192$) nor accuracy ($\beta =-0.006,SE=0.057,p=0.919$), the asymptotic performances being already reached through practice and the consolidation process. While most studies in the field have measured retention and even offline gains between the end of training and the beginning of a post-sleep retest, we chose here to measure consolidation as the difference at the same point in time between the new and consolidated sequences. We also measured the difference in speed (mean correct sequence duration) between the last two blocks of training and the first two blocks of retest for the consolidated sequences (fixed-effect:post, random-effect:sequence) in order to be comparable to previous studies, and found a significantly lower sequence duration in the beginning of the retest ($\beta =0.086,SE=0.023,p=.000141$).

Figure 1 with 1 supplement see all

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Second, we tested separately the new and consolidated sequences to verify that they did not show consistent differences across subjects using a model that combined sequence, blocks and their interaction as fixed effect (model and results can be found in Supplementary files 3 and 4 respectively). Importantly, there were also no significant differences between the two consolidated sequences in term of speed ($\beta =0.031,SE=0.026,p=0.234$) and accuracy ($\beta =-0.030,SE=0.111,p=0.789$), nor between the two new sequences with respect to speed ($\beta =0.025,SE=0.045,p=0.577$) and accuracy ($\beta =-0.245,SE=0.138,p=0.076$). To verify that behavioral differences could not be accounted by the retest of trained sequences (7 blocks of 12 repetitions for each sequence) preceding the task analyzed here, we compared the sequence duration of the five first sequences of the retest blocks with the duration of the untrained sequences during the present task. The values reported in supplementary material (Figure 1—figure supplement 1), show that the trained sequences are still performed faster than the untrained sequences (${\displaystyle \beta =-.431,SE=0.090,p<{10}^{-}5}$).

A common distributed sequence representation irrespective of learning stage

From the preprocessed functional MRI data we extracted patterns of activity for each block of practice, and computed a cross-validated Mahalanobis distance (Nili et al., 2014; Walther et al., 2016) using a Searchlight approach (Kriegeskorte et al., 2006) over brain cortical surfaces and subcortical regions of interest. An illustration of the method can be found in Figure 2—figure supplement 4. Such multivariate distances, when positive, demonstrate that there is a stable difference in activity patterns between the conditions compared, and thus reflect the level of discriminability between these conditions. To assess true representational patterns, and not mere global activity differences, we computed this discriminability measure for sequences that were at the same stage of learning, thus separately for consolidated and new sequences. From the individual discriminability maps, we then measured the prevalence of discriminability at the group level, using non-parametric testing with a Threshold-Free-Cluster-Enhancement approach (TFCE) (Smith and Nichols, 2009) to enable locally adaptive cluster correction.

To extract the brain regions that show discriminative activity patterns for specific sequence during both learning stages, we then submitted these separate group results for the consolidated and new sequences to a minimum-statistic conjunction analysis. A large distributed set of regions (Figure 2) displayed significant discriminability, including the primary visual, as well as the posterior parietal, primary and supplementary motor, premotor and dorsolateral prefrontal cortices. (See the statistical maps for each learning stage separately in the Supplementary material (Figure 2—figure supplement 1, Figure 2—figure supplement 2).

Figure 2 with 4 supplements see all

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To complement this result we extracted the main effect of activation during this task (Figure 2—figure supplement 3), showing that pattern distances do not exactly correspond to the regions significantly activated in mass-univariate GLM analysis.

Reorganization of the distributed sequence representation after memory consolidation

In order to evaluate the reorganization of sequence representation undergone by consolidation at the group level, the consolidated and new sequence discriminability maps from all participants were submitted to a non-parametric two-tailed pairwise t-test with TFCE. To ascertain that a greater discriminability in one stage versus the other was supported by a significant level of discriminability within that stage, we then calculated the minimum statistic conjunction (Nichols et al., 2005) of the contrast maps with the consolidated and new sequences group results, respectively with the positive and negative contrast differences (Figure 3). This procedure equals to mask the contrast with significantly positive simple effects. The resulting map thus represents the subset of voxels that fulfill the union of two logical conjunctions: ((Consolidated>New)∩(Consolidated>0))∪((New>Consolidated)∩(New>0)) with each sub-test being assessed by thresholding the p-value of individual non-parametric permutation tests.

Figure 3

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Discriminability between the consolidated sequences was significantly higher than that between the new sequences in bilateral sensorimotor putamen, thalamus and anterior insula, as well as in the ipsilateral cerebellar lobule IX, posterior cingulate and parietal cortices, and contralaterally in the lateral and dorsal premotor, supplementary motor, frontopolar and dorsolateral prefrontal cortices in addition to cerebellar Crus I. By contrast, the pattern dissimilarity was higher for the new sequences in bilateral hippocampi as well as the body of the caudate nuclei, subthalamic nuclei, and cerebellar Crus II ipsilaterally. Although striatal activity patterns differentiating newly acquired sequences were found in contralateral putamen (Figure 2—figure supplement 1), this discriminability was significantly larger for consolidated sequences in sensorimotor regions of the putamen bilaterally.

We also conducted a univariate GLM analysis to contrast the consolidated and new sequences, but no results survived multiple comparison correction. This negative result could be accounted for by the absence of smoothing of signals during preprocessing.

In the present study, we aimed to identify the brain regions whose activity patterns differentiate between representations of multiple motor sequences during their execution in different acquisition phases: newly learned and consolidated. Using an MVPA approach, we considered that stable local patterns of activity could be used as a proxy for the specialization of neuronal circuits supportive of the efficient retrieval and expression of sequential motor memory traces. To investigate the differential pattern strength, we computed the novel unbiased multivariate distance and applied robust permutation-based statistics with adaptive cluster correction.

Behavioral data analyzed and presented in the article as well as statistical maps of brain representational measure have been deposited on the Open Science Framework with the DOI 10.17605/OSF.IO/EPJ2V

1. 1. Pinsard B

   (2018) Open Science Framework

   Consolidation alters motor sequence-specific distributed representations.

   https://osf.io/epj2v/?view_only=cca0307cb0d84787b4b203451e96c0ed

1. 1. Abrahamse EL
   2. Ruitenberg MF
   3. de Kleine E
   4. Verwey WB

   (2013) Control of automated behavior: insights from the discrete sequence production task

   Frontiers in Human Neuroscience 7:.

   https://doi.org/10.3389/fnhum.2013.00082

   • PubMed
   • Google Scholar
2. 1. Aguirre GK
   2. Mattar MG
   3. Magis-Weinberg L

   (2011) De bruijn cycles for neural decoding

   NeuroImage 56:1293–1300.

   https://doi.org/10.1016/j.neuroimage.2011.02.005

   • PubMed
   • Google Scholar
3. 1. Albouy G
   2. King BR
   3. Maquet P
   4. Doyon J

   (2013a) Hippocampus and striatum: dynamics and interaction during acquisition and sleep-related motor sequence memory consolidation

   Hippocampus 23:985–1004.

   https://doi.org/10.1002/hipo.22183

   • PubMed
   • Google Scholar
4. 1. Albouy G
   2. Sterpenich V
   3. Vandewalle G
   4. Darsaud A
   5. Gais S
   6. Rauchs G
   7. Desseilles M
   8. Boly M
   9. Dang-Vu T
   10. Balteau E
   11. Degueldre C
   12. Phillips C
   13. Luxen A
   14. Maquet P

   (2013b) Interaction between hippocampal and striatal systems predicts subsequent consolidation of motor sequence memory

   PLOS ONE 8:e59490.

   https://doi.org/10.1371/journal.pone.0059490

   • PubMed
   • Google Scholar
5. 1. Albouy G
   2. Fogel S
   3. Pottiez H
   4. Nguyen VA
   5. Ray L
   6. Lungu O
   7. Carrier J
   8. Robertson E
   9. Doyon J

   (2013c) Daytime sleep enhances consolidation of the spatial but not motoric representation of motor sequence memory

   PLOS ONE 8:e52805.

   https://doi.org/10.1371/journal.pone.0052805

   • PubMed
   • Google Scholar
6. 1. Albouy G
   2. Fogel S
   3. King BR
   4. Laventure S
   5. Benali H
   6. Karni A
   7. Carrier J
   8. Robertson EM
   9. Doyon J

   (2015) Maintaining vs. enhancing motor sequence memories: respective roles of striatal and hippocampal systems

   NeuroImage 108:423–434.

   https://doi.org/10.1016/j.neuroimage.2014.12.049

   • PubMed
   • Google Scholar
7. 1. Allefeld C
   2. Görgen K
   3. Haynes JD

   (2016) Valid population inference for information-based imaging: from the second-level t-test to prevalence inference

   NeuroImage 141:378–392.

   https://doi.org/10.1016/j.neuroimage.2016.07.040

   • PubMed
   • Google Scholar
8. 1. Andersson JL
   2. Skare S
   3. Ashburner J

   (2003) How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging

   NeuroImage 20:870–888.

   https://doi.org/10.1016/S1053-8119(03)00336-7

   • PubMed
   • Google Scholar
9. 1. Arbuckle SA
   2. Yokoi A
   3. Pruszynski JA
   4. Diedrichsen J

   (2019) Stability of representational geometry across a wide range of fMRI activity levels

   NeuroImage 186:.

   https://doi.org/10.1016/j.neuroimage.2018.11.002

   • PubMed
   • Google Scholar
10. 1. Avants BB
    2. Epstein CL
    3. Grossman M
    4. Gee JC

    (2008) Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain

    Medical Image Analysis 12:26–41.

    https://doi.org/10.1016/j.media.2007.06.004

    • PubMed
    • Google Scholar
11. 1. Bassett DS
    2. Yang M
    3. Wymbs NF
    4. Grafton ST

    (2015) Learning-induced autonomy of sensorimotor systems

    Nature Neuroscience 18:744–751.

