Wakeful targeted memory reactivation during short rest periods modulates early motor learning.

Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice NPJ Sci Learn . 2026 Mar 2;11:23. doi: 10.1038/s41539-026-00407-9 Search in PMC Search in PubMed View in NLM Catalog Add to search Wakeful targeted memory reactivation during short rest periods modulates early motor learning Ryushin Kawasoe Ryushin Kawasoe 1 Graduate School of Welfare and Health Science, Oita University, Oita, Japan 6 Present Address: Division of Health Sciences, Graduate School of Medicine, The University of Osaka, Suita, Osaka Japan Find articles by Ryushin Kawasoe 1, 6 , Kana Matsumura Kana Matsumura 1 Graduate School of Welfare and Health Science, Oita University, Oita, Japan Find articles by Kana Matsumura 1 , Taiga Shinohara Taiga Shinohara 2 Faculty of Welfare and Health Science, Oita University, Oita, Japan Find articles by Taiga Shinohara 2 , Koki Arima Koki Arima 2 Faculty of Welfare and Health Science, Oita University, Oita, Japan Find articles by Koki Arima 2 , Yuhi Takeo Yuhi Takeo 3 Department of Rehabilitation, Oita University Hospital, Oita, Japan 4 Graduate School of Medicine, Oita University, Oita, Japan Find articles by Yuhi Takeo 3, 4 , Takashi Ikeda Takashi Ikeda 5 Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan Find articles by Takashi Ikeda 5 , Hisato Sugata Hisato Sugata 1 Graduate School of Welfare and Health Science, Oita University, Oita, Japan 2 Faculty of Welfare and Health Science, Oita University, Oita, Japan 4 Graduate School of Medicine, Oita University, Oita, Japan Find articles by Hisato Sugata 1, 2, 4, ✉ Author information Article notes Copyright and License information 1 Graduate School of Welfare and Health Science, Oita University, Oita, Japan 2 Faculty of Welfare and Health Science, Oita University, Oita, Japan 3 Department of Rehabilitation, Oita University Hospital, Oita, Japan 4 Graduate School of Medicine, Oita University, Oita, Japan 5 Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan 6 Present Address: Division of Health Sciences, Graduate School of Medicine, The University of Osaka, Suita, Osaka Japan ✉ Corresponding author. Received 2025 Jul 3; Accepted 2026 Feb 13; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13065769  PMID: 41771881 Abstract This study investigated whether wakeful targeted memory reactivation (TMR) during short rest intervals improves motor learning. Participants were randomly assigned to three groups and performed a sequential key-press task under each condition: (1) TMR regular group: auditory cues played at the same speed as the previous task, (2) TMR fast group: auditory cues played 1.3 times faster, and (3) TMR random group: auditory cues randomized in pitch. To examine the motor learning effect of cue structure, we compared motor learning across three groups (TMR regular , TMR fast , and TMR random ). The TMR fast group enhanced early learning gains compared with the TMR regular group. Electroencephalogram data revealed stronger functional connectivity centered on the lateral orbitofrontal cortex (lOFC) in the TMR fast group than in the TMR regular group. Together, these findings suggest that wakeful TMR can enhance early motor learning depending on cue timing and structure, highlighting the importance of optimizing sensory parameters for learning improvement. Subject terms: Neuroscience, Psychology, Psychology Introduction In daily life, we continuously consolidate and refine motor skills through unconscious processes, particularly during rest periods, to enhance performance efficiency. The improvement of motor performance efficiency through repeated movement is referred to as “motor learning,” which consists of both “online learning,” during which performance improves with practice, and “offline learning,” in which improvement occurs during rest periods between practice sessions 1 . Offline learning is believed to involve memory consolidation, a process that typically entails stabilizing and improving memory traces during rest periods 2 , 3 . Importantly, such consolidation occurs across multiple timescales: over hours to days (e.g., sleep-dependent consolidation) 4 – 7 , within minutes (e.g., wakeful-rest consolidation) 8 – 11 , and even within seconds during short rest intervals (micro-consolidation) 12 – 14 . Earlier studies focused on sleep-dependent consolidation occurring between practice sessions 4 , 5 , and demonstrated the involvement of the cerebellum, hippocampus, and striatum in this process 6 , 7 . More recent research has shown that memory consolidation can occur not only during sleep but also during wakeful rest periods 8 – 11 . During wakeful rest, consolidation over shorter timescales is mediated by the primary motor cortex, dorsolateral prefrontal cortex, and orbitofrontal cortex (OFC) 9 , 11 . Remarkably, studies have confirmed that micro-consolidation can occur rapidly, even with just a 10-s break between motor tasks 12 – 14 . Bönstrup et al. 12 introduced the terms “micro-online learning” and “micro-offline learning,” referring to the improvements observed during short practice and rest intervals, respectively. They limited their analysis to trials accounting for ~95% of session learning and demonstrated that “micro-offline learning” substantially contributed to early learning. As a neurophysiological mechanism during “micro-offline learning,” Buch et al. 13 reported the involvement of a network architecture over several brain regions, such as the primary sensorimotor cortex, entorhinal cortex, and hippocampus. They also reported that the neural activity patterns replayed during rest occurred 20-fold faster than during the preceding task, a phenomenon called “neural replay” 13 . In recent years, this phenomenon has been reported to be induced by the hippocampal activity in several studies 15 – 18 . For instance, Roux et al. 18 reported that neural replay in the hippocampus is critical for the consolidation of procedural motor memory. Another study demonstrated that neural replay correlates with activation of the hippocampus and lateral orbitofrontal cortex (lOFC) 19 . These data emphasize that active neurophysiological mechanisms are involved during rest periods in offline learning, which play a vital role in driving improvements in motor performance. Parallel to these data, a phenomenon known as “targeted memory reactivation (TMR)” improves learning efficiency by promoting the reactivation of specific memory traces. TMR associates learning content with specific sensory cues, such as auditory and olfactory stimuli, which are presented again during sleep to facilitate memory reactivation 20 – 23 . For instance, Antony et al. 24 showed that re-presentation of task-related sounds during sleep improved subsequent motor performance, indicating enhanced sleep-dependent consolidation. The neural substrate of sleep TMR engages multiple brain regions 25 – 27 . Legendre et al. 28 demonstrated that functional connectivity (FC) between the hippocampus and OFC is involved in TMR-induced memory reactivation, which in turn results in improved memory task performance after sleep. In contrast to sleep TMR, several studies have focused on whether TMR improves consolidation in wakeful rest 29 – 33 . Salfi et al. 29 demonstrated improvement in motor performance by TMR combined with motor imagery. Furthermore, Tambini et al. 30 reported that wakeful TMR induced by visual stimuli enhanced memory stability. These data suggest that TMR during wakeful rest improves motor performance and memory stability. In contrast, Diekelmann et al. 31 reported that presenting an olfactory cue together