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(A) The BOLD time series within a seed region (putamen -Put -in this example) was correlated to the time series of each voxel of the target region (here the hippocampus -HC) separately for each of the three runs (only a single exemplar run depicted here). This resulted in distinct multivoxel connectivity pattern vectors containing the r-to-z transformed correlation for the n voxels of the target region. (B) Depiction of seed / target ROI pairs: left panel depicts Put as the seed and HC as the target (reflecting the multivoxel pattern of HC connectivity with the Put) whereas the right panel illustrates the HC seed and the Put target (reflecting the multivoxel pattern of Put connectivity with the HC). (C) Violin plots (Bechtold, 2016) depicting the Similarity Indices (SI; Fisher's Z-transformed correlation coefficients) for relevant pairs of fMRI runs (pre RS to MSL task and MSL task to post RS). The postlearning rest multivoxel pattern of HC connectivity with the Put was significantly less similar to the task connectivity pattern as compared to pre-learning rest (left panel). Conversely, the similarity between the RS and task-related multivoxel pattern of Put connectivity with the HC did not differ between the two RS runs (right panel). (D) Violin plots depicting explained variance (EV) and reverse explained variance (REV) for the 2 seed-target combinations. EV
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Memory consolidation is thought to be mediated by the offline reactivation of brain regions recruited during initial learning. Evidence for hippocampal reactivation in humans comes from studies showing that hippocampal response patterns elicited during learning can persist into subsequent rest intervals. Such investigations have largely been limite...
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Context 1
... indicated that the multivoxel pattern of hippocampal connectivity with the putamen (i.e., putaminal seed and hippocampal target) was significantly altered by task practice. Specifically, the connectivity pattern during post-learning, as compared to pre-learning, rest was less similar to the task pattern (t (54) Figure 3C, right panel). These results collectively indicate that task-related multivoxel patterns of hippocampal and striatal connectivity do not persist during subsequent rest. ...
Context 2
... results were observed using the partial correlation approach ( Figure 3D). Specifically, EV was significantly lower than REV for the multivoxel pattern of hippocampal connectivity with the putamen (z(54) = 3.22, p(unc) = 0.001, p(FDR) = 0.003, r = 0.31), indicating the post RS pattern explained less variance in the task pattern than pre RS. ...
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Memory consolidation, the process by which newly encoded and fragile memories become more robust, is thought to be supported by the reactivation of brain regions - including the hippocampus - during post-learning rest. While hippocampal reactivations have been demonstrated in humans in the declarative memory domain, it remains unknown whether such...
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... This leads to the following intriguing question: Is the reactivation of learning-related hippocampal activity a mechanism that also supports memory consolidation in the motor domain? Research released over the last two years has indeed offered evidence supporting the reinstatement of motor-task-related hippocampal activity during subsequent offline episodes 7,8 . Specifically, Buch and colleagues 7 leveraged the high temporal resolution of magnetoencephalography (MEG) and demonstrated temporally condensed reactivation of task-related hippocampal activity during the rest blocks following motor sequence task practice. ...
Memory consolidation in the declarative memory domain is known to be supported by the replay or reactivation of learning-related hippocampal activity during subsequent offline epochs (i.e., during post-encoding rest). Examinations into an analogous hippocampal reactivation process following motor learning have, until recently, been non-existent. This gap in the literature has been fueled by the traditional – yet outdated - view that the hippocampus is not involved in motor learning. Here, we discuss recent research in the motor memory domain that provides evidence in support of hippocampal reactivation following motor sequence learning. We conclude by highlighting several areas that warrant examination in future research, including experimentally manipulating post-learning hippocampal reactivation in an effort to enhance the motor memory consolidation process.
... Indeed, the cortisol concentration, higher in the morning than in the afternoon, was negatively correlated with neural plasticity. Although the influence of the cortisol level on motor consolidation must directly be evaluated, we could speculate that a high level of cortisol during the training 40,41 , while the degree of its activation, associated with that of the striatum during PP, seems to predict the performance gain after a night of sleep 42,43 . ...