    https://doi.org/10.1038/nn.3993

    • Google Scholar
12. 1. Beck AT
    2. Ward CH
    3. Mendelson M
    4. Mock J
    5. Erbaugh J

    (1961) An inventory for measuring depression

    Archives of General Psychiatry 4:561–571.

    https://doi.org/10.1001/archpsyc.1961.01710120031004

    • PubMed
    • Google Scholar
13. 1. Berlot E
    2. Popp NJ
    3. Diedrichsen J

    (2018) In search of the engram, 2017

    Current Opinion in Behavioral Sciences 20:56–60.

    https://doi.org/10.1016/j.cobeha.2017.11.003

    • Google Scholar
14. Preprint

    1. Beukema P
    2. Diedrichsen J
    3. Verstynen T

    (2018) Binding during sequence learning does not alter cortical representations of individual actions

    bioRxiv.

    https://doi.org/10.1101/255794

    • Google Scholar
15. Preprint

    1. Bollu T
    2. Whitehead SC
    3. Prasad N
    4. Walker JR
    5. Shyamkumar N
    6. Subramaniam R
    7. Kardon BM
    8. Cohen I
    9. Goldberg JH

    (2018) Cortical control of kinematic primitives in mice performing a hold-still-center-out reach task

    bioRxiv.

    https://doi.org/10.1101/304907

    • Google Scholar
16. 1. Born J
    2. Wilhelm I

    (2012) System consolidation of memory during sleep

    Psychological Research 76:192–203.

    https://doi.org/10.1007/s00426-011-0335-6

    • PubMed
    • Google Scholar
17. 1. Boutin A
    2. Pinsard B
    3. Boré A
    4. Carrier J
    5. Fogel SM
    6. Doyon J

    (2018) Transient synchronization of hippocampo-striato-thalamo-cortical networks during sleep spindle oscillations induces motor memory consolidation

    NeuroImage 169:419–430.

    https://doi.org/10.1016/j.neuroimage.2017.12.066

    • PubMed
    • Google Scholar
18. 1. Brawn TP
    2. Fenn KM
    3. Nusbaum HC
    4. Margoliash D

    (2010) Consolidating the effects of waking and sleep on motor-sequence learning

    Journal of Neuroscience 30:13977–13982.

    https://doi.org/10.1523/JNEUROSCI.3295-10.2010

    • PubMed
    • Google Scholar
19. Preprint

    1. Burman DD

    (2018a) Hippocampal connectivity with sensorimotor cortex during volitional finger movements. i. Laterality and relationship to motor learning

    Biorxiv.

    https://doi.org/10.1101/479451

    • Google Scholar
20. Preprint

    1. Burman DD

    (2018b) Hippocampal connectivity with sensorimotor cortex during volitional finger movements. II. spatial and temporal selectivity

    Biorxiv.

    https://doi.org/10.1101/479436

    • Google Scholar
21. 1. Buysse DJ
    2. Reynolds CF
    3. Monk TH
    4. Berman SR
    5. Kupfer DJ

    (1989) The Pittsburgh sleep quality index: a new instrument for psychiatric practice and research

    Psychiatry Research 28:193–213.

    https://doi.org/10.1016/0165-1781(89)90047-4

    • PubMed
    • Google Scholar
22. 1. Combrisson E
    2. Jerbi K

    (2015) Exceeding chance level by chance: the caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy

    Journal of Neuroscience Methods 250:126–136.

    https://doi.org/10.1016/j.jneumeth.2015.01.010

    • PubMed
    • Google Scholar
23. 1. Corbit VL
    2. Ahmari SE
    3. Gittis AH

    (2017) A corticostriatal balancing act supports skill learning

    Neuron 96:253–255.

    https://doi.org/10.1016/j.neuron.2017.09.046

    • PubMed
    • Google Scholar
24. 1. Costa RM
    2. Cohen D
    3. Nicolelis MA

    (2004) Differential corticostriatal plasticity during fast and slow motor skill learning in mice

    Current Biology 14:1124–1134.

    https://doi.org/10.1016/j.cub.2004.06.053

    • PubMed
    • Google Scholar
25. 1. Dale AM
    2. Fischl B
    3. Sereno MI

    (1999) Cortical surface-based analysis. I. segmentation and surface reconstruction

    NeuroImage 9:179–194.

    https://doi.org/10.1006/nimg.1998.0395

    • PubMed
    • Google Scholar
26. 1. Davachi L
    2. DuBrow S

    (2015) How the hippocampus preserves order: the role of prediction and context

    Trends in Cognitive Sciences 19:92–99.

    https://doi.org/10.1016/j.tics.2014.12.004

    • PubMed
    • Google Scholar
27. 1. Dayan E
    2. Cohen LG

    (2011) Neuroplasticity subserving motor skill learning

    Neuron 72:443–454.

    https://doi.org/10.1016/j.neuron.2011.10.008

    • PubMed
    • Google Scholar
28. 1. Debas K
    2. Carrier J
    3. Orban P
    4. Barakat M
    5. Lungu O
    6. Vandewalle G
    7. Hadj Tahar A
    8. Bellec P
    9. Karni A
    10. Ungerleider LG
    11. Benali H
    12. Doyon J

    (2010) Brain plasticity related to the consolidation of motor sequence learning and motor adaptation

    PNAS 107:17839–17844.

    https://doi.org/10.1073/pnas.1013176107

    • PubMed
    • Google Scholar
29. 1. Debas K
    2. Carrier J
    3. Barakat M
    4. Marrelec G
    5. Bellec P
    6. Hadj Tahar A
    7. Karni A
    8. Ungerleider LG
    9. Benali H
    10. Doyon J

    (2014) Off-line consolidation of motor sequence learning results in greater integration within a cortico-striatal functional network

    NeuroImage 99:50–58.

    https://doi.org/10.1016/j.neuroimage.2014.05.022

    • PubMed
    • Google Scholar
30. Preprint

    1. Diedrichsen J
    2. Provost S
    3. Zareamoghaddam H

    (2016) On the distribution of cross-validated mahalanobis distances

    arXiv.

    https://arxiv.org/abs/1607.01371

    • Google Scholar
31. 1. Diedrichsen J
    2. Kornysheva K

    (2015) Motor skill learning between selection and execution

    Trends in Cognitive Sciences 19:227–233.

    https://doi.org/10.1016/j.tics.2015.02.003

    • Google Scholar
32. 1. Doyon J
    2. Song AW
    3. Karni A
    4. Lalonde F
    5. Adams MM
    6. Ungerleider LG

    (2002) Experience-dependent changes in cerebellar contributions to motor sequence learning

    PNAS 99:1017–1022.

    https://doi.org/10.1073/pnas.022615199

    • PubMed
    • Google Scholar
33. 1. Doyon J
    2. Korman M
    3. Morin A
    4. Dostie V
    5. Hadj Tahar A
    6. Benali H
    7. Karni A
    8. Ungerleider LG
    9. Carrier J

    (2009a) Contribution of night and day sleep vs. simple passage of time to the consolidation of motor sequence and visuomotor adaptation learning

    Experimental Brain Research 195:15–26.

    https://doi.org/10.1007/s00221-009-1748-y

    • PubMed
    • Google Scholar
34. 1. Doyon J
    2. Bellec P
    3. Amsel R
    4. Penhune V
    5. Monchi O
    6. Carrier J
    7. Lehéricy S
    8. Benali H

    (2009b) Contributions of the basal ganglia and functionally related brain structures to motor learning

    Behavioural Brain Research 199:61–75.

    https://doi.org/10.1016/j.bbr.2008.11.012

    • PubMed
    • Google Scholar
35. 1. Doyon J
    2. Gabitov E
    3. Vahdat S
    4. Lungu O
    5. Boutin A

    (2018) Current issues related to motor sequence learning in humans

    Current Opinion in Behavioral Sciences 20:89–97.

    https://doi.org/10.1016/j.cobeha.2017.11.012

    • Google Scholar
36. 1. Doyon J
    2. Benali H

    (2005) Reorganization and plasticity in the adult brain during learning of motor skills

    Current Opinion in Neurobiology 15:161–167.

    https://doi.org/10.1016/j.conb.2005.03.004

    • PubMed
    • Google Scholar
37. 1. Dudai Y
    2. Karni A
    3. Born J

    (2015) The consolidation and transformation of memory

    Neuron 88:20–32.

    https://doi.org/10.1016/j.neuron.2015.09.004

    • PubMed
    • Google Scholar
38. 1. Ejaz N
    2. Hamada M
    3. Diedrichsen J

    (2015) Hand use predicts the structure of representations in sensorimotor cortex

    Nature Neuroscience 18:1034–1040.

    https://doi.org/10.1038/nn.4038

    • PubMed
    • Google Scholar
39. Book

    1. Etzel JA
    2. Cole MW
    3. Braver TS

    (2004)

    Looking outside the searchlight

    In: Langs G, Rish I, Grosse-Wentrup M, Murphy B, editors. Machine Learning and Interpretation in Neuroimaging. Berlin Heidelberg: Springer. pp. 26–33.