with a learning task during wakeful rest suppressed learning, suggesting that sensory cues during wakefulness can interfere with motor learning. A more recent study demonstrated that the effect of TMR during wakeful rest depends on learning contents, such as navigation and contextual learning tests 32 . Another study showed that wakeful TMR accelerated motor learning efficiency with faster stimulation (~1.3-fold faster than during learning) 33 . Hence, these mixed findings suggest that the efficacy of wakeful TMR may depend on cue parameters such as temporal speed. To date, although evidence from sleep TMR suggests the involvement of multiple brain regions, only a few studies have demonstrated the neurophysiological mechanisms underlying wakeful TMR. Extensive research in recent years has focused on TMR incorporating sensory cues during wakefulness, with ongoing debate 32 . Furthermore, studies on wakeful TMR have used auditory or olfactory stimuli ranging from several minutes to several hours 29 , 31 , 33 . However, the impact of manipulating TMR parameters within a short duration, particularly cue speed, on motor learning remains unclear. Accordingly, we formulated two hypotheses: (1) wakeful TMR combined with auditory stimuli during short rest intervals would modulate learning gain depending on the specific temporal properties of the auditory stimuli, and (2) wakeful TMR during short rest intervals engages FC among regions implicated in micro-consolidation and neural replay, including the lOFC, primary sensorimotor cortex, and prefrontal areas. To explore these hypotheses, we investigated how TMR affects motor learning during short 10-s rest intervals, focusing on both behavioral outcomes and neurophysiological profiles. Participants repeatedly performed a 10-s key-press task followed by a 10-s rest period. To induce wakeful TMR, task-related auditory stimuli were presented during short rest intervals, and learning efficiency was compared across three auditory stimulus conditions to examine the effects on short-term motor learning. An electroencephalogram (EEG) was recorded throughout the experiment to analyze the neural mechanisms underlying micro-consolidation during wakeful TMR. Results Changes in motor performance during short-term rest intervals We assigned 69 participants to four groups comprising three auditory stimulus groups and one control group. Five participants were excluded from subsequent analyses due to incorrect button presses during rest intervals or substantial electromyographic noise contamination in EEG data (Table 1 ). The experimental design consisted of practice and rest intervals for 10 s each, and participants performed a five-item explicit motor sequence task in which they pressed a key with their nondominant left hand during practice intervals, according to previous research 12 (Fig. 1 ). The sequence during practice intervals was always constant (4-1-3-2-4), and participants pressed the key that matched the number as rapidly and accurately as possible. In the rest intervals, a five-item sequence was converted into “cross (×)” and continuously displayed for 10 s. In practice intervals, auditory stimuli were matched to each item with their respective musical scales and were played at the timing of the key-press. In rest intervals, the sound was played at equal intervals using the average value of the key-pressing time of the previous practice interval. Motor performance was evaluated as the correct sequence pressing speed (sequences [seq]/s). Table 1. Characteristics of participants in each group TMR regular TMR fast TMR random TMR no p -value Gender (male/female) 10/6 7/9 8/7 8/9 0.7257 Age (years; mean ± SD) 21.6 ± 2.8 20.8 ± 1.5 21.5 ± 1.8 20.6 ± 1.1 0.5294 Handedness (mean ± SD) 92.9 ± 11.4 95.4 ± 7.3 91.9 ± 7.5 87.4 ± 14.9 0.4164 Open in a new tab This table shows participants included in the final analysis after exclusions. Excluded participants were as follows: 2 from TMR regular , 1 from TMR fast , and 2 from TMR random . Fig. 1. Sequential motor learning task. Open in a new tab Each trial consisted of 10-s practice intervals followed by 10-s rest intervals, repeated for 36 trials. The motor learning task involved a key-pressing task using the nondominant left hand, in which participants were instructed to press the button corresponding to the number displayed on the screen as rapidly and accurately as possible. The five sequence items, 4-1-3-2-4, were repeated during practice intervals. The primary outcome measure was the key-pressing speed. During the rest intervals, the items were replaced with the cross (×) mark. EEG signals were simultaneously recorded throughout the experiment. Additionally, auditory tones were presented at each button press during both motor task and rest intervals, with numbers 1–4 corresponding to the musical notes C, D, E, and F. Participants were assigned to three TMR groups and one control group. The three TMR groups differed in the auditory stimulation provided during practice and rest intervals: (1) TMR regular group, with auditory stimuli replayed at the same speed as the preceding practice; (2) TMR fast group, with stimuli replayed at 1.3 times the speed of the preceding practice; and (3) TMR random group, with stimuli replayed at the same speed but using randomly varied musical scales. In addition, a control group (TMR no ) was included to assess the reproducibility of motor sequence learning previously reported 12 – 14 . Importantly, the replay speed of 1.3 times in the TMR fast group was selected based on a previous study showing that motor learning was facilitated when somatosensory stimulation was applied at this rate 33 . Figure 2 shows the motor learning in the TMR no group. The sequence pressing speed accelerated with each successive trial (Fig. 2A ). According to a previous study, we defined learning during practice intervals as micro-online learning and learning during the rest interval as micro-offline learning 12 (Fig. 2B ). In the TMR no group, the mean (±SD) gains were: cumulative learning = 0.505 ± 0.192, micro-offline learning = 2.809 ± 1.611, and micro-online learning = −2.348 ± 1.693. Cumulative learning was defined as the sum of micro-online and micro-offline learning. A one-sample t-test showed significant learning gains for cumulative learning and micro-offline learning (cumulative learning: t = 10.85, p < 0.001, Cohen’s d = 2.63; micro-offline learning: t = 7.22, p < 0.001, Cohen’s d = 1.75), but not for micro-online learning (micro-online learning: t = −5.73, p < 0.001, Cohen’s d = −1.39, Fig. 2C ). This indicates that the improvement in motor performance occurred primarily during rest intervals but not practice intervals. Fig. 2. Reproducibility of motor sequence learning. Open in a new tab A Motor performance in a TMR no group participant. The black line indicates the participant’s performance score during practice intervals, and the gray shaded area the standard errors. The vertical axis shows the sequence speed per second and the horizontal axis the number of trials. B Enlarged view of motor performance during the early learning period. Micro-online indicates motor performance scores during practice intervals and micro-offline motor performance scores during rest intervals. C Comparison of cumulative learning gains in TMR no group participants. The vertical axis represents the learning gain. Cumulative learning was defined as the sum of micro-online and -offline learning. Cumulative learning and micro-offline learning showed significant improvement in learning gain, suggesting that micro-offline learning is responsible for most of the cumulative