Time-of-day influences both physical and mental performances. Its impact on motor learning is, however, not well established yet. Here, using a finger tapping-task, we investigated the time-of-day effect on skill acquisition (i.e., immediately after a physical or mental practice session) and consolidation (i.e., 24 h later). Two groups (one physical and one mental) were trained in the morning (10 a.m.) and two others (one physical and one mental) in the afternoon (3 p.m.). We found an enhancement of motor skill following both types of practice, whatever the time of the day, with a better acquisition for the physical than the mental group. Interestingly, there was a better consolidation for both groups when the training session was scheduled in the afternoon. Overall, our results indicate that the time-of-day positively influences motor skill consolidation and thus must be considered to optimize training protocols in sport and clinical domains to potentiate motor learning.
... A complementary hypothesis, needing certainly further exploration, implicates the hippocampus. Animal studies have reported that this area is under circadian influence 40,41 , while the degree of its activation, associated with that of the striatum during PP, seems to predict the performance gain after a night of sleep 42,43 . ...
Time-of-day influences both physical and mental performance. Its impact on motor learning is, however, not well established yet. Here, using a finger tapping-task, we investigated the time-of-day effect on skill acquisition (i.e., immediately after a physical or mental practice session) and consolidation (i.e., 24 hours later). Two groups (one physical and one mental) were trained in the morning (10 a.m.) and two others (one physical and one mental) in the afternoon (3 p.m.). We found an enhancement of motor skill following both types of practice, whatever the time of the day, with a better acquisition for the physical than the mental group. Interestingly, there was a better consolidation for both groups when the training session was scheduled in the afternoon. Overall, our results indicate that the time-of-day positively influences motor skill consolidation and thus must be considered to optimize training protocols in sport and clinical domains to potentiate motor (re)learning.
... Such an approach affords the opportunity to study the similarity of multivoxel brain patterns between different fMRI runs or sessions (e.g., at rest and/or during task practice). Using MVCS analyses, earlier studies have shown that local brain response patterns that were related to the learning episode can persist into subsequent rest periods, i.e., during the early offline consolidation window that immediately follows learning 32,34,35 . Importantly, the persistence of brain patterns is thought to reflect "replay" of learning-related neural patterns and to be functionally relevant as it can predict subsequent memory retention 32,34 . ...
... stimulation would specifically affect these learning-related brain patterns. Additionally, based on the critical involvement of the hippocampus in motor sequence learning 4 and on previous reports showing persistence of hippocampal patterns into rest after both declarative 8,32,34,[38][39][40] and motor 35 learning, we predicted that motor sequence learning would induce greater hippocampal pattern persistence into post-task rest as compared to random practice. As inhibitory stimulation was expected to induce greater modulation of task-related patterns in the hippocampus as compared to facilitatory stimulation, we predicted that persistence of hippocampal task patterns into post-learning rest would be stronger under inhibitory prefrontal stimulation. ...
... The resulting co-registration parameters were then applied to the realigned functional images. To optimize voxel pattern analyses, functional and anatomical data remained in subject-specific (i.e., native) space, and no spatial smoothing was applied to functional images 32,35 . www.nature.com/scientificreports/ ...