    • Google Scholar
40. 1. Etzel JA
    2. Zacks JM
    3. Braver TS

    (2013) Searchlight analysis: promise, pitfalls, and potential

    NeuroImage 78:261–269.

    https://doi.org/10.1016/j.neuroimage.2013.03.041

    • PubMed
    • Google Scholar
41. 1. Fischer S
    2. Hallschmid M
    3. Elsner AL
    4. Born J

    (2002) Sleep forms memory for finger skills

    PNAS 99:11987–11991.

    https://doi.org/10.1073/pnas.182178199

    • PubMed
    • Google Scholar
42. 1. Fischl B
    2. Sereno MI
    3. Tootell RB
    4. Dale AM

    (1999) High-resolution intersubject averaging and a coordinate system for the cortical surface

    Human Brain Mapping 8:272–284.

    https://doi.org/10.1002/(SICI)1097-0193(1999)8:4<272::AID-HBM10>3.0.CO;2-4

    • PubMed
    • Google Scholar
43. 1. Fischl B
    2. Rajendran N
    3. Busa E
    4. Augustinack J
    5. Hinds O
    6. Yeo BT
    7. Mohlberg H
    8. Amunts K
    9. Zilles K

    (2008) Cortical folding patterns and predicting cytoarchitecture

    Cerebral Cortex 18:1973–1980.

    https://doi.org/10.1093/cercor/bhm225

    • PubMed
    • Google Scholar
44. 1. Fogel S
    2. Albouy G
    3. King BR
    4. Lungu O
    5. Vien C
    6. Bore A
    7. Pinsard B
    8. Benali H
    9. Carrier J
    10. Doyon J

    (2017) Reactivation or transformation? motor memory consolidation associated with cerebral activation time-locked to sleep spindles

    Plos One 12:e0174755.

    https://doi.org/10.1371/journal.pone.0174755

    • PubMed
    • Google Scholar
45. 1. François-Brosseau FE
    2. Martinu K
    3. Strafella AP
    4. Petrides M
    5. Simard F
    6. Monchi O

    (2009) Basal ganglia and frontal involvement in self-generated and externally-triggered finger movements in the dominant and non-dominant hand

    European Journal of Neuroscience 29:1277–1286.

    https://doi.org/10.1111/j.1460-9568.2009.06671.x

    • PubMed
    • Google Scholar
46. 1. Glasser MF
    2. Sotiropoulos SN
    3. Wilson JA
    4. Coalson TS
    5. Fischl B
    6. Andersson JL
    7. Xu J
    8. Jbabdi S
    9. Webster M
    10. Polimeni JR
    11. Van Essen DC
    12. Jenkinson M
    13. WU-Minn HCP Consortium

    (2013) The minimal preprocessing pipelines for the human connectome project

    NeuroImage 80:105–124.

    https://doi.org/10.1016/j.neuroimage.2013.04.127

    • PubMed
    • Google Scholar
47. 1. Haar S
    2. Dinstein I
    3. Shelef I
    4. Donchin O

    (2017) Effector-Invariant movement encoding in the human motor system

    The Journal of Neuroscience 37:9054–9063.

    https://doi.org/10.1523/JNEUROSCI.1663-17.2017

    • PubMed
    • Google Scholar
48. 1. Haber SN
    2. Calzavara R

    (2009) The cortico-basal ganglia integrative network: the role of the thalamus

    Brain Research Bulletin 78:69–74.

    https://doi.org/10.1016/j.brainresbull.2008.09.013

    • PubMed
    • Google Scholar
49. 1. Hanke M
    2. Halchenko YO
    3. Sederberg PB
    4. Olivetti E
    5. Fründ I
    6. Rieger JW
    7. Herrmann CS
    8. Haxby JV
    9. Hanson SJ
    10. Pollmann S

    (2009) PyMVPA: a unifying approach to the analysis of neuroscientific data

    Frontiers in Neuroinformatics 3:3.

    https://doi.org/10.3389/neuro.11.003.2009

    • PubMed
    • Google Scholar
50. 1. Hardwick RM
    2. Rottschy C
    3. Miall RC
    4. Eickhoff SB

    (2013) A quantitative meta-analysis and review of motor learning in the human brain

    NeuroImage 67:283–297.

    https://doi.org/10.1016/j.neuroimage.2012.11.020

    • PubMed
    • Google Scholar
51. 1. Hebart MN
    2. Baker CI

    (2018) Deconstructing multivariate decoding for the study of brain function

    NeuroImage 180:4–18.

    https://doi.org/10.1016/j.neuroimage.2017.08.005

    • PubMed
    • Google Scholar
52. 1. Jamalabadi H
    2. Alizadeh S
    3. Schönauer M
    4. Leibold C
    5. Gais S

    (2016) Classification based hypothesis testing in neuroscience: below-chance level classification rates and overlooked statistical properties of linear parametric classifiers

    Human Brain Mapping 37:1842–1855.

    https://doi.org/10.1002/hbm.23140

    • PubMed
    • Google Scholar
53. 1. Jankowski J
    2. Scheef L
    3. Hüppe C
    4. Boecker H

    (2009) Distinct striatal regions for planning and executing novel and automated movement sequences

    NeuroImage 44:1369–1379.

    https://doi.org/10.1016/j.neuroimage.2008.10.059

    • PubMed
    • Google Scholar
54. 1. Johns MW

    (1991) A new method for measuring daytime sleepiness: the epworth sleepiness scale

    Sleep 14:540–545.

    https://doi.org/10.1093/sleep/14.6.540

    • PubMed
    • Google Scholar
55. 1. Karni A
    2. Meyer G
    3. Rey-Hipolito C
    4. Jezzard P
    5. Adams MM
    6. Turner R
    7. Ungerleider LG

    (1998) The acquisition of skilled motor performance: fast and slow experience-driven changes in primary motor cortex

    PNAS 95:861–868.

    https://doi.org/10.1073/pnas.95.3.861

    • PubMed
    • Google Scholar
56. 1. Kawai R
    2. Markman T
    3. Poddar R
    4. Ko R
    5. Fantana AL
    6. Dhawale AK
    7. Kampff AR
    8. Ölveczky BP

    (2015) Motor cortex is required for learning but not for executing a motor skill

    Neuron 86:800–812.

    https://doi.org/10.1016/j.neuron.2015.03.024

    • PubMed
    • Google Scholar
57. 1. King BR
    2. Hoedlmoser K
    3. Hirschauer F
    4. Dolfen N
    5. Albouy G

    (2017) Sleeping on the motor Engram: the multifaceted nature of sleep-related motor memory consolidation

    Neuroscience & Biobehavioral Reviews 80:1–22.

    https://doi.org/10.1016/j.neubiorev.2017.04.026

    • PubMed
    • Google Scholar
58. 1. Klein A
    2. Andersson J
    3. Ardekani BA
    4. Ashburner J
    5. Avants B
    6. Chiang MC
    7. Christensen GE
    8. Collins DL
    9. Gee J
    10. Hellier P
    11. Song JH
    12. Jenkinson M
    13. Lepage C
    14. Rueckert D
    15. Thompson P
    16. Vercauteren T
    17. Woods RP
    18. Mann JJ
    19. Parsey RV

    (2009) Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration

    NeuroImage 46:786–802.

    https://doi.org/10.1016/j.neuroimage.2008.12.037

    • PubMed
    • Google Scholar
59. 1. Korman M
    2. Raz N
    3. Flash T
    4. Karni A

    (2003) Multiple shifts in the representation of a motor sequence during the acquisition of skilled performance

    PNAS 100:12492–12497.

    https://doi.org/10.1073/pnas.2035019100

    • PubMed
    • Google Scholar
60. 1. Kornysheva K
    2. Diedrichsen J

    (2014) Human premotor areas parse sequences into their spatial and temporal features

    eLife 3:e03043.

    https://doi.org/10.7554/eLife.03043

    • PubMed
    • Google Scholar
61. 1. Kriegeskorte N
    2. Goebel R
    3. Bandettini P

    (2006) Information-based functional brain mapping

    PNAS 103:3863–3868.

    https://doi.org/10.1073/pnas.0600244103

    • PubMed
    • Google Scholar
62. 1. Kupferschmidt DA
    2. Juczewski K
    3. Cui G
    4. Johnson KA
    5. Lovinger DM

    (2017) Parallel, but dissociable, processing in discrete corticostriatal inputs encodes skill learning

    Neuron 96:476–489.

    https://doi.org/10.1016/j.neuron.2017.09.040

    • PubMed
    • Google Scholar
63. 1. Landry S
    2. Anderson C
    3. Conduit R

    (2016) The effects of sleep, wake activity and time-on-task on offline motor sequence learning

    Neurobiology of Learning and Memory 127:56–63.

    https://doi.org/10.1016/j.nlm.2015.11.009

    • Google Scholar
64. 1. Ledoit O
    2. Wolf M

    (2004) Honey, I shrunk the sample covariance matrix

    The Journal of Portfolio Management 30:110–119.

    https://doi.org/10.3905/jpm.2004.110

    • Google Scholar
65. 1. Lehéricy S
    2. Benali H
    3. Van de Moortele PF
    4. Pélégrini-Issac M
    5. Waechter T
    6. Ugurbil K
    7. Doyon J

    (2005) Distinct basal ganglia territories are engaged in early and advanced motor sequence learning

    PNAS 102:12566–12571.

    https://doi.org/10.1073/pnas.0502762102

    • PubMed
    • Google Scholar
66. 1. Makino H
    2. Ren C
    3. Liu H
    4. Kim AN
    5. Kondapaneni N
    6. Liu X
    7. Kuzum D
    8. Komiyama T

    (2017) Transformation of Cortex-wide emergent properties during motor learning

    Neuron 94:880–890.

    https://doi.org/10.1016/j.neuron.2017.04.015

    • PubMed
    • Google Scholar
67. 1. Miyachi S
    2. Hikosaka O
    3. Lu X

    (2002) Differential activation of monkey striatal neurons in the early and late stages of procedural learning

    Experimental Brain Research 146:122–126.

    https://doi.org/10.1007/s00221-002-1213-7

    • PubMed
    • Google Scholar
68. 1. Miyawaki Y
    2. Uchida H
    3. Yamashita O
    4. Sato MA
    5. Morito Y
    6. Tanabe HC
    7. Sadato N
    8. Kamitani Y

    (2008) Visual image reconstruction from human brain activity using a combination of multiscale local image decoders

    Neuron 60:915–929.

    https://doi.org/10.1016/j.neuron.2008.11.004

    • PubMed
    • Google Scholar
69. 1. Monchi O
    2. Petrides M
    3. Strafella AP
    4. Worsley KJ
    5. Doyon J

