Does Motor Memory Reactivation through Practice and Post-Learning Sleep Modulate Consolidation?

Retrieving previously stored information makes memory traces labile again and can trigger restabilization in a strengthened or weakened form depending on the reactivation condition. Available evidence for long-term performance changes upon reactivation of motor memories and the effect of post-learning sleep on their consolidation remains scarce, and so does the data on the ways in which subsequent reactivation of motor memories interacts with sleep-related consolidation. Eighty young volunteers learned (Day 1) a 12-element Serial Reaction Time Task (SRTT) before a post-training Regular Sleep (RS) or Sleep Deprivation (SD) night, either followed (Day 2) by morning motor reactivation through a short SRTT testing or no motor activity. Consolidation was assessed after three recovery nights (Day 5). A 2 × 2 ANOVA carried on proportional offline gains did not evidence significant Reactivation (Morning Reactivation/No Morning Reactivation; p = 0.098), post-training Sleep (RS/SD; p = 0.301) or Sleep*Reactivation interaction (p = 0.257) effect. Our results are in line with prior studies suggesting a lack of supplementary performance gains upon reactivation, and other studies that failed to disclose post-learning sleep-related effects on performance improvement. However, lack of overt behavioural effects does not detract from the possibility of sleep- or reconsolidation-related covert neurophysiological changes underlying similar behavioural performance levels

Dynamics of sleep-dependent brain structural reorganization & consolidation of motor learning

Currently available data indicate that microstructural brain changes underlying motor sequence learning can be evidenced already in the very short term (i.e. less than one hour). Further evidence suggests that post-training sleep contributes to consolidate motor memories, potentially leading to enduring microstructural modifications. Moreover, strengthening a recent memory trace by triggering its (re)processing during sleep is feasible through Targeted Memory Reactivation (TMR), by cueing previously learned material during post-training sleep. Whether TMR leads to additional changes in the brain’s microstructure remains yet to be determined.Ninety young, healthy adults underwent five Diffusion Weighted Imaging (DWI) sessions, participated in two sequential motor trainings, and experienced either a post-training night of total sleep deprivation (SD), regular sleep (RS), or sleep with TMR, spread over five days. We combined standard Diffusion Tensor Imaging (DTI) with Neurite Orientation Dispersion & Density Imaging (NODDI) to assess dendritic and axonal complexity more accurately in grey matter.Significant learning-induced changes were observed across extensive occipitoparietal and temporal regions, as well as in the cerebellar cortex and motor-related subcortical areas such as the thalamus, putamen, and hippocampus. Notably, reductions in Mean Diffusivity (MD) within cortical areas corresponded with decreases in Free Water Fraction (FWF) and increases in Neurite Density Index (NDI). Subcortical regions also showed MD reductions, with FWF reductions observed in all areas except the putamen, which exhibited a marked NDI increase suggesting enhanced neurite density. On day 5, a limited persistence of these learning-induced modifications was noted. A subsequent retraining session elicited noticeable cortical changes, albeit less extensive than those observed during initial learning. At the subcortical level, modifications were detected in the putamen and the thalamus, as well as in the cerebellar cortex. Furthermore, the caudate nucleus exhibited structural variations throughout the procedure, with the timing of these changes differing among groups (SD/RS/TMR). Sleep-related consolidation (SD vs. RS) did not markedly influence diffusion parameters within the observed timeframe. However, differences were noted between the RS and TMR groups, suggesting that TMR during sleep may affect specific brain regions implicated in motor-sequence learning. Our findings underscore the rapid, learning-related reorganization that occur in specific cortical and subcortical areas, reflecting a shift from hippocampal engagement in the early stages of skill learning, to more striatal regions in later stages, mirroring prior functional studies showing a similar dynamic. It is noticeable, however, that this transition did not occur uniformly across all our participants, particularly in the caudate nucleus, possibly influenced by the strength of the memory trace formed by the end of learning, or the performance strategic mode (speed/accuracy) individuals decided to prioritize.Furthermore, we explored the impact of a brief behavioral reactivation of the memory trace at wake following post-training sleep or sleep deprivation on delayed motor performance. Existing evidence on the long-term effects of motor memory reactivation at wake and its interplay with sleep-related consolidation is sparse, highlighting the need for further exploration into the mechanisms underpinning long-term performance enhancements post reactivation.With this project, we aimed at deepening our understanding of the complex interconnections between learning, sleep, neuronal plasticity, and the efficacy of targeted reactivation approaches in enhancing motor memory consolidation. Here, we highlight the dynamics of structural plasticity processes inherent to learning and memory consolidation.Doctorat en Sciences psychologiques et de l'éducationinfo:eu-repo/semantics/nonPublishe

Does Motor Memory Reactivation through Practice and Post-Learning Sleep Modulate Consolidation?

