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Uncorrected Proof Biochemical Pharmacology xxx (xxxx) xxx-xxx Contents lists available at ScienceDirect Biochemical Pharmacology journal homepage: http://ees.elsevier.com Review Post-learning micro- and macro-structural neuroplasticity changes with time and sleep Whitney Stee a,b,⁎, Philippe Peigneux a,b,