    (2006) Functional role of the basal ganglia in the planning and execution of actions

    Annals of Neurology 59:257–264.

    https://doi.org/10.1002/ana.20742

    • PubMed
    • Google Scholar
70. 1. Mumford JA
    2. Turner BO
    3. Ashby FG
    4. Poldrack RA

    (2012) Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses

    NeuroImage 59:2636–2643.

    https://doi.org/10.1016/j.neuroimage.2011.08.076

    • PubMed
    • Google Scholar
71. 1. Nambu A

    (2011) Somatotopic organization of the primate basal ganglia

    Frontiers in Neuroanatomy 5:.

    https://doi.org/10.3389/fnana.2011.00026

    • PubMed
    • Google Scholar
72. 1. Nambu I
    2. Hagura N
    3. Hirose S
    4. Wada Y
    5. Kawato M
    6. Naito E

    (2015) Decoding sequential finger movements from preparatory activity in higher-order motor regions: a functional magnetic resonance imaging multi-voxel pattern analysis

    European Journal of Neuroscience 42:2851–2859.

    https://doi.org/10.1111/ejn.13063

    • PubMed
    • Google Scholar
73. 1. Nettersheim A
    2. Hallschmid M
    3. Born J
    4. Diekelmann S

    (2015) The role of sleep in motor sequence consolidation: stabilization rather than enhancement

    Journal of Neuroscience 35:6696–6702.

    https://doi.org/10.1523/JNEUROSCI.1236-14.2015

    • PubMed
    • Google Scholar
74. 1. Nichols T
    2. Brett M
    3. Andersson J
    4. Wager T
    5. Poline JB

    (2005) Valid conjunction inference with the minimum statistic

    NeuroImage 25:653–660.

    https://doi.org/10.1016/j.neuroimage.2004.12.005

    • PubMed
    • Google Scholar
75. 1. Nili H
    2. Wingfield C
    3. Walther A
    4. Su L
    5. Marslen-Wilson W
    6. Kriegeskorte N

    (2014) A toolbox for representational similarity analysis

    PLoS Computational Biology 10:e1003553.

    https://doi.org/10.1371/journal.pcbi.1003553

    • PubMed
    • Google Scholar
76. 1. Omrani M
    2. Kaufman MT
    3. Hatsopoulos NG
    4. Cheney PD

    (2017) Perspectives on classical controversies about the motor cortex

    Journal of Neurophysiology 118:1828–1848.

    https://doi.org/10.1152/jn.00795.2016

    • PubMed
    • Google Scholar
77. 1. Orban P
    2. Peigneux P
    3. Lungu O
    4. Albouy G
    5. Breton E
    6. Laberenne F
    7. Benali H
    8. Maquet P
    9. Doyon J

    (2010) The multifaceted nature of the relationship between performance and brain activity in motor sequence learning

    NeuroImage 49:694–702.

    https://doi.org/10.1016/j.neuroimage.2009.08.055

    • PubMed
    • Google Scholar
78. 1. Peters L
    2. De Smedt B
    3. Op de Beeck HP

    (2015) The neural representation of arabic digits in visual cortex

    Frontiers in Human Neuroscience 9:.

    https://doi.org/10.3389/fnhum.2015.00517

    • PubMed
    • Google Scholar
79. 1. Pilgramm S
    2. de Haas B
    3. Helm F
    4. Zentgraf K
    5. Stark R
    6. Munzert J
    7. Krüger B

    (2016) Motor imagery of hand actions: decoding the content of motor imagery from brain activity in frontal and parietal motor areas

    Human Brain Mapping 37:81–93.

    https://doi.org/10.1002/hbm.23015

    • PubMed
    • Google Scholar
80. 1. Pinsard B
    2. Boutin A
    3. Doyon J
    4. Benali H

    (2018) Integrated fMRI preprocessing framework using extended Kalman filter for estimation of Slice-Wise motion

    Frontiers in Neuroscience 12:.

    https://doi.org/10.3389/fnins.2018.00268

    • PubMed
    • Google Scholar
81. 1. Rasch B
    2. Born J

    (2008) Reactivation and consolidation of memory during sleep

    Current Directions in Psychological Science 17:188–192.

    https://doi.org/10.1111/j.1467-8721.2008.00572.x

    • Google Scholar
82. 1. Reithler J
    2. van Mier HI
    3. Goebel R

    (2010) Continuous motor sequence learning: cortical efficiency gains accompanied by striatal functional reorganization

    NeuroImage 52:263–276.

    https://doi.org/10.1016/j.neuroimage.2010.03.073

    • PubMed
    • Google Scholar
83. 1. Robertson EM
    2. Tormos JM
    3. Maeda F
    4. Pascual-Leone A

    (2001) The role of the dorsolateral prefrontal cortex during sequence learning is specific for spatial information

    Cerebral Cortex 11:628–635.

    https://doi.org/10.1093/cercor/11.7.628

    • PubMed
    • Google Scholar
84. Conference

    1. Seabold S
    2. Perktold J

    (2010)

    Statsmodels: econometric and statistical modeling with python

    Proceedings of the 9th Python in Science Conference.

    • Google Scholar
85. 1. Shum J
    2. Hermes D
    3. Foster BL
    4. Dastjerdi M
    5. Rangarajan V
    6. Winawer J
    7. Miller KJ
    8. Parvizi J

    (2013) A brain area for visual numerals

    Journal of Neuroscience 33:6709–6715.

    https://doi.org/10.1523/JNEUROSCI.4558-12.2013

    • PubMed
    • Google Scholar
86. 1. Smith SM
    2. Nichols TE

    (2009) Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference

    NeuroImage 44:83–98.

    https://doi.org/10.1016/j.neuroimage.2008.03.061

    • PubMed
    • Google Scholar
87. 1. Tomassini V
    2. Jbabdi S
    3. Kincses ZT
    4. Bosnell R
    5. Douaud G
    6. Pozzilli C
    7. Matthews PM
    8. Johansen-Berg H

    (2011) Structural and functional bases for individual differences in motor learning

    Human Brain Mapping 32:494–508.

    https://doi.org/10.1002/hbm.21037

    • PubMed
    • Google Scholar
88. 1. Ungerleider LG
    2. Doyon J
    3. Karni A

    (2002) Imaging brain plasticity during motor skill learning

    Neurobiology of Learning and Memory 78:553–564.

    https://doi.org/10.1006/nlme.2002.4091

    • PubMed
    • Google Scholar
89. 1. Vahdat S
    2. Lungu O
    3. Cohen-Adad J
    4. Marchand-Pauvert V
    5. Benali H
    6. Doyon J

    (2015) Simultaneous Brain-Cervical cord fMRI reveals intrinsic spinal cord plasticity during motor sequence learning

    PLOS Biology 13:e1002186.

    https://doi.org/10.1371/journal.pbio.1002186

    • PubMed
    • Google Scholar
90. 1. Vahdat S
    2. Fogel S
    3. Benali H
    4. Doyon J

    (2017) Network-wide reorganization of procedural memory during NREM sleep revealed by fMRI

    eLife 6:e24987.

    https://doi.org/10.7554/eLife.24987

    • PubMed
    • Google Scholar
91. 1. Varoquaux G

    (2018) Cross-validation failure: small sample sizes lead to large error bars

    NeuroImage 180:.

    https://doi.org/10.1016/j.neuroimage.2017.06.061

    • PubMed
    • Google Scholar
92. 1. Verwey WB
    2. Shea CH
    3. Wright DL

    (2015) A cognitive framework for explaining serial processing and sequence execution strategies

    Psychonomic Bulletin & Review 22:54–77.

    https://doi.org/10.3758/s13423-014-0773-4

    • PubMed
    • Google Scholar
93. 1. Walther A
    2. Nili H
    3. Ejaz N
    4. Alink A
    5. Kriegeskorte N
    6. Diedrichsen J

    (2016) Reliability of dissimilarity measures for multi-voxel pattern analysis

    NeuroImage 137:188–200.

    https://doi.org/10.1016/j.neuroimage.2015.12.012

    • PubMed
    • Google Scholar
94. 1. Waters S
    2. Wiestler T
    3. Diedrichsen J

    (2017) Cooperation not competition: bihemispheric tDCS and fMRI show role for ipsilateral hemisphere in motor learning

    The Journal of Neuroscience 37:7500–7512.

    https://doi.org/10.1523/JNEUROSCI.3414-16.2017

    • PubMed
    • Google Scholar
95. 1. Waters-Metenier S
    2. Husain M
    3. Wiestler T
    4. Diedrichsen J

    (2014) Bihemispheric transcranial direct current stimulation enhances effector-independent representations of motor synergy and sequence learning

    The Journal of Neuroscience 34:1037–1050.

    https://doi.org/10.1523/JNEUROSCI.2282-13.2014

    • PubMed
    • Google Scholar
96. 1. Wiestler T
    2. McGonigle DJ
    3. Diedrichsen J

    (2011) Integration of sensory and motor representations of single fingers in the human cerebellum

    Journal of Neurophysiology 105:3042–3053.

    https://doi.org/10.1152/jn.00106.2011

    • PubMed
    • Google Scholar
97. 1. Wiestler T
    2. Waters-Metenier S
    3. Diedrichsen J

    (2014) Effector-independent motor sequence representations exist in extrinsic and intrinsic reference frames

    Journal of Neuroscience 34:5054–5064.

    https://doi.org/10.1523/JNEUROSCI.5363-13.2014

    • PubMed
    • Google Scholar
98. 1. Wiestler T
    2. Diedrichsen J

    (2013) Skill learning strengthens cortical representations of motor sequences

    eLife 2:e00801.

    https://doi.org/10.7554/eLife.00801

    • PubMed
    • Google Scholar
99. 1. Wu T
    2. Kansaku K
    3. Hallett M

    (2004) How self-initiated memorized movements become automatic: a functional MRI study