learning (***: p < 0.001, one-sample t-test). Changes in motor performance between the conditions To examine the effects of TMR with consistent auditory feedback, we focused on the TMR regular , TMR fast , and TMR random groups. First, consistent with the TMR no group, all three groups exhibited significant increases in cumulative learning gains for total and micro-offline learning, whereas micro-online learning significantly decreased (TMR regular , micro-online learning: t = −5.00, p < 0.001, Cohen’s d = −1.25; micro-offline learning: t = 5.64, p < 0.001, Cohen’s d = 1.41; cumulative learning: t = 9.14, p < 0.001, Cohen’s d = 2.29 ; TMR fast , micro-online learning: t = −3.50, p < 0.001, Cohen’s d = −0.87; micro-offline learning: t = 4.10, p < 0.001, Cohen’s d = 1.03; cumulative learning: t = 10.49, p < 0.001, Cohen’s d = 2.62; TMR random , micro-online learning: t = −4.08, p < 0.001, Cohen’s d = −1.05; micro-offline learning: t = 4.66, p < 0.001, Cohen’s d = 1.20; cumulative learning: t = 8.53, p < 0.001, Cohen’s d = 2.20; Fig. S1 ). The learning gain in each group exhibited a rapid increase in motor performance during the early learning phase (Fig. 3A ). Further, a repeated-measures ANOVA was performed with learning phase (early, intermediate, late) as a within-subject factor and Group as a between-subject factor. As the Mauchly’s test indicated that the assumption of sphericity was not violated ( W = 0.99, p = 0.87), the results are reported using uncorrected degrees of freedom. The repeated-measures ANOVA revealed a significant main effect of learning phase ( F ( 2.88 ) = 94.92, p < 0.001, η 2 = 0.683) and post-hoc tests indicated significant differences for both early–intermediate ( p < 0.001, Bonferroni corrected) and early–late ( p < 0.001, Bonferroni corrected) comparisons, whereas the intermediate–late comparison was not significant ( p = 1.000; Table S1 ). Additionally, there was a significant Group × Learning Phase interaction ( F ( 4.88 ) = 3.36, p = 0.013, η 2 = 0.132). In addition, post-hoc comparisons demonstrated that the TMR fast group showed significantly greater total learning gains than the TMR regular group during the early learning phase ( p = 0.007, Fig. 3B and Table S2 ). Furthermore, no significant group differences were observed during intermediate (all p > 0.13) or late (all p = 1.00) learning phases (Table S2 ). These results confirmed that the significant group differences were specific to the early learning phase. The TMR random group, however, did not differ significantly from any other in early learning performance (all p > 0.29). Fig. 3. Comparison of motor performance scores. Open in a new tab A Improvement in motor performance in the TMR regular , TMR fast , and TMR random groups. Solid lines indicate average motor performance scores across participants. Shaded areas indicate standard errors. pink: TMR regular group, blue: TMR fast group, orange: TMR random group. B Learning gains phases (early, intermediate, and late) for the three groups. A repeated-measures ANOVA revealed a significant main effect of the learning phase ( p < 0.001) and a significant Learning Phase × Group interaction ( p = 0.013). Post-hoc comparisons showed that learning gains significantly increased from the early to intermediate and early to late phases (* p < 0.001), but not between the intermediate and late phases. Importantly, during the early phase, the TMR fast group exhibited greater gains than the TMR regular group († p = 0.007). To further investigate the source of the group differences observed in the early phase, we compared the micro-online and -offline learning gains among the three groups. As shown in Fig. 4 , one-way ANOVAs revealed no significant differences among groups for either micro-online ( F 2.44 = 0.06, p = 0.945, η 2 = 0.003, Fig. 4 A) or -offline learning ( F 2.44 = 0.39, p = 0.681, η 2 = 0.017, Fig. 4 B). Collectively, although the TMR fast group exhibited significantly greater early learning gains than the TMR regular group, this difference was not driven by a significant change in either micro-online or -offline processes alone. Furthermore, to address potential confounding factors, we analyzed the number of auditory stimuli to determine whether the early learning gain differences were driven by replay stimulus counts. However, we found no significant differences in stimulus counts across conditions ( p = 0.696; Fig. S2 ). This suggests that the observed early learning effects were not due to the number of auditory stimuli, implying a potential influence of stimulus presentation speed. Fig. 4. Comparison of learning gains during early learning. Open in a new tab A Violin plots showing the distribution of participant micro-online learning gains for the TMR regular , TMR fast , and TMR random groups. Each dot represents an individual participant. The white dot indicates the median, the thick black bar shows the interquartile range, and the thin line the full data range. A one-way ANOVA revealed no significant differences among groups ( p = 0.945). B Violin plots showing the distribution of participant micro-offline learning gains for the TMR regular , TMR fast , and TMR random groups. Each dot represents an individual participant. The white dot indicates the median, the thick black bar shows the interquartile range, and the thin line the full data range. A one-way ANOVA revealed no significant differences among groups ( p = 0.681). Comparison of FC between conditions To clarify the neurophysiological mechanisms related to wakeful TMR, we compared the strength of FC between groups that demonstrated significant differences in the early learning phase. In this analysis of FC, we focused on the alpha and beta frequency bands because these frequencies are related to motor learning 9 , 34 . We calculated FC using lagged coherence, considering the phase shifts among different brain regions 35 . The TMR fast group showed significantly stronger FC in the alpha band between the left lOFC and right pars orbitalis ( t = 4.32, p < 0.05, FDR-corrected) (Fig. 5 ), as well as with the left rostral middle frontal gyrus (RMFG) ( t = 4.46, p < 0.05, FDR-corrected) (Fig. 5 ), compared with the TMR regular group. Fig. 5. Comparison of FC between TMR fast and TMR regular groups. Open in a new tab FC between the left lOFC, RMFG, and right pars orbitalis in the TMR fast group was significantly stronger than that in the TMR regular group in the alpha band. L left, R right, lOFC lateral orbitofrontal cortex, RMFG rostral middle frontal gyrus. Discussion This study explored the impact of wakeful TMR during 10-s rest intervals on micro-offline motor learning. The results of this study showed that the TMR fast group exhibited greater early learning gains than the TMR regular group and stronger FC centered on the lOFC. We initially hypothesized that the impact of wakeful TMR on micro-offline learning would depend on the specific properties of auditory cues, particularly their temporal speed. Our results are consistent with this hypothesis, indicating that learning outcomes differed based on the stimulation condition. As mentioned in the Introduction, the effect of TMR during wakeful rest remains a subject of debate. Although previous studies reported that wakeful TMR facilitates motor learning 29 , 33 , more recent data suggests that it could interfere with motor learning in some contexts 31 , 32 , 36 . In the present study, the comparison of early learning performance between the TMR fast and TMR regular groups, where different auditory stimulus speeds were applied during rest intervals, indicated significantly enhanced early learning in