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.

Evidence for sleep-dependent changes in micro-structural 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-minutes 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-minutes 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 remodelling of neurites and glial cells subtending learning and memory processes in basal ganglia and hippocampal structures..

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.