    Journal of Neurophysiology 91:1690–1698.

    https://doi.org/10.1152/jn.01052.2003

    • PubMed
    • Google Scholar
100. 1. Yin HH
     2. Mulcare SP
     3. Hilário MR
     4. Clouse E
     5. Holloway T
     6. Davis MI
     7. Hansson AC
     8. Lovinger DM
     9. Costa RM

     (2009) Dynamic reorganization of striatal circuits during the acquisition and consolidation of a skill

     Nature Neuroscience 12:333–341.

     https://doi.org/10.1038/nn.2261

     • PubMed
     • Google Scholar
101. 1. Yokoi A
     2. Arbuckle SA
     3. Diedrichsen J

     (2018) The role of human primary motor cortex in the production of skilled finger sequences

     The Journal of Neuroscience 38:1430–1442.

     https://doi.org/10.1523/JNEUROSCI.2798-17.2017

     • PubMed
     • Google Scholar
102. 1. Yousry TA
     2. Schmid UD
     3. Alkadhi H
     4. Schmidt D
     5. Peraud A
     6. Buettner A
     7. Winkler P

     (1997) Localization of the motor hand area to a knob on the Precentral Gyrus. A new landmark

     Brain 120:141–157.

     https://doi.org/10.1093/brain/120.1.141

     • PubMed
     • Google Scholar

Thank you for submitting your article "Consolidation alters motor sequence-specific distributed representations" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Timothy Verstynen as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Rich Ivry as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Atsushi Yokoi (Reviewer #2).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

The present work examines motor skill learning by using multi-voxel pattern analysis to compare patterns of the brain activity elicited by two well-trained sequences that were consolidated by at least one overnight sleep and two novel sequences. The key findings were that while there was some overlap between brain regions in which the two learning stages of sequences were represented (e.g.., bilateral premotor and parietal regions), there were regions where the sequences of either one of the stages were more strongly represented, especially around the sub-cortical structures. These sub-cortical structures included, for example, some parts of bilateral hippocampi, ipsilateral cerebellar cortex, and ipsilateral caudate, where the new sequences were more strongly represented. On the other hand, in the areas including bilateral sensorimotor putamen and thalamus the consolidated sequences were more strongly represented, suggesting that neural substrate storing sequence information drastically changes over the early consolidation process.

Essential revisions:

All three reviewers highlighted major concerns that fall under seven general themes.

1) Performance or representational differences?

The behavioral results clearly show that the performance (both speed and accuracy) is different between the trained and untrained sequences. Thus, the differences in multivariate patterns could be driven by differences in performance rather than differences in encoding. There is (at least) one way to look at this issue: The behavioral performance shown in Figure 1 reveals that performance for the untrained sequences steadily improves across runs and appears to reach an asymptote half-way through the experiment. If the difference in multivariate patterns is truly related to long-term consolidation, as opposed to being the consequent result of changes in performance, then comparing the last 8 blocks of the trained vs. untrained sequences should replicate the key results (i.e., Figure 3). In contrast, comparing the last 8 blocks of the untrained sequences to the first 8 blocks of the of the untrained sequences should not reveal a similar set of clusters. However, if this split-half comparison on the untrained sequences produces qualitatively similar maps as the trained vs. untrained comparison, then it would strongly suggest that the differences in multivariate distances is driven largely by performance effects.

2) Elaboration of representational distances.

The true power of the RSA approach is that you can directly measure the distinctiveness of representations across conditions. Yet it is used mainly here as an alternative to traditional decoding methods. It would be nice for the authors to show the representational distances across sequences in key regions (e.g., cortical motor clusters, striatum, cerebellum). This would give the reader a sense of how training may be altering sequence-related representations.

3) Distinguishing between signal amplitude and representational discriminability.

The map of clusters that discriminate between any of the four sequences (Figure 2), reveals a pretty standard sensorimotor network. Is RSA discriminability really driven by regions with significant task-related BOLD responses as estimated from traditional univariate GLM maps (in contrast to true differences in the covariance pattern of local voxels)? How well does the discriminability of the searchlight correlate with local task-related activity maps from univariate GLM? If they are correlated, how can you distinguish between searchlight results just being the result of local signal-to-noise differences from results driven by true differences in encoding?

Related to this was concern that, even after accounting for the potential first-finger effect, there remains potential differences in overall activity levels across the new and old sequences. Can this be addressed, especially given the differences in behavioural performance between new and consolidated sequences. How much were the overall activities different across the blocks and sequences?). Note that the pair-wise dissimilarities between fingers changed quite a bit in different activation levels (= tapping frequencies) (see Figure 4A), indicating that the direct comparison between thumb/index distance in one tapping frequency and index/little one in another frequency would not be meaningful. The authors could try, for instance, using multivariate pattern angle, which is less affected by pattern scaling (Walther et al., 2016), or assessing the XOR (disjunctive union) of the prevalence of cross-validated distances for the consolidated and the new sequences, avoiding the direct comparison between them.

4) Elaboration of learning mechanisms & dynamics.

The authors make an extensive effort in the Introduction and Discussion sections to try to link these results to hippocampus. However, there is not a direct assessment of the role of the hippocampus to the rest of the network. There is only the observation of a cluster in each hippocampus that reliably distinguishes between the trained and untrained sequences, not an analysis that shows this is driving the rest of the changes in encoding in other areas.

5) Issues with behavioral data:

One reviewer noted that you have referenced a recent paper that shows movement rate differences that are performed with a single digit do not appreciably alter classification results. However, there are likely speed accuracy tradeoff differences between the samples, which may bias classification comparisons.

Moreover, there is no evidence here for consolidation. While there is a significant difference between condition for the trained and novel sequences, the trained were reactivated prior to imaging. Might the advantage for trained have come from the warm-up session (which was not given for the novel)? Performance for the novel sequences becomes similar to the TSeqs after block 12, which corresponds to 60 trials of practice for either novel sequence. The performance distinction between conditions may be entirely driven by the warmup period. What did performance look like for the trained sequences at the very start of this reactivation/warmup period?

6) Controlling for first-finger effects.

As you note in the Discussion section, the pattern discriminability between the sequences starting with different fingers might reflect a "first-finger effect", where the discriminability of two sequences is almost solely driven by which finger was moved the first, not by the sequential order per se. This also applies to the pattern dissimilarity between the two new sequences (1-2-4-3-1 and 4-1-3-2-4) which, in contrast to the two consolidated sequences that had the same first finger (1-4-2-3-1, and 1-3-2-4-1), had different first fingers. Without accounting for this potential confound, the comparison made between the "new/new" and the "old/old" dissimilarities is hard to interpret, as it is unclear whether we are comparing between genuine sequence representations, or between sequence representation and individual finger representation.

7) Clarification of methods.

How did participants know that they made an error? Were they explicitly told a key press was incorrect? Or did they have to self-monitor performance during training? Was the same button box apparatus used during scanning as in training? Was the presentation of sequence blocks counterbalanced during scanning? How many functional runs were performed? Is the classification performance different between runs?

Additional detail is needed regarding the correction of motion artefact. In subsection “MRI data preprocessing”, the authors state that BOLD signal was further processed by detecting intensity changes corresponding to "large motion". What additional processing was performed? What was considered large motion? Was motion DOFs included in the GLM? Are there differences in motion between sequences and between sequence conditions? More information is needed on the detrending procedure. Is there evidence that detrending worked similarly between the different time windows between spikes? How were spikes identified? What happened to the spikes? In general, what software was used for the imaging analysis? Just PyMVPA?

The description of the analysis in subsection “Reorganization of the distributed sequence representation after memory consolidation” should be reworded. Is this describing how you analyze the difference in the consolidated and novel sequence discriminability maps? But how is this a conjunction? A conjunction reflects the overlap between 2 contrasts, and in this case what we are looking at is a difference. Related to this, there are different types of conjunctions. Please provide more details, as conjunctions can inflate significance. What software was used and how were the thresholds set for the conjunction?

[Editors' note: further revisions were requested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Consolidation alters motor sequence-specific distributed representations" for further consideration at eLife. Your revised article has been favorably evaluated by Richard Ivry (Senior Editor), Tim Verstynen (Reviewing Editor), and two reviewers.

The manuscript has been improved but there are some remaining issues that need to be addressed. While we like to minimize back and forth in the review process, we believe the first two issues are critical here, especially the first, in establishing the robustness of your effects.

Essential revisions:

1) Concern that the statistical tests are too liberal. The contrasts appear to be global null conjunction tests. There has been significant debate regarding the usage of global null conjunction in fMRI research (see commentary on conjunctions involving papers by Nichols and Friston, 2006). Conjunctions of the global null do not require that activation for both (or however many) individual maps supplied to the conjunction to be individually significant, and as result, small effects can be amplified. Further, most often in the literature conjunctions are performed between multiple differential condition contrasts (e.g., CondA>CondB with CondA>CondC), and not (CondA>CondB with CondA>baseline). The primary concern here is with the contrast involving differences between consolidated and novel sequences. The authors test: (consolidated > new) ∧ (consolidated > 0); and then for new: (new > consolidated) ∧ (new > 0). It is unclear why contrasts relative to baseline are integrated in the contrast. We recognize that there is reason to want to correct for effects that are positive, but a more parsimonious version of this would be to use a liberal mask for positive effects, using the contrast (consolidated > new), where there is no risk of inflating statistics. Instead, by incorporating the baseline contrast in the conjunction the effect is driven by (consolidated > 0), and not the more subtle effects that are of interest (e.g., consolidated > new). It is critical that the authors demonstrate the effects are present without the baseline conjunction test. Again, there is no need for a conjunction. Simple contrasts (consolidated > new, masked by consolidated > 0) and vice versa seem to be a preferable solution and we would like to see this in lieu of the conjunction analysis.