the TMR fast group. Thus, learning outcomes were highly dependent on cue parameters. In the TMR fast group, we set the auditory speed as 1.3 times the performance speed right before the rest interval, based on previous research showing that electrical stimuli at 1.3× speed improved learning performance 29 . Hence, our findings suggest that at a microscale of wakeful TMR, the use of temporally accelerated cues may enhance early motor learning efficiency more effectively than cues delivered at a regular speed. The TMR fast group exhibited significantly greater early learning gains compared with the TMR regular group. We suggest that the faster presentation rate itself contributed to the enhanced learning gains by more closely mirroring the naturally time-compressed neural reactivation patterns observed in both animal 37 and human 13 , 19 studies. In a human study, Buch et al. reported that hippocampo-neocortical reactivation during wakeful rest occurs at roughly 20 times the original speed 13 . This suggests that temporally compressed auditory stimulation may better align with endogenous memory consolidation processes. Additionally, the number of auditory stimuli was directly determined by the participant’s preceding tapping count in our study. Although retrospective analysis revealed no significant differences in the total stimulus count between groups, the count was not experimentally fixed. Thus, we cannot entirely rule out the possibility that stimulus count contributed to the observed learning effect. Together, our findings suggest that the optimal sensory stimuli for inducing TMR during wakeful rest can enhance early motor learning. Although significant differences in early learning gains were observed among groups, no significant changes were detected in either micro-offline or micro-online gains during the early learning stage. This discrepancy likely reflects differences in the statistical reliability of the respective indices. Total learning, defined as the contrast between the first and last trials within a phase, represents an average across many trials and is therefore less susceptible to trial-to-trial variability. In contrast, micro-online and micro-offline gains are computed as cumulative trial-wise increments, which inherently capture moment-to-moment fluctuations, inflate variance, and reduce sensitivity to between-group differences. In this study, a significant difference in early learning gain was observed between the TMR fast and TMR regular groups. In contrast, the TMR random group yielded no distinct learning modulation. We suggest that random auditory cues failed to induce TMR because they lacked a consistent association with the task sequence. Consequently, these cues were processed as irrelevant background noise rather than memory triggers. This finding underscores the critical role of cue–task congruency and implies that the temporal structure of the stimulation influences motor learning only when the cues map onto the learned motor pattern. However, as direct physiological evidence supporting this interpretation is currently lacking, further research is required to clarify the neural processing of nonassociated cues. In addition, FC strengths in the TMR fast group among the lOFC, RMFG, and pars orbitalis were significantly stronger than those in the TMR regular group. The lOFC is a component of the limbic network (LIM), which is broadly constructed from the hippocampus and cingulate cortex 38 – 40 , along with regions such as the lOFC. Notably, the LIM is associated with explicit motor learning 41 . Moreover, Schuck et al. 19 reported that the hippocampus and lOFC, which are specific components of the LIM, are deeply involved in neural replay, a phenomenon unique to offline learning. Regarding the FC, it has been reported that FC between lOFC and RMFG is related to working memory functions involving motor actions 42 . The FC between lOFC and pars orbitalis is related to the integration and adaptation of external feedback 43 . Additionally, Neubert et al. 44 reported that FC mediates the integration of extrinsic sensory stimuli and subsequent processing, acting as a link to guide flexible action selection. Several studies have also confirmed that activities in these brain regions are modulated by rhythmic auditory stimulation 45 – 49 . For instance, Braunlich et al. 48 reported that FC, including the lOFC and pars orbitalis, correlated with the speed of auditory stimulation. Wang et al. 49 also demonstrated that faster auditory stimulation results in greater activation of the prefrontal cortex, particularly the RMFG. These findings suggest that the auditory stimuli linked to task contents affect the higher-order memory-related brain regions by modulating the speed of sound. This mechanism supports our observation that early learning was more facilitated in the TMR fast group than in the TMR regular group. These results indicate that the FC among the lOFC, RMFG, and pars orbitalis was activated in the TMR fast group, enhancing early learning. Crucially, although faster auditory stimulation may generally enhance auditory cortical activation 50 , the pattern observed in this study cannot be explained solely by stimulus intensity. Importantly, the regions exhibiting stronger connectivity, including the lOFC, pars orbitalis, and RMFG, are not primary auditory cortices. These areas are more consistently associated with higher-order processes, such as neural replay 19 , 41 , working memory 42 , and feedback integration 43 , 44 . Collectively, the increased connectivity observed in the TMR fast group likely reflects condition-specific modulation of memory-related networks rather than a simple effect of stronger auditory stimulation. It should be noted that the frontal and temporal regions showing significant FC in this study are highly susceptible to artifacts, indicating that some residual contamination may remain. Our study has several limitations. First, the current study design did not include a strictly matched control condition to isolate the specific effects of the TMR protocol. Consequently, the precise mechanisms whereby TMR modulates intrinsic motor learning remain to be fully elucidated. Therefore, to disentangle these effects, future studies should include an additional condition wherein auditory cues are presented either during practice or rest. Next, although we used surface EEG to elucidate the neurophysiological mechanism related to wakeful TMR, it cannot reliably measure the activity of deep brain structures. Therefore, we could not examine the association of deep brain structures, such as the hippocampus, with wakeful TMR. Furthermore, this study estimated the electrical current source using a standard/template brain instead of individual structural MRI data. This approach may introduce uncertainty in anatomical localization, compounded by the inherent indeterminacy of the EEG inverse problem. Third, we applied auditory stimuli during rest intervals at 1.3 times the performance speed, based on practice intervals immediately preceding those in the TMR fast group, according to a previous study 33 . However, a previous study applied electrical stimulation and not an auditory stimulus to the fingertips to induce TMR. The electrical stimuli used in that study were directly applied to the fingertips that pressed the button; hence, the association with the learning task was direct. This difference in the modality and specificity of stimulation may explain why the modified auditory stimuli applied at 1.3 times the speed did not result in optimal improvement of motor learning and micro-consolidation 12 . Fourth, although