Motor sequence learning (MSL) is supported by dynamical interactions between hippocampal and striatal networks that are thought to be orchestrated by the prefrontal cortex. In the present study, we tested whether individually-tailored theta-burst stimulation of the dorsolateral prefrontal cortex (DLPFC) prior to MSL can modulate multivoxel response patterns in the stimulated cortical area, the hippocampus and the striatum. Response patterns were assessed with multivoxel correlation structure analyses of functional magnetic resonance imaging data acquired during task practice and during resting-state scans before and after learning/stimulation. Results revealed that, across stimulation conditions, MSL induced greater modulation of task-related DLPFC multivoxel patterns than random practice. A similar learning-related modulatory effect was observed on sensorimotor putamen patterns under inhibitory stimulation. Furthermore, MSL as well as inhibitory stimulation affected (posterior) hippocampal multivoxel patterns at post-intervention rest. Exploratory analyses showed that MSL-related brain patterns in the posterior hippocampus persisted into post-learning rest preferentially after inhibitory stimulation. These results collectively show that prefrontal stimulation can alter multivoxel brain patterns in deep brain regions that are critical for the MSL process. They also suggest that stimulation influenced early offline consolidation processes as evidenced by a stimulation-induced modulation of the reinstatement of task pattern into post-learning wakeful rest.
... As previous research has suggested that motor and declarative memory reactivation are supported by both common (e.g. 20) and distinct ( e.g., 21) neural processes, it remains unclear whether TMR triggers the same electrophysiological modulations across the two memory systems. Therefore, the goal of the present study was to elucidate the neurophysiological processes supporting memory reactivation during sleep which underlie TMR-induced enhancement in motor memory consolidation. ...
Targeted memory reactivation (TMR) during post-learning sleep is known to enhance motor memory consolidation but the underlying neurophysiological processes remain unclear. Here, we confirm the beneficial effect of auditory TMR on motor performance. At the neural level, TMR enhanced slow waves (SW) characteristics. Additionally, greater TMR-related phase-amplitude coupling between slow (0.3-2 Hz) and sigma (12-16 Hz) oscillations after the SW peak was related to higher TMR effect on performance. Importantly, sounds that were not associated to learning strengthened SW-sigma coupling at the SW trough and the increase in sigma power nested in the trough of the potential evoked by these unassociated sounds was related to the TMR benefit. Altogether, our data suggest that, depending on their precise temporal coordination during post learning sleep, slow and sigma oscillations play a crucial role in either memory reinstatement or protection against irrelevant information; two processes that critically contribute to motor memory consolidation.
... Such an approach affords the opportunity to study the similarity of multivoxel brain patterns between different fMRI runs or sessions (e.g., at rest and/or during task practice). Using MVCS analyses, earlier studies have shown that local brain response patterns that were related to the learning episode can persist into subsequent rest periods (i.e., after learning has ended 32,34,35 ). Importantly, the persistence of brain patterns is thought to reflect "replay" of learning-related neural patterns and to be functionally relevant as it can predict subsequent memory retention 32,34 . ...
... The resulting coregistration parameters were then applied to the realigned functional images. To optimize voxel pattern analyses, functional and anatomical data remained in subject-specific (i.e., native) space, and no spatial smoothing was applied to functional images 32,35 . ...
... The analysis pipeline was written in Matlab and followed similar procedures as in 35 . Prior to running MVCS analyses, additional preprocessing of the time series was completed. ...
Motor sequence learning (MSL) is supported by dynamical interactions between hippocampal and striatal networks that are thought to be orchestrated by the prefrontal cortex. In the present study, we tested whether individually-tailored theta-burst stimulation of the dorsolateral prefrontal cortex (DLPFC) prior to MSL, can modulate multivoxel response patterns in the stimulated cortical area, the hippocampus and the striatum. Response patterns were assessed with multivoxel correlation structure analyses of functional magnetic resonance imaging data acquired during task practice and during resting-state scans before and after learning/stimulation. Results revealed that, across stimulation conditions, MSL induced greater modulation of task-related DLPFC multivoxel patterns than random practice. A similar learning-related modulatory effect was observed on sensorimotor putamen patterns under inhibitory stimulation. Furthermore, MSL as well as inhibitory stimulation affected (posterior) hippocampal multivoxel patterns at post-intervention rest. Exploratory analyses showed that MSL-related brain patterns in the posterior hippocampus persisted into post-learning rest preferentially after inhibitory stimulation. These results collectively show that prefrontal stimulation can alter multivoxel brain patterns in deep brain regions that are critical for the MSL process. They also suggest that stimulation influenced early offline consolidation processes as evidenced by a stimulation-induced modulation of the reinstatement of task pattern into post-learning wakeful rest.