Retrieving previously stored information makes memory traces labile again and can trigger restabilization in a strengthened or weakened form depending on the reactivation condition. Available evidence for long-term performance changes upon reactivation of motor memories and the effect of post-learning sleep on their consolidation remains scarce, and so does the data on the ways in which subsequent reactivation of motor memories interacts with sleep-related consolidation. Eighty young volunteers learned (Day 1) a 12-element Serial Reaction Time Task (SRTT) before a post-training Regular Sleep (RS) or Sleep Deprivation (SD) night, either followed (Day 2) by morning motor reactivation through a short SRTT testing or no motor activity. Consolidation was assessed after three recovery nights (Day 5). A 2 × 2 ANOVA carried on proportional offline gains did not evidence significant Reactivation (Morning Reactivation/No Morning Reactivation; p = 0.098), post-training Sleep (RS/SD; p = 0.301) or Sleep*Reactivation interaction (p = 0.257) effect. Our results are in line with prior studies suggesting a lack of supplementary performance gains upon reactivation, and other studies that failed to disclose post-learning sleep-related effects on performance improvement. However, lack of overt behavioural effects does not detract from the possibility of sleep- or reconsolidation-related covert neurophysiological changes underlying similar behavioural performance levels.info:eu-repo/semantics/publishe

Post-learning micro- and macro-structural neuroplasticity changes with time and sleep.

Neuroplasticity refers to the fact that our brain can partially modify both structure and function to adequately respond to novel environmental stimulations. Neuroplasticity mechanisms are not only operating during the acquisition of novel information (i.e. online) but also during the offline periods that take place after the end of the actual learning episode. Structural brain changes as a consequence of learning have been consistently demonstrated on the long term using non-invasive neuroimaging methods, but short-term changes remained more elusive. Fortunately, the swift development of advanced MR methods over the last decade now allows tracking fine-grained cerebral changes on short timescales beyond gross volumetric modifications stretching over several days or weeks. Besides a mere effect of time, post-learning sleep mechanisms have been shown to play an important role in memory consolidation and promote long-lasting changes in neural networks. Sleep was shown to contribute to structural modifications over weeks of prolonged training, but studies evidencing more rapid post-training sleep structural effects linked to memory consolidation are still scarce in human. On the other hand, animal studies convincingly show how sleep might modulate synaptic microstructure. We aim here at reviewing the literature establishing a link between different types of training/learning and the resulting structural changes, with an emphasis on the role of post-training sleep and time in tuning these modifications. Open questions are raised such as the role of post-learning sleep in macrostructural changes, the links between different MR structural measurement-related modifications and the underlying microstructural brain processes, and bidirectional influences between structural and functional brain changes.info:eu-repo/semantics/publishe

Beta-tACS does not impact the dynamics of motor memory consolidation

SCOPUS: le.jinfo:eu-repo/semantics/publishe

Microstructural dynamics of motor learning and sleep-dependent consolidation: A diffusion imaging study

International audienceMemory consolidation can benefit from post-learning sleep, eventually leading to long-term microstructural brain modifications to accommodate new memory representations. Non-invasive diffusion-weighted magnetic resonance imaging (DWI) allows the observation of (micro)structural brain remodeling after time-limited motor learning. Here, we combine conventional diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) that allows modeling dendritic and axonal complexity in gray matter to investigate with improved specificity the microstructural brain mechanisms underlying time- and sleep-dependent motor memory consolidation dynamics. Sixty-one young healthy adults underwent four DWI sessions, two sequential motor trainings, and a night of total sleep deprivation or regular sleep distributed over five days. We observed rapid-motor-learning-related remodeling in occipitoparietal, temporal, and motor-related subcortical regions, reflecting temporary dynamics in learning-related neuronal brain plasticity processes. Sleep-related consolidation seems not to exert a detectable impact on diffusion parameters, at least on the timescale of a few days