2) In the retest session before the MVPA session, is there an overnight gain in performance (i.e., gain in performance measure between the last trial of previous day and the first trial of the next day) in the two trained sequences? This is the typical behavioural evidence for motor memory consolidation (e.g., Stickgold, 2005), and we think it should be incorporated here.

3) The consolidated sequences were not learned at the same time and not subject to the same consolidation and reconsolidation period. These differences could ultimately bias the classification results for the consolidated sequences. Tseq1 was practiced on Day1, and then tested on Day2. Tseq2 was then immediately practiced after Tseq1. Tseq1 and Tseq2 were tested in the scanner protocol on Day3. There are 2 problems. First, Tseq1 was consolidated over 2 nights of sleep, versus 1 for Tseq2. Because of this, any difference in neural representation could be due to the simple age and consolidation history of the memory. Second, exposure to a new sequence (Tseq2) immediately following the reactivation of Tseq1 can interfere with the reconsolidation of Tseq1. Evidence from recent studies of motor reconsolidation have shown that exposure to similar manipulation can significantly alter the memory trace for the reactivated, previously consolidated memory (Tseq1). Essentially what is left is a classification between a sequence that was consolidated and reconsolidated vs. a sequence that was consolidated. It is unclear why the authors did not train both of the sequences at the same time particularly because they are testing them as such. This limitation should be addressed.

4) Regarding the performance issue (i.e., improvement of new sequences during the MVPA session): Critically, in their response, the authors argued that the inconsistency of Author response image 1 to Figure 3 is due to the lack of statistical power because of using only one cross-validation fold. However, at the same time, they argued that the inconsistency of Author response image 2 (the contrast of distances between the first and the second run) to Figure 3 is the supporting evidence that the result presented in Figure 3 reflects consolidation, despite that these estimates were also computed using only one cross-validation fold. If they say Author response image 1 is unreliable because of using single fold, then what is presented in the Author response image 2 should also be unreliable for the very same reason. Ideally, the new sequences should have practiced well until their performance and underlying representations reached to plateau before going into the scanner, as the initial rapid change in the representation would essentially have little thing to do with consolidation process. It is still true that the distance estimate computed through the 4 partitions would be significantly biased if there is some inconsistency in pattern difference in any of the partitions due to the ongoing performance improvement. Therefore, caution is required to conclude that the difference highlighted in Figure 3 can be attributed specifically to the consolidation. The authors should discuss the potential impact of the sub-optimal estimate of the plateau representation for the new sequences.

Essential revisions:

All three reviewers highlighted major concerns that fall under seven general themes.

1) Performance or representational differences?

The behavioral results clearly show that the performance (both speed and accuracy) is different between the trained and untrained sequences. Thus, the differences in multivariate patterns could be driven by differences in performance rather than differences in encoding. There is (at least) one way to look at this issue: The behavioral performance shown in Figure 1 reveals that performance for the untrained sequences steadily improves across runs and appears to reach an asymptote half-way through the experiment. If the difference in multivariate patterns is truly related to long-term consolidation, as opposed to being the consequent result of changes in performance, then comparing the last 8 blocks of the trained vs. untrained sequences should replicate the key results (i.e., Figure 3). In contrast, comparing the last 8 blocks of the untrained sequences to the first 8 blocks of the of the untrained sequences should not reveal a similar set of clusters. However, if this split-half comparison on the untrained sequences produces qualitatively similar maps as the trained vs. untrained comparison, then it would strongly suggest that the differences in multivariate distances is driven largely by performance effects.

The reviewers are right that behavioral differences between trained and untrained task are an inherent limitation common to most design investigating learning, and particularly the fact that performances of an untrained task tend to improve while being measured.

To further support our results, however, we conducted the suggested analyses:

The contrast between trained and untrained sequence distances in the last run (i.e. when performance for both sequences reached an asymptote) resulted in significant differences illustrated in Author response image 1 which only partly replicates the results of Figure 3. Notably only contralateral putamen representation is found significantly different, while no significant difference is found in the hippocampus. Among the potential factors that could have influenced these results, is the important reduction in statistical power induced by going from a 6-folds to a 1-fold measure.

Author response image 1

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Contrasting untrained sequence across runs, provided results (see Author response image 2) distinct from that displayed in Figure 3, in that pattern difference was weaker in the second run as compared to the first in a group of regions including the anterior parts of the caudate bilaterally as well as the contralateral putamen and dorsal prefrontal cortex. While being out of the scope of the present manuscript and conjectural, these results could reflect the quickly decreasing implication of the anterior cortico-striatal attentional processes in the initial “fast-learning” phase of MSL.

Author response image 2

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Therefore, these analyses support the fact that our results reflect stable pattern differences across the whole session, despite the ongoing acquisition of the untrained sequences and inherent changes in behavioral performance.

Moreover, the use of cross-validated Mahalanobis distance between patterns evoked by the production of sequence at the same stage of learning does have advantages over classification measures regarding potential behavioral bias. More precisely, it measures how these pattern differences within splits are stable across splits of the data. Thus, pattern differences have to be consistently present within each split in which behavioral measures of the untrained sequences do not differ. It also has to be noted that among the 6 folds of the cross-validation, 4 are across the 2 runs, and thus assess which patterns differences are stable across runs rather independently of the abovementioned behavioral changes. Taken together, we thus think that the stable pattern difference observed reflects true encoding of persistent sequential information.

2) Elaboration of representational distances.

The true power of the RSA approach is that you can directly measure the distinctiveness of representations across conditions. Yet it is used mainly here as an alternative to traditional decoding methods. It would be nice for the authors to show the representational distances across sequences in key regions (e.g., cortical motor clusters, striatum, cerebellum). This would give the reader a sense of how training may be altering sequence-related representations.

We agree with the reviewer that the present study uses RSA as a continuous alternative to classification-based decoding. As a matter of fact, our study was initially designed for the latter, but RSA was then used to avoid the bias and limitation of classification. As such, we only included two sequences per condition (consolidated/new) resulting in one distance per condition, and this prevents us from investigating the geometry of representations.

If we understand the reviewer’s comment, the addition of ROIs distance matrix in results would benefit the present study. However, in this 4x4 matrix, only the two within-condition measures which are presented on brain surface is interpretable. We replaced the statistical measure in the maps by the group-average cross-validated Mahalanobis distance. We hope that this modification provides a better sense of the changes in sequence representations.

3) Distinguishing between signal amplitude and representational discriminability.

The map of clusters that discriminate between any of the four sequences (Figure 2), reveals a pretty standard sensorimotor network. Is RSA discriminability really driven by regions with significant task-related BOLD responses as estimated from traditional univariate GLM maps (in contrast to true differences in the covariance pattern of local voxels)? How well does the discriminability of the searchlight correlate with local task-related activity maps from univariate GLM? If they are correlated, how can you distinguish between searchlight results just being the result of local signal-to-noise differences from results driven by true differences in encoding?

The reviewers are right that the clusters found in Figure 2 are partly consistent with univariate results, and it is expected that representation of information relevant to sequence production are often present in a subset of the regions that are significantly activated by the task. In a sense, any RSA result is a ratio of the strength or variance that enables discriminating the information of interest over the level of noise.

As suggested, we conducted a GLM analysis on the activity evoked during motor sequence production and the resulting map (see Author response image 3) reproduces a well-known motor network, but it does not exactly correlate with the results of Figure 2. Notably, subcortical activity common to both conditions does not result in common level of sequence representation.

The main effect GLM analysis was added in the supplementary material and the following sentence was added to the manuscript (subsection “A common distributed sequence representation irrespective of learning stage”): “To complement this result we extracted the main effect of activation during this task (Figure 1—figure supplement 3), showing that pattern distance do not exactly correspond to the regions significantly activated in mass-univariate GLM analysis.”

When contrasting consolidated and new sequence activity during execution, no result survived FDR correction, and thus, our results do not seem to result from large-scale activity scaling over the extent of each searchlight. It has to be noted that our data were not smoothed during preprocessing, and that this might have prevented the assessment of large-scale activity differences between conditions recruiting similar networks. Nonetheless, this approach allowed us to measure fine-grained pattern differences.

The following sentence was added to the manuscript (subsection “Reorganization of the distributed sequence representation after memory consolidation”): “We also conducted a univariate GLM analysis to contrast the consolidated and new sequences, but no results survived multiple comparison correction. This negative result could be accounted by the absence of smoothing of signals during preprocessing.”

Author response image 3

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Related to this was concern that, even after accounting for the potential first-finger effect, there remains potential differences in overall activity levels across the new and old sequences. Can this be addressed, especially given the differences in behavioural performance between new and consolidated sequences. How much were the overall activities different across the blocks and sequences?). Note that the pair-wise dissimilarities between fingers changed quite a bit in different activation levels (= tapping frequencies) (see Figure 4A), indicating that the direct comparison between thumb/index distance in one tapping frequency and index/little one in another frequency would not be meaningful. The authors could try, for instance, using multivariate pattern angle, which is less affected by pattern scaling (Walther et al., 2016), or assessing the XOR (disjunctive union) of the prevalence of cross-validated distances for the consolidated and the new sequences, avoiding the direct comparison between them.

Following the reviewer’s suggestion, we thresholded the contrast map with the following xor test: ((Consolidated>0)⊕(New>0))∩(Consolidated>New)) which is presented in Author response image 4. Interestingly, the results are similar to that of Figure 3. It thus appears that the changes we reported are less likely to result from the scaling of the “pattern-to-noise” ratio.