we applied FDR correction to control the p-value across all ROI pairs, we adopted a relatively liberal threshold ( q = 0.3) due to the exploratory nature of this novel study. While this approach is consistent with previous exploratory work 51 , 52 , it increases the possibility of type II errors. Therefore, the findings regarding FC should be interpreted with caution and confirmed in future studies using stringent thresholds. Fifth, our FC analyses were limited to comparisons between groups that showed behavioral differences. Therefore, we cannot rule out the possibility that TMR intervention modulates FC even in the absence of behavioral differences. A comprehensive FC comparison across all groups could provide stronger evidence and represent an important direction for future research. Finally, the number of auditory replays was not experimentally fixed but determined by the participant’s performance in the preceding practice trial. Although our retrospective analysis indicated no significant differences in replay stimulus counts across groups, the lack of a strict control over this parameter constitutes a limitation. Thus, we cannot rule out the possibility that variations in replay count may have influenced motor learning outcomes. In summary, we investigated how the specific temporal properties of auditory stimuli modulate the effects of wakeful TMR on motor learning during short rest intervals. Our results demonstrated that auditory stimuli presented at 1.3× the preceding practice speed during rest intervals improved early motor learning compared to stimuli presented at the same speed. Furthermore, this effect involves lOFC-mediated FC as a neurophysiological mechanism. Thus, these findings suggest that optimizing sensory cue parameters during wakeful rest intervals actively promotes early motor learning. In the future, it is essential to establish rigorous control conditions and directly compare them with TMR protocols to accurately isolate the specific effects of TMR during short wakeful rest. Additionally, the possible synergy between TMR and other cognitive strategies may help further optimize motor learning. For instance, Salfi et al. 29 reported that while TMR alone did not yield significant improvements, combining it with motor imagery successfully improved motor learning. This suggests that integrating TMR with other contextual or cognitive facilitation strategies can significantly accelerate motor learning during wakefulness, unlocking benefits not obtained with TMR alone. This highlights a potential direction for future research aimed at refining the application of TMR in wakeful conditions. Materials and methods Participants A total of 69 participants were randomly assigned to four conditions for comparing the effect of TMR on auditory stimuli (mean age of participants: 21.2 ± 1.9 years; 35 men, 34 women). Three and two participants were excluded from subsequent analyses due to incorrect button presses during rest intervals and substantial electromyographic noise contamination in the EEG data, respectively (Table 1 ). All participants had normal visual and auditory functions and no history of neurological or psychiatric disorders. Participants were instructed to refrain from drinking alcohol the day before and from smoking 1 h before the experiment to control their physical condition. All participants were confirmed right-handed based on the Edinburgh Handedness Inventory Test 53 . Participants who had more than five consecutive years of piano experience in the past were excluded from the study, according to previous studies 12 , 13 . The experimental procedures and objectives were explained in detail to the participants, and written informed consent was obtained before the experiment. This study was conducted according to the protocol approved by the Ethics Committee of Oita University School of Medicine (Approval Number: 2606). Motor learning task We created an experimental design based on previous research to measure learning gain in short intervals using 10-s practice intervals and 10-s rest intervals as one trial for 36 trials continuously (Fig. 1 ) 12 . The participant sat at a desk with a monitor. In the practice intervals, the participants pressed the keypad (HHSC-1×4-CR, Current Designs, Philadelphia, PA, USA) with their nondominant left hand as rapidly and accurately as possible, corresponding to the number displayed for 10 s. The numbers displayed on the monitor consisted of five sequence items (4-1-3-2-4), and participants explicitly learned that sequence. Key-presses 1–4 corresponded to the little, ring, middle, and index fingers, respectively. An asterisk mark was displayed above the numbers at the time the key was pressed, irrespective of whether the answer was correct or incorrect. The timing (ms) of key-presses was recorded for subsequent data analysis. During the rest intervals, “×” was presented instead of the five-item sequence for 10 s. The participants were instructed to stop key-pressing and fixate their gaze on the cross mark in the center of the screen. A transition phase was set for 200 ms as a preparation period for the transition from the rest intervals to the practice intervals. The total time required for the experiment was approximately 12 min. Calculation of learning metrics To characterize motor learning, we calculated three distinct metrics (total learning, micro-online learning, and micro-offline learning). Micro-online learning gain ( G online ) represents the improvement within a practice trial. It was defined as the difference between the speed of the last sequence ( S i , end ) and that of the first sequence ( S i , start ) within each trial ( i ). This value was added across the learning phase: G online = ∑ ( S i , end − S i , start ) where S denotes the correct sequence speed and i the trial index. Micro-offline learning gain ( G offline ) represents the improvement across a rest interval. It was defined as the difference between the first sequence speed of the subsequent trial ( S i + 1 , start ) and the last sequence speed of the preceding trial ( S i , end ). This value was also added across the phase: G offline = ∑ ( S i + 1 , start − S i , end ) Total learning ( G total ) represents the overall improvement in motor performance capacity. It was calculated as the difference between the mean sequence speed of the last trial ( S ¯ last ) and that of the first trial ( S ¯ first ). G total = S ¯ last − S ¯ first Note the mathematical distinction. Total learning uses trial averages to minimize variability. In contrast, micro-online and -offline learning gains rely on single-sequence data points. Therefore, total learning is not mathematically equivalent to a simple sum of micro-online and -offline gains. Stimulus conditions Auditory stimuli were applied to the participants during the practice and rest intervals to analyze the effects of TMR on motor learning during short-term rest intervals. Although olfactory and auditory stimuli have often been used as sensory stimuli to induce TMR 29 , 31 , 32 , 54 , 55 , olfactory functions exhibit genetic variability 56 and can be influenced by culture, environment, and daily life experience 57 , which can cause individual differences 58 . Therefore, we adopted auditory stimuli as a sensory stimulus to induce wakeful TMR, as described by previous studies 59 – 61 . In this study, we linked the key-pressing to piano sounds. Key-presses 1–4 corresponded to the musical scale “C-D-E-F,” respectively, ensuring the corresponding sound played when the button was pressed in the practice intervals. During the subsequent rest intervals, the same sounds were