As opposed to those requiring a single action for reward acquisition, tasks necessitating action sequences demand that animals learn action elements and their sequential order and sustain the behaviour until the sequence is completed. With repeated learning, animals not only exhibit precise execution of these sequences but also demonstrate enhanced smoothness and efficiency. Previous research has demonstrated that midbrain dopamine and its major projection target, the striatum, play crucial roles in these processes. Recent studies have shown that dopamine from the substantia nigra pars compacta (SNc) and the ventral tegmental area (VTA) serve distinct functions in action sequence learning. The distinct contributions of dopamine also depend on the striatal subregions, namely the ventral, dorsomedial and dorsolateral striatum. Here, we have reviewed recent findings on the role of striatal dopamine in action sequence learning, with a focus on recent rodent studies.
Functional brain responses in hippocampo- and striato-cortical networks during initial motor sequence learning (MSL) are critical for memory consolidation. We have recently shown that prefrontal stimulation applied prior to initial MSL can alter these learning-related responses. In the present study, we investigated whether such stimulation-induced modulations of brain responses can influence motor memory consolidation at different timescales. Specifically, we examined the effect of prefrontal stimulation on the behavioral and neural responses associated to (i) fast consolidation processes occurring during short rest episodes interspersed with practice during initial learning (i.e., micro timescale) and (ii) slow consolidation process taking place across practice sessions separated by 24h (i.e., macro timescale). To do so, we applied active (inhibitory or facilitatory) or control theta-burst stimulation to the prefrontal cortex of young healthy participants before they were trained on an MSL task while their brain activity was recorded using functional magnetic resonance imaging (fMRI). Motor performance was retested, in the MRI scanner, after a night of sleep. Both our behavioral and brain imaging results indicate that while stimulation did not modulate consolidation at the macro timescale, it disrupted the micro-offline consolidation process. Specifically, our behavioral data indicate that active - as compared to control - stimulation resulted in a decrease in micro-offline gains in performance over the short rest intervals. At the brain level, stimulation disrupted activity in the caudate nucleus and the hippocampus during the micro-offline intervals. Additionally, multivariate pattern persistence from task into inter-practice rest episodes - which is thought to reflect the reactivation of learning-related patterns - was hindered by active prefrontal stimulation in the hippocampus and caudate nucleus. Importantly, stimulation also altered the link between the brain and the behavioral markers of the micro-offline consolidation process. These results collectively suggest that active prefrontal stimulation prior to MSL disrupted both the behavioral and neural correlates of motor memory consolidation at the micro timescale.
While the time-of-day significantly impacts motor performance, its effect on motor learning has not yet been elucidated. Here, we investigated the influence of the time-of-day on skill acquisition (i.e., skill improvement immediately after a training-session) and consolidation (i.e., skill retention after a time interval). Three groups were trained at 10 a.m. (G10am), 3 p.m. (G3pm), or 8 p.m. (G8pm) on a finger-tapping task. We recorded the skill (i.e. the ratio between movement duration and accuracy), before and immediately after the training to evaluate skill acquisition, and after 24 hours, to measure skill consolidation. We did not observe any difference in acquisition according to the time of the day. However, we found an improvement in performance 24 hours after the evening training (G8pm) while the morning (G10am) and the afternoon (G3pm) groups deteriorated and stabilized their performance, respectively. Furthermore, two control experiments (G8wake and G8sleep) supported the idea that a night of sleep contributes to the skill consolidation of the evening group. These results show an influence of time-of-day on the consolidation process, with better consolidation when the training is carried out in the evening. This finding may have an important impact on the planning of training programs in sports, clinical, or experimental domains.