While not being certain, we here assume the reviewer is referring to Arbuckle et al., 2018, when mentioning Figure 4A. We agree that their results show a dissimilarity scaling in relation the frequency of single-finger tapping that can alternatively be explained by the number of movements/key presses per trial (Figure 1). While they manipulated frequency in their study, it is probable that the number of movements is scaling the neuronal activity and, indirectly, the BOLD signal, which results in an increased signal-to-noise ratio. In our protocol, the number of key presses was fixed to be the same across all trials. However, their frequency significantly evolved for the untrained sequences, and remained significantly different from the frequency of the trained sequences across the task. Even if this difference does not amount to doubling frequency as in (Arbuckle et al., 2018), it could still influence our results, notably during the first run where changes are more pronounced. Contrary to Arbuckle et al., (2018), trial lengths in the present study were inversely related to the tapping frequency, and thus, patterns of slower untrained sequences average slightly more TR than patterns of trained sequences. Longer trials should therefore benefit from increased signal-to-noise ratio. In sum, there are many co-varying behavioral and statistical factors that can influence RSA measures. The Arbuckle article is a very interesting step in characterizing these.

Author response image 4

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4) Elaboration of learning mechanisms & dynamics.

The authors make an extensive effort in the Introduction and Discussion sections to try to link these results to hippocampus. However, there is not a direct assessment of the role of the hippocampus to the rest of the network. There is only the observation of a cluster in each hippocampus that reliably distinguishes between the trained and untrained sequences, not an analysis that shows this is driving the rest of the changes in encoding in other areas.

The approach that we adopted is a data-driven mapping technique that explores the localized representations of information using an alternative metric and, as such, does not model interactions between regions. The results revealed the stronger implication of the hippocampus in the initial learning phase, a finding that we then discussed in relation to previous studies with more conventional GLM-based and connectivity measures.

We removed references to our results as a network, as we are not conducting any kind of network analysis with connectivity measures. We hope that this clarification improves the reader's understanding of our approach and answers the reviewers’ concerns.

5) Issues with behavioral data:

One reviewer noted that you have referenced a recent paper that shows movement rate differences that are performed with a single digit do not appreciably alter classification results. However, there are likely speed accuracy tradeoff differences between the samples, which may bias classification comparisons.

Moreover, there is no evidence here for consolidation. While there is a significant difference between condition for the trained and novel sequences, the trained were reactivated prior to imaging. Might the advantage for trained have come from the warm-up session (which was not given for the novel)? Performance for the novel sequences becomes similar to the TSeqs after block 12, which corresponds to 60 trials of practice for either novel sequence. The performance distinction between conditions may be entirely driven by the warmup period. What did performance look like for the trained sequences at the very start of this reactivation/warmup period?

We agree with the reviewers that our results could be confounded by the retest of trained sequences, which were performed prior to the task of interest in the present work. However, the statistics show that the sequence duration is lower for trained sequences as compared to new sequences throughout the two runs. It is true that, visually in Figure 1, the gap seems to be closed around block #12. However, the remaining difference is consistent with MSL literature that shows small marginal gains during consolidation as compared to the improvement observed during training. While Figure 1 shows the between-subject variance with error-bars, the mixed-effect model approach does take separately into account between-subject variance, and as such, within-subject differences between conditions are assessed (see supplementary materials).

Following the reviewers’ suggestion, we extracted the mean duration of the first 5 sequences (over the 12 sequences) of each block during the retest of trained sequences. This was done in order to make this measure more comparable to the MVPA task, as higher speed is facilitated by longer blocks. We then plotted the sequences (see Author response image 5) and performed statistical comparison of the first block mean duration between trained and new sequences. The analyses revealed that the trained sequences show some warm-up during the first blocks of retest, yet the speed in the first block is significantly greater (β = -.431, SE = 0.090, p < 10^-5) than the speed of new sequences at the beginning of MVPA task. Therefore, we believe that consolidation of the trained sequences did occur. We added the following sentence to integrate these results in the manuscript (subsection “Behavioral performance”): “To verify that behavioral differences could not be accounted by the preceding retest of trained sequences, we compared the sequence duration of the first 5 sequences of the retest blocks to the duration of the untrained sequences during the present task. The values reported in supplementary material (Figure 1—figure supplement 1), show that the trained sequence are still performed faster than the untrained sequences (β = -.431, SE = 0.090, p < 10^-5).

Author response image 5

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6) Controlling for first-finger effects.

As you note in the Discussion section, the pattern discriminability between the sequences starting with different fingers might reflect a "first-finger effect", where the discriminability of two sequences is almost solely driven by which finger was moved the first, not by the sequential order per se. This also applies to the pattern dissimilarity between the two new sequences (1-2-4-3-1 and 4-1-3-2-4) which, in contrast to the two consolidated sequences that had the same first finger (1-4-2-3-1, and 1-3-2-4-1), had different first fingers. Without accounting for this potential confound, the comparison made between the "new/new" and the "old/old" dissimilarities is hard to interpret, as it is unclear whether we are comparing between genuine sequence representations, or between sequence representation and individual finger representation.

The reviewers are right that this is a limitation for part of the current results, as discussed in the original manuscript (now subsection “A distributed representation of finger motor sequence”). However, the design of the present task is different of the one that uncovered this effect in that each sequence is executed separately and the execution is uninterrupted. This should theoretically reduce considerably this initiating effect. Moreover, the main results of the present paper are the differences in striatal and hippocampal representations, and these regions are not expected to show strong and reliable somatotopy for individual finger movements. Therefore, we do not expect a pattern associated with the first finger in these regions. While this effect is known to impact the motor cortical regions, it would be expected to bias positively the new sequences discriminability, but no cortical regions show higher discriminability than for consolidated sequences. At most, this effect could have caused some false-negative results in the contrast between conditions. As such, we still believe that our results are of interest for shedding light in our understanding of the neural substrate mediating motor sequence learning.

7) Clarification of methods.

How did participants know that they made an error? Were they explicitly told a key press was incorrect? Or did they have to self-monitor performance during training? Was the same button box apparatus used during scanning as in training? Was the presentation of sequence blocks counterbalanced during scanning? How many functional runs were performed? Is the classification performance different between runs?

No feedback was provided to the participant regarding their performance. Prior to the practice period, they were given the instruction that if they realized that they made an error, they should not try to correct it but instead to restart practicing from the beginning of the sequence. All training and testing took place in the scanner with the same apparatus (subsection “Procedures and tasks”). The order of sequence blocks was determined by DeBruijn cycles that allow to counterbalance the temporal successions of conditions across each run (subsection “Procedures and tasks”). Two functional runs were performed and each run contained two repetitions of the DeBruijn counterbalancing and were thus split to get four folds of cross-validation. The “classification performance” or rather pattern discriminability was not contrasted between runs as it was not the scope of the present study, but representational changes could occur for untrained sequences in a subset of regions.

As we’ve now come to realise that the information pertaining to error feedback was not explicitly described in the original manuscript, the following sentences have been added to subsection “Procedures and tasks”: "No feedback was provided to the participant regarding their performance. Prior to the practice period, they were given instructions that if they realized they made an error, they should not try to correct it but instead restart practicing from the beginning of the sequence."

Additional detail is needed regarding the correction of motion artefact. In subsection “MRI data preprocessing”, the authors state that BOLD signal was further processed by detecting intensity changes corresponding to "large motion". What additional processing was performed? What was considered large motion? Was motion DOFs included in the GLM? Are there differences in motion between sequences and between sequence conditions? More information is needed on the detrending procedure. Is there evidence that detrending worked similarly between the different time windows between spikes? How were spikes identified? What happened to the spikes? In general, what software was used for the imaging analysis? Just PyMVPA?

We agree that the manuscript would benefit from a more detailed account of the motion artefact correction.

The motion artifact removal was performed independently from the GLM regression. A detrending approach designed to avoid introducing long-range temporal correlation that are detrimental to cross-validation was adopted. The description of the detrending procedure was extended with the following sentence (subsection “MRI data preprocessing”):

"To do so, the whole-brain average absolute value of the BOLD signal derivative was computed and used as a measure to detect widespread signal changes due to large motion. We proceeded by recursively detecting the time corresponding to the largest global signal change to separate the run data, then detected the second largest change, etc. Once the run data was chunked into periods of stable position constrained to be at least 32 sec. long, we performed detrending in each chunk separately and concatenated the chunk back together. The rationale behind this procedure is that large movements change the pattern of voxelwise signal trend, and detrending or regression of movement parameters across the whole run can thus induce spurious temporal auto-correlation in presence of baseline shift. This step was implemented using Python and PyMVPA."

The description of the analysis in subsection “Reorganization of the distributed sequence representation after memory consolidation” should be reworded. Is this describing how you analyze the difference in the consolidated and novel sequence discriminability maps? But how is this a conjunction? A conjunction reflects the overlap between 2 contrasts, and in this case what we are looking at is a difference. Related to this, there are different types of conjunctions. Please provide more details, as conjunctions can inflate significance. What software was used and how were the thresholds set for the conjunction?

We agree with the reviewers that this sentence would benefit from a clarification. The contrast map was thresholded by the union of two conjunctions

((Consolidate>New)∩(Consolidated>0))∪((New>Consolidated)∩(New>0)). Each of these subtests was performed on the p-value resulting from non-parametric permutation testing with p-values thresholded at.05. As we are using logical conjunctions or minimum statistics, no inflation is expected. The permutation testing was implemented in Python.

We rephrased it as such (subsection “Reorganization of the distributed sequence representation after memory consolidation”): "The resulting map thus represents the subset of voxels that fulfill the union of two logical conjunctions (Consolidated>New)∩(Consolidated>0))∪((New>Consolidated)∩(New>0)) with each sub-test being assessed by thresholding the p-value of individual non-parametric permutation tests."

[Editors' note: further revisions were requested prior to acceptance, as described below.]