Correction of Susceptibility Distortion in EPI: A Semi-supervised Approach with Deep Learning

Echo planar imaging (EPI) is the most common approach for acquiring diffusion and functional MRI data due to its high temporal resolution. However, this comes at the cost of higher sensitivity to susceptibility-induced B0 field inhomogeneities around air/tissue interfaces. This leads to severe geometric distortions along the phase encoding direction (PED). To correct this distortion, the standard approach involves an analogous acquisition using an opposite PED leading to images with inverted distortions and then non-linear image registration, with a transformation model constrained along the PED, to estimate the voxel-wise shift that undistorts the image pair and generates a distortion-free image. With conventional image registration approaches, this type of processing is computationally intensive. Recent advances in unsupervised deep learning-based approaches to image registration have been proposed to drastically reduce the computational cost of this task. However, they rely on maximizing an intensity-based similarity measure, known to be suboptimal surrogate measures of image alignment. To address this limitation, we propose a semi-supervised deep learning algorithm that directly leverages ground truth spatial transformations during training. Simulated and real data experiments demonstrate improvement to distortion field recovery compared to the unsupervised approach, improvement image similarity compared to supervised approach and precision similar to TOPUP but with much faster processing.SCOPUS: cp.kinfo:eu-repo/semantics/publishe

Sleep‐dependent structural neuroplasticity after a spatial navigation task: A diffusion imaging study

Evidence for sleep-dependent changes in microstructural neuroplasticity remains scarce, despite the fact that it is a mandatory correlate of the reorganization of learning-related functional networks. We investigated the effects of post-training sleep on structural neuroplasticity markers measuring standard diffusion tensor imaging (DTI), mean diffusivity (MD), and the revised biophysical neurite orientation dispersion and density imaging (NODDI), free water fraction (FWF), and neurite density (NDI) parameters that enable disentangling whether MD changes result from modifications in neurites or in other cellular components (e.g., glial cells). Thirty-four healthy young adults were scanned using diffusion-weighted imaging (DWI) on Day1 before and after 40-min route learning (navigation) in a virtual environment, then were sleep deprived (SD) or slept normally (RS) for the night. After recovery sleep for 2 nights, they were scanned again (Day4) before and after 40-min route learning (navigation) in an extended environment. Sleep-related microstructural changes were computed on DTI (MD) and NODDI (NDI and FWF) parameters in the cortical ribbon and subcortical hippocampal and striatal regions of interest (ROIs). Results disclosed navigation learning-related decreased DWI parameters in the cortical ribbon (MD, FWF) and subcortical (MD, FWF, NDI) areas. Post-learning sleep-related changes were found at Day4 in the extended learning session (pre- to post-relearning percentage changes), suggesting a rapid sleep-related remodeling of neurites and glial cells subtending learning and memory processes in basal ganglia and hippocampal structures

Sleep-dependent structural neuroplasticity after a spatial navigation task: A diffusion imaging study.

Evidence for sleep-dependent changes in microstructural neuroplasticity remains scarce, despite the fact that it is a mandatory correlate of the reorganization of learning-related functional networks. We investigated the effects of post-training sleep on structural neuroplasticity markers measuring standard diffusion tensor imaging (DTI), mean diffusivity (MD), and the revised biophysical neurite orientation dispersion and density imaging (NODDI), free water fraction (FWF), and neurite density (NDI) parameters that enable disentangling whether MD changes result from modifications in neurites or in other cellular components (e.g. glial cells). Thirty-four healthy young adults were scanned using diffusion-weighted imaging (DWI) on Day1 before and after 40-min route learning (navigation) in a virtual environment, then were sleep deprived (SD) or slept normally (RS) for the night. After recovery sleep for 2 nights, they were scanned again (Day4) before and after 40-min route learning (navigation) in an extended environment. Sleep-related microstructural changes were computed on DTI (MD) and NODDI (NDI and FWF) parameters in the cortical ribbon and subcortical hippocampal and striatal regions of interest (ROIs). Results disclosed navigation learning-related decreased DWI parameters in the cortical ribbon (MD, FWF) and subcortical (MD, FWF, NDI) areas. Post-learning sleep-related changes were found at Day4 in the extended learning session (pre- to post-relearning percentage changes), suggesting a rapid sleep-related remodeling of neurites and glial cells subtending learning and memory processes in basal ganglia and hippocampal structures.info:eu-repo/semantics/publishe