played to induce TMR. Even if participants pressed an incorrect item in the practice interval, corrected sounds were provided in the subsequent rest intervals, i.e., “F-C-E-D-F.” The loudness of the sound was set to approximately 70 dB, according to previous research 54 . To demonstrate the effect of TMR on motor learning during short-term wakeful rest depending on the specific temporal properties of the auditory stimuli, we randomly assigned the participants to the following three groups: Group 1 (TMR regular group): Auditory stimulus during practice and rest intervals. The sound speed provided during rest intervals matched the average key-press speed during practice intervals just before the rest interval. Group 2 (TMR fast group): Auditory stimulus during practice and rest intervals. The sound speed provided during rest intervals was modified to 1.3 times faster than the average key-press speed during practice intervals just before the rest interval. Group 3 (TMR random group): Auditory stimulus during practice and rest intervals. The sound speed provided during rest intervals matched the average key-press speed during practice intervals just before the rest interval; however, four types of musical scales were randomly presented. Additionally, we set up a TMR no group to confirm the reproducibility of previously reported findings 12 – 14 . Further, the TMR regular and TMR fast groups were included to examine the effects of temporal TMR characteristics on learning efficiency. Particularly, the TMR fast group was set based on a previous study that showed that motor learning improved by applying somatosensory stimulation at 1.3 times the performance speed before learning occurs 33 . The TMR random group was included as a control group to observe the learning effects of presenting unrelated auditory stimuli. The number of auditory sequences played during the 10-second rest interval was not fixed across participants. Instead, the number of auditory replays was matched to the number of key-press tapping performed during the preceding practice interval. Therefore, the number of replays varied between participants based on their performance speed. In the TMR fast group, the auditory sequences were compressed to 1.3 times speed, resulting in playback lasting approximately 7.8 s within the 10-s rest interval, with the remaining time left silent. EEG data acquisition Surface EEG data were measured during the experiment to elucidate the neurophysiological mechanism of the learning effect of TMR during short rest intervals. These data were recorded using a 64-channel EEG system (g. HIamp, g.tec, AUT) in an electrically shielded room. The EEG signals were digitally recorded using an online 0.1- to 100-Hz band-pass filter at a sampling rate of 1000 Hz. The ground electrode was located on the forehead, and the reference electrodes were mounted on the left and right earlobes. The right hemisphere uses the right earlobe as the reference electrode, and the left hemisphere uses the left earlobe as the reference electrode. The active electrodes were arranged according to the International 10-10 system. To detect eye blinks, EOG was simultaneously recorded using EEG. The electrode impedance did not exceed 50 kΩ, as specified in the g.tec active electrode specifications, and prior studies have also reported EEG recordings with impedances below 50 kΩ 62 – 64 . EEG data analysis The EEG data were analyzed using the Brainstorm software 65 implanted in MATLAB (MathWorks Inc., USA). The EEG data were subjected to independent component analysis (ICA; infomax algorithm) to detect regularly occurring artifacts such as eye blinking and cardiac response. First, all ICA activations were reviewed on a scrolling display, and components with time courses resembling eye blinks and cardiac artifacts were searched for and identified. Second, the nature of these components was validated by plotting their scalp topographies, providing further evidence of their physiological origins. Identified artifact components were removed from the EEG data by back-projecting the remaining components. Then, the EEG data were re-referenced to the arithmetic mean of all EEG electrodes (common average). Tomographic reconstruction of the data was performed by generating a three-shell sphere head model. The noise covariance matrix, including the practice and rest intervals, was calculated from the continuous EEG data. The minimum norm estimate was applied to the EEG data with 15,000 points over the cortical surface to compute the source distribution. Then, the EEG source data were parcellated into 68 regions of interest using the Desikan–Killiany Atlas 66 . To calculate FC between brain regions, we applied lagged coherence that measures the phase congruency of two EEG signals with some phase shift 35 because the EEG between different brain regions shows a phase shift within a few milliseconds. To elucidate the neurophysiological mechanisms in motor learning by TMR, we analyzed only the EEG data from the rest intervals. Moreover, we divided the 36 trials into the following three segments: early learning (1–12 trials), intermediate learning (13–24 trials), and late learning (25–36 trials). Although studies have reported a relationship between motor functions and oscillatory brain activities such as theta and gamma bands 67 , 68 , we focused on the alpha (8–13 Hz) and beta (13–30 Hz) bands, referring to studies that demonstrated a strong relationship between motor learning and oscillatory brain activities 9 , 34 . Statistical analysis As mentioned earlier, we excluded three participants who pressed the button incorrectly during the rest intervals. We also excluded two participants whose EEG data were contaminated with substantial electromyographic noise during the experiment. Hence, the final sample size used for data analyses was as follows: TMR regular group 16, TMR fast group 16, and TMR random group 15 (Table 1 ). Additionally, the TMR no group comprised 17 participants. Statistical analysis was conducted using MATLAB (R2022b). One-sample t-tests were performed to evaluate micro-online learning, micro-offline learning, and cumulative learning (the combined effect of micro-online and micro-offline learning) across all groups. We conducted repeated-measures ANOVA to examine the effects of the learning phase and Group on motor performance. Learning Phase (early, intermediate, late; within-subject factor) and Group (TMR regular , TMR fast , TMR random ; between-subject factor) were included in the model. Post-hoc comparisons were performed using Bonferroni correction. In the connectivity analyses, all ROI pairs were tested using Student’s t-tests. To control for multiple comparisons, p-values were corrected for FDR over all ROI pairs. Given the exploratory nature of this study, a liberal threshold of q = 0.3 was adopted, consistent with previous exploratory work 51 , 52 . Supplementary information Supplementary Information (502.7KB, pdf) Acknowledgements The authors want to acknowledge all volunteers who participated in this study. This study was supported by a grant from the KAKENHI [23K21590, 25K02965], the Japan Society for the Promotion of Science. Author contributions R.K. and H.S. contributed to the experimental design, data collection and analysis, and drafting of the manuscript. T.I. contributed to the establishment of the motor task system. K.M., T.S., K.A., and Y.T. contributed to the recruitment and data collection of participants. All authors read and approved the final version of the manuscript. Data availability Behavioral and EEG data are available upon request by contacting the corresponding author, Hisato Sugata ([email protected]). Code availability Custom-written code is available upon request by contacting the corresponding author, Hisato Sugata ([email protected]). Competing interests The authors declare no competing interests. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Supplementary information The online version contains supplementary material available at 10.1038/s41539-026-00407-9. References 1. Wessel, M. J., Zimerman, M. & Hummel, F. C. Non-invasive brain stimulation: an interventional tool for enhancing behavioral training after stroke. Front. Hum. Neurosci. 9 , 265 (2015). [ DOI ] [ PMC free article ] [ PubMed ] 2. Meier, B. & Cock, J. Offline consolidation in implicit sequence learning. Cortex 57 , 156–166 (2014). [ DOI ] [ PubMed ] [ Google Scholar ] 3. Debas, K. et al. Off-line consolidation of motor sequence learning results in greater integration within a cortico-striatal functional network. Neuroimage 99 , 50–58 (2014). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 4. Walker, M. P., Brakefield, T., Morgan, A., Hobson, J. A. & Stickgold, R. Practice with sleep makes perfect: sleep-dependent motor skill learning. Neuron 35 , 205–211 (2002). [ DOI ] [ PubMed ] [ Google Scholar ] 5. Walker, M. P. et al. Sleep and the time course of motor skill learning. Learn. Mem. 10 , 275–284 (2003). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 6. King, B. R., Hoedlmoser, K., Hirschauer, F., Dolfen, N. & Albouy, G. Sleeping on the motor engram: The multifaceted nature of sleep-related motor memory consolidation. Neurosci. Biobehav. Rev. 80 , 1–22 (2017). [ DOI ] [ PubMed ] 7. Fogel, S. et al. Reactivation or transformation? Motor memory consolidation associated with cerebral activation time-locked to sleep spindles. PLoS ONE 12 , e0174755 (2017). [ DOI ] [ PMC free article ] [ PubMed ] 8. Hotermans, C., Peigneux, P., De Noordhout, A. M., Moonen, G. & Maquet, P. Early boost and slow consolidation in motor skill learning. Learn. Mem. 13 , 580–583 (2006). [ DOI ] [ PubMed ] [ Google Scholar ] 9. Kawasoe, R. et al. Functional connectivity via the dorsolateral prefrontal cortex in the late phase of rest periods predicts offline learning. Neurosci. Lett. 822 , 137645 (2024). [ DOI ] [ PubMed ] 10. Wamsley, E. J. Memory consolidation during waking rest. Trends Cogn. Sci. 23 , 171–173 (2019). [ DOI ] [ PMC free article ] [ PubMed ] 11. Sami, S., Robertson, E. M. & Chris Miall, R. The time course of task-specific memory consolidation effects in resting state networks. J. Neurosci. 34 , 3982–3992 (2014). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 12. Bönstrup, M. et al. A rapid form of offline consolidation in skill learning. Curr. Biol. 29 , 1346–1351.e4 (2019). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 13. Buch, E. R., Claudino, L., Quentin, R., Bönstrup, M. & Cohen, L. G. Consolidation of human skill linked to waking hippocampo-neocortical replay. Cell Rep. 35 , 109193 (2021). [ DOI ] [ PMC free article ] [ PubMed ] 14. Bönstrup, M., Iturrate, I., Hebart, M. N., Censor, N. & Cohen, L. G. Mechanisms of offline motor learning at a microscale of seconds in large-scale crowdsourced data. NPJ Sci. Learn. 5 , 7 (2020). [ DOI ] [ PMC free article ] [ PubMed ] 15. Singer, A. C., Carr, M. F., Karlsson, M. P. & Frank, L. M. Hippocampal SWR activity predicts correct decisions during the initial learning of an alternation task. Neuron 77 , 1163–1173 (2013). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 16. Papale, A. E., Zielinski, M. C., Frank, L. M., Jadhav, S. P. & Redish, A. D. Interplay between hippocampal sharp-wave-ripple events and vicarious trial and error behaviors in decision making. Neuron 92 , 975–982 (2016). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 17. Jacobacci, F. et al. Rapid hippocampal plasticity supports motor sequence learning. Proc. Natl. Acad. Sci. USA 117 , 23898–23903 (2020). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 18. Roux, L., Hu, B., Eichler, R., Stark, E. & Buzsáki, G. Sharp wave ripples during learning stabilize the hippocampal spatial map. Nat. Neurosci. 20 , 845–853 (2017). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 19. Schuck, N. W. & Niv, Y. Sequential replay of nonspatial task states in the human hippocampus. Science 364 , eaaw5181 (2019). [ DOI ] [ PMC free article ] [ PubMed ] 20. Rudoy, J. D., Voss, J. L., Westerberg, C. E. & Paller, K. A. Strengthening individual memories by reactivating them during sleep. Science 326 , 1079 (2009). [ DOI ] [ PMC free article ] [ PubMed ] 21. Carbone, J. & Diekelmann, S. An update on recent advances in targeted memory reactivation during sleep. npj Sci. Learn. 9, 31 (2024). [ DOI ] [ PMC free article ] [ PubMed ] 22. Bar, E. et al. Local targeted memory reactivation in human sleep. Curr. Biol. 30 , 1435–1446.e5 (2020). [ DOI ] [ PubMed ] [ Google Scholar ] 23. Cheng, L. Y., Che, T., Tomic, G., Slutzky, M. W. & Paller, K. A. Memory reactivation during sleep improves execution of a challenging motor skill. J. Neurosci. 34 , 9608–9616 (2021). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 24. Antony, J. W., Gobel, E. W., O’Hare, J. K., Reber, P. J. & Paller, K. A. Cued memory reactivation during sleep influences skill learning. Nat. Neurosci. 15 , 1114–1116 (2012). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 25. Cairney, S. A., Guttesen, A.áV., El Marj, N. & Staresina, B. P. Memory consolidation is linked to spindle-mediated information processing during sleep. Curr. Biol. 28 , 948–954.e4 (2018). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 26. Schechtman, E. et al. Multiple memories can be simultaneously reactivated during sleep as effectively as a single memory. Commun. Biol. 4 , 25 (2021). [ DOI ] [ PMC free article ] [ PubMed ] 27. Lewis, P. A. & Bendor, D. How targeted memory reactivation promotes the selective strengthening of memories in sleep. Curr. Biol. 29 , R906–R912 (2019). [ DOI ] [ PubMed ] 28. Legendre, G. et al. Reinstatement of emotional associations during human sleep: an intracranial EEG study. Preprint at bioRxiv 10.1101/2022.06.24.497499 (2022). 29. Salfi, F., Tempesta, D., De Gennaro, L. & Ferrara, M. Cued memory reactivation during motor imagery practice influences early improvement of procedural skill learning. Neuroscience 418 , 244–253 (2019). [ DOI ] [ PubMed ] [ Google Scholar ] 30. Tambini, A., Berners-Lee, A. & Davachi, L. Brief targeted memory reactivation during the awake state enhances memory stability and benefits the weakest memories. Sci Rep. 7 , 15325 (2017). [ DOI ] [ PMC free article ] [ PubMed ] 31. Diekelmann, S., Büchel, C., Born, J. & Rasch, B. Labile or stable: opposing consequences for memory when reactivated during waking and sleep. Nat. Neurosci. 14 , 381–386 (2011). [ DOI ] [ PubMed ] [ Google Scholar ] 32. Rawson, A. B., Nalluru, S., O’Reilly, J. X. & Barron, H. C. Memory reactivation generates new, adaptive behaviours that reach beyond direct experience. Sci. Rep. 14 , 30097 (2024). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 33. Veldman, M. P. et al. Somatosensory targeted memory reactivation enhances motor performance via hippocampal-mediated plasticity. Cereb. Cortex 10.1093/cercor/bhac304 (2022). [ DOI ] [ PubMed ] 34. Sugata, H. et al. Role of beta-band resting-state functional connectivity as a predictor of motor learning ability. Neuroimage 210 , 116562 (2020). [ DOI ] [ PubMed ] 35. Pascual-Marqui, R. D. Instantaneous and lagged measurements of linear and nonlinear dependence between groups of multivariate time series: frequency decomposition. Preprint at 10.48550/arXiv.0711.1455 (2007). 36. Hoffman, L. J., Foley, J. M., Tanrıverdi, B., Chein, J. & Olson, I. R. Awake targeted memory reactivation doesn’t work. Mem. Cognit. 10.3758/s13421-024-01576-x (2024). [ DOI ] [ PMC free article ] [ PubMed ] 37. M. A. Wilson & B. L. McNaughton. Reactivation of hippocampal ensemble memories during sleep. Science 265 , 676–9 (1994). [ DOI ] [ PubMed ] 38. Enatsu, R. et al. Connections of the limbic network: a corticocortical evoked potentials study. Cortex 62 , 20–33 (2015). [ DOI ] [ PubMed ] [ Google Scholar ] 39. Thomas Yeo, B. T. et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106 , 1125–1165 (2011). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 40. Girn, M., Setton, R., Turner, G. R. & Spreng, R. N. The “limbic network,” comprising orbitofrontal and anterior temporal cortex, is part of an extended default network: Evidence from multi-echo fMRI. Netw. Neurosci. 1–23 10.1162/netn_a_00385 (2024). [ DOI ] [ PMC free article ] [ PubMed ] 41. Areshenkoff, C. N. et al. Distinct patterns of connectivity with the motor cortex reflect different components of sensorimotor learning. PLoS Biol. 22 , e3002934 (2024). [ DOI ] [ PMC free article ] [ PubMed ] 42. Rolls, E. T., Deco, G., Huang, C. C. & Feng, J. Prefrontal and somatosensory-motor cortex effective connectivity in humans. Cereb. Cortex 33 , 4939–4963 (2023). [ DOI ] [ PubMed ] [ Google Scholar ] 43. Sitek, K. R. et al. Decreased cerebellar-orbitofrontal connectivity correlates with stuttering severity: whole-brain functional and structural connectivity associations with persistent developmental stuttering. Front. Hum. Neurosci. 10 , 190 (2016). [ DOI ] [ PMC free article ] [ PubMed ] 44. Neubert, F. X., Mars, R. B., Thomas, A. G., Sallet, J. & Rushworth, M. F. S. Comparison of human ventral frontal cortex areas for cognitive control and language with areas in Monkey frontal cortex. Neuron 81 , 700–713 (2014). [ DOI ] [ PubMed ] [ Google Scholar ] 45. Bella, S. D. et al. Gait improvement via rhythmic stimulation in Parkinson’s disease is linked to rhythmic skills. Sci. Rep. 7 , 42005 (2017). [ DOI ] [ PMC free article ] [ PubMed ] 46. Forner-Cordero, A. et al. Effects of supraspinal feedback on human gait: rhythmic auditory distortion. J.Neuroeng. Rehabil. 16 , 159 (2019). [ DOI ] [ PMC free article ] [ PubMed ] 47. Thaut, M. H. et al. Rhythmic auditory stimulation in gait training for Parkinson’s disease patients. Mov. Disord. 11 , 193–200 (1996). [ DOI ] [ PubMed ] [ Google Scholar ] 48. Braunlich, K. et al. Rhythmic auditory cues shape neural network recruitment in Parkinson’s disease during repetitive motor behavior. Eur. J. Neurosci. 49 , 849–858 (2019). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 49. Wang, J. et al. Comparison of different rhythmic auditory stimuli on prefrontal cortex cortical activation during upper limb movement in patients with Parkinson’s disease: a functional near-infrared spectroscopy study. Front. Neurol. 15 , 1336268 (2024). [ DOI ] [ PMC free article ] [ PubMed ] 50. Rinne, T. et al. Modulation of auditory cortex activation by sound presentation rate and attention. Hum. Brain Mapp. 26 , 94–99 (2005). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 51. Sharkey, R. J., Karama, S. & Dagher, A. Overweight is not associated with cortical thickness alterations in children. Front. Neurosci. 9 , 24 (2015). [ DOI ] [ PMC free article ] [ PubMed ] 52. Bhaumik, D. K., Wang, Y., Yen, P. S. & Ajilore, O. A. Development of a Bayesian multimodal model to detect biomarkers in neuroimaging studies. Front. Neuroimaging 2 , 1147508 (2023). [ DOI ] [ PMC free article ] [ PubMed ] 53. Oldfield, R. C. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9 , 97–113 (1971). [ DOI ] [ PubMed ] 54. Schreiner, T., Lehmann, M. & Rasch, B. Auditory feedback blocks memory benefits of cueing during sleep. Nat Commun 6 , 8729 (2015). [ DOI ] [ PMC free article ] [ PubMed ] 55. Van Dongen, E. V. et al. Memory stabilization with targeted reactivation during human slow-wave sleep. Proc. Natl. Acad. Sci. USA 109 , 10575–10580 (2012). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 56. Trimmer, C. et al. Genetic variation across the human olfactory receptor repertoire alters odor perception. Proc. Natl. Acad. Sci. USA 116 , 9475–9480 (2019). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 57. R. Hudson. From molecule to mind: the role of experience in shaping olfactory function. J. Comp. Physiol. A 185 , 297–304 (1999). [ DOI ] [ PubMed ] 58. Wrzesniewski, A., Mccauley, C. & Rozin, P. Odor and affect: individual differences in the impact of odor on liking for places, things and people. Chem. Senses 24 , 713–721 (1999). [ DOI ] [ PubMed ] [ Google Scholar ] 59. Schechtman, E., Heilberg, J. & Paller, K. A. Memory consolidation during sleep involves context reinstatement in humans. Cell Rep. 42 , 112331 (2023). [ DOI ] [ PMC free article ] [ PubMed ] 60. Nicolas, J. et al. Targeted memory reactivation during post-learning sleep does not enhance motor memory consolidation in older adults. J. Sleep Res. 10.1111/jsr.14027 (2023). [ DOI ] [ PubMed ] 61. Whitmore, N. W., Harris, J. C., Kovach, T. & Paller, K. A. Improving memory via automated targeted memory reactivation during sleep. J. Sleep Res. 31 , e13731 (2022). [ DOI ] [ PMC free article ] [ PubMed ] 62. Korik, A., Sosnik, R., Siddique, N. & Coyle, D. Decoding imagined 3D hand movement trajectories from EEG: Evidence to support the use of mu, beta, and low gamma oscillations. Front. Neurosci. 12 , 130 (2018). [ DOI ] [ PMC free article ] [ PubMed ] 63. Celli, M. et al. One-year-later spontaneous EEG features predict visual exploratory human phenotypes. Commun. Biol. 5 , 1361 (2022). [ DOI ] [ PMC free article ] [ PubMed ] 64. King, J. R., Wyart, V. & King, J. R. The human brain encodes a chronicle of visual events at each instant of time through the multiplexing of traveling waves. J. Neurosci. 41 , 7224–7233 (2021). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 65. Tadel, F., Baillet, S., Mosher, J. C., Pantazis, D. & Leahy, R. M. Brainstorm: a user-friendly application for MEG/EEG analysis. Comput. Intell. Neurosci. 2011 , 879716 (2011). [ DOI ] [ PMC free article ] [ PubMed ] 66. Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31 , 968–980 (2006). [ DOI ] [ PubMed ] [ Google Scholar ] 67. Delorme, A., Westerfield, M. & Makeig, S. Medial prefrontal theta bursts precede rapid motor responses during visual selective attention. J. Neurosci. 27 , 11949–11959 (2007). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 68. Perfetti, B. et al. Modulation of gamma and theta spectral amplitude and phase synchronization is associated with the development of visuo-motor learning. J. Neurosci. 31 , 14810–14819 (2011). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials Supplementary Information (502.7KB, pdf) Data Availability Statement Behavioral and EEG data are available upon request by contacting the corresponding author, Hisato Sugata ([email protected]). Custom-written code is available upon request by contacting the corresponding author, Hisato Sugata ([email protected]). Articles from NPJ Science of Learning are provided here courtesy of Nature Publishing Group ACTIONS View on publisher site PDF (1.8 MB) Cite Collections Permalink PERMALINK Copy RESOURCES Similar articles Cited by other articles Links to NCBI Databases Cite Copy Download .nbib .nbib Format: AMA APA MLA NLM Add to Collections Create a new collection Add to an existing collection Name your collection * Choose a collection Unable to load your collection due to an error Please try again Add Cancel Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top