Essential revisions:

1) Concern that the statistical tests are too liberal. The contrasts appear to be global null conjunction tests. There has been significant debate regarding the usage of global null conjunction in fMRI research (see commentary on conjunctions involving papers by Nichols and Friston, 2006). Conjunctions of the global null do not require that activation for both (or however many) individual maps supplied to the conjunction to be individually significant, and as result, small effects can be amplified. Further, most often in the literature conjunctions are performed between multiple differential condition contrasts (eg., CondA>CondB with CondA>CondC), and not (CondA>CondB with CondA>baseline). The primary concern here is with the contrast involving differences between consolidated and novel sequences. The authors test: (consolidated > new) ∧ (consolidated > 0); and then for new: (new > consolidated) ∧ (new > 0). It is unclear why contrasts relative to baseline are integrated in the contrast. We recognize that there is reason to want to correct for effects that are positive, but a more parsimonious version of this would be to use a liberal mask for positive effects, using the contrast (consolidated > new), where there is no risk of inflating statistics. Instead, by incorporating the baseline contrast in the conjunction the effect is driven by (consolidated > 0), and not the more subtle effects that are of interest (e.g., consolidated > new). It is critical that the authors demonstrate the effects are present without the baseline conjunction test. Again, there is no need for a conjunction. Simple contrasts (consolidated > new, masked by consolidated > 0) and vice versa seem to be a preferable solution and we would like to see this in lieu of the conjunction analysis.

We agree with the reviewer that our use of the term conjunction may have been misleading, and that testing against global-null should be avoided. In fact, in our analyses, we did not use the global-null conjunction approach (Friston et al., 1999) but used instead the more conservative approach that was later proposed to provide nominal false-positive rate (Nichols et al., 2005) that equates to masking. Therefore, each test (contrast and simple effect) are individually significant, as assessed through permutation. We thus added the reference to Nichols et al., 2005 in the manuscript (subsection “Reorganization of the distributed sequence representation after memory consolidation”, subsection “Statistical testing”) and mentioned that it is equivalent to masking (“This procedure equals to mask the contrast with significantly positive simple effects.”, subsection “Reorganization of the distributed sequence representation after memory consolidation”; “For the contrast between consolidated and new sequences we applied a minimum-statistic conjunction test (Nichols et al. 2005) (equivalent to masking positive and negative differences with the respective simple effect) to assess that the differences found were supported by statistically significant above zero Mahalanobis distances.”, subsection “Statistical testing”) in order to clarify our use of the conjunction analysis.

2) In the retest session before the MVPA session, is there an overnight gain in performance (i.e., gain in performance measure between the last trial of previous day and the first trial of the next day) in the two trained sequences? This is the typical behavioural evidence for motor memory consolidation (e.g., Stickgold, 2005), and we think it should be incorporated here.

We agree with the reviewers that consolidation of MSL if often assessed by offline gains in performance. However, many factors can influence the appearance of these gains (Pan and Rickard, 2015). First, among these factors, the number of blocks taken into account at the end of training and beginning of retest can largely influence the amount of gains. The majority of studies that revealed such gains have used performance based upon 3 or 4 blocks in order to reduce variance, and thus counteract the transiently deteriorated performance often seen at the beginning of retest caused by the “warm-up” phenomenon. In our study, performance in the post-sleep retest session of the trained sequence was measured using one-block of 12 sequences only, and the latter was compared to the last block of training in order to compare apples with apples. Using such a design, no spontaneous gains in performance was observed. Importantly, however, no loss in performance was observed either the next day, hence suggesting evidence of consolidation. In fact, it has been theoretically argued that consolidation can be assessed by retention as opposed to the performance decay observed when no sleep is allowed (Nettersheim et al., 2015); a pattern of results that was found here in our study. Second, our results show a persistent performance advantage for the consolidated sequences compared to new ones. In our opinion, this isolates even better the sequence learning consolidation effect from other generalizable components of the task, which can be learned on the first day and transferred to the new sequence. This also allows to control for many factors (fatigue, circadian cycle, etc.) that modulates the memory and motor performances in sleep studies. Thus, while our way of measuring consolidation deviates from the common approach in the field, including in studies from our group, we think that the participants clearly showed evidence of consolidation of the trained sequences. In sum, we support the idea that the consolidation of motor memory does not requires gains but rather retention of performance level attained at the end of the training (similarly to what occurs with declarative memory), and that comparing performance of consolidated knowledge to that of new one at the same point in time does have benefit in controlling many experimental factors.

As a comparison to previous studies, we tested whether the mean correct sequence duration of the last 2 blocks of the training sessions differed from the first 2 blocks of the retest sessions on day 3 (fix-effect:pre-post, random-effect:sequence), and the result was significant (β =−0.086, SE=0.023, p=.00014).

We added the following sentences to the behavioral Results section: “While most studies in the field have measured retention and even offline gains between the end of training and the beginning of a post-sleep retest, we chose here to measure consolidation as the difference at the same point in time between the new and consolidated sequences. We also measured the difference in speed (mean correct sequence duration) between the last two blocks of training and the first two blocks of retest for the consolidated sequences (fixed-effect:post, random-effect:sequence) in order to be comparable to previous studies, and found a significantly lower sequence duration in the beginning of the retest (β =−0.086, SE=0.023, p=.00014).”

3) The consolidated sequences were not learned at the same time and not subject to the same consolidation and reconsolidation period. These differences could ultimately bias the classification results for the consolidated sequences. Tseq1 was practiced on Day1, and then tested on Day2. Tseq2 was then immediately practiced after Tseq1. Tseq1 and Tseq2 were tested in the scanner protocol on Day3. There are 2 problems. First, Tseq1 was consolidated over 2 nights of sleep, versus 1 for Tseq2. Because of this, any difference in neural representation could be due to the simple age and consolidation history of the memory. Second, exposure to a new sequence (Tseq2) immediately following the reactivation of Tseq1 can interfere with the reconsolidation of Tseq1. Evidence from recent studies of motor reconsolidation have shown that exposure to similar manipulation can significantly alter the memory trace for the reactivated, previously consolidated memory (Tseq1). Essentially what is left is a classification between a sequence that was consolidated and reconsolidated vs. a sequence that was consolidated. It is unclear why the authors did not train both of the sequences at the same time particularly because they are testing them as such. This limitation should be addressed.

The reviewers are right that this is a limitation of our study, as this was not primarily designed for the present analysis, but intended to measure consolidation and reconsolidation of motor sequence learning. However, many models posit that reconsolidation uses the same mechanism as consolidation, and the cerebral structures that support the long-term memory should not differ. Therefore, the consistent pattern differences between consolidated sequences localized in this study should reflect genuine sequence encoding. This is further supported by the absence of behavioral difference between the consolidated and reconsolidated sequences.

Accordingly, we added the following sentence to the discussion to address this limitation: “Similarly, due to the main goal of the study, which was to investigate consolidation and reconsolidation of sequential motor learning, the trained sequences were not learned on the same day, potentially inducing modulating effect on the brain patterns that were measured. However, as reconsolidation is thought to rely on mechanisms similar to consolidation, we believe that both sequences were supported by local networks in the same regions that evoked differentiated patterns of activity rather than by large-scale reconsolidation induced activity changes.” (subsection “Methodological considerations”).

4) Regarding the performance issue (i.e., improvement of new sequences during the MVPA session): Critically, in their response, the authors argued that the inconsistency of Author response image 1 to Figure 3 is due to the lack of statistical power because of using only one cross-validation fold. However, at the same time, they argued that the inconsistency of Author response image 2 (the contrast of distances between the first and the second run) to Figure 3 is the supporting evidence that the result presented in Figure 3 reflects consolidation, despite that these estimates were also computed using only one cross-validation fold. If they say Author response image 1 is unreliable because of using single fold, then what is presented in the Author response image 2 should also be unreliable for the very same reason. Ideally, the new sequences should have practiced well until their performance and underlying representations reached to plateau before going into the scanner, as the initial rapid change in the representation would essentially have little thing to do with consolidation process. It is still true that the distance estimate computed through the 4 partitions would be significantly biased if there is some inconsistency in pattern difference in any of the partitions due to the ongoing performance improvement. Therefore, caution is required to conclude that the difference highlighted in Figure 3 can be attributed specifically to the consolidation. The authors should discuss the potential impact of the sub-optimal estimate of the plateau representation for the new sequences.

We would like to thank the reviewer for noting this issue. We thus added the following sentence in the text of the manuscript to address the sub-optimal estimate of the plateau representation for new sequences: “In particular, it can be noted from Figure 1 that the subjects’ performance of the new sequences evolved as they practiced them. Thus, as we assessed differences in patterns evoked by their execution across the whole-session, it is possible that representations emerging when the performance plateaued were missed by our analysis. This problem is inherent to any investigation of learning mechanisms and should be addressed in future studies measuring the dynamic of pattern difference changes within the training session which would require both a sensitive metric and signal of highest quality.” (subsection “Methodological considerations”).

Author details

1. Basile Pinsard

   1. Laboratoire d’Imagerie Biomédicale, Sorbonne Université, CNRS, INSERM, Paris, France
   2. Functional Neuroimaging Unit, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, Canada

   Contribution

   Conceptualization, Software, Formal analysis, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   For correspondence

   basile.pinsard@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4391-3075

2. Arnaud Boutin

   1. Functional Neuroimaging Unit, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, Canada
   2. McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada

   Contribution

   Conceptualization, Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5696-2626

3. Ella Gabitov

   1. Functional Neuroimaging Unit, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, Canada
   2. McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada

   Contribution

   Conceptualization, Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

4. Ovidiu Lungu

   Functional Neuroimaging Unit, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, Canada

   Contribution

   Conceptualization, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

5. Habib Benali

   1. Laboratoire d’Imagerie Biomédicale, Sorbonne Université, CNRS, INSERM, Paris, France
   2. PERFORM Centre, Concordia University, Montreal, Canada

   Contribution

   Supervision, Methodology

   Competing interests

   No competing interests declared

6. Julien Doyon

   1. Functional Neuroimaging Unit, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, Canada
   2. McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
   3. Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Canada

   Contribution

   Conceptualization, Supervision, Project administration, Writing—review and editing

   For correspondence

   julien.doyon@mcgill.ca

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3788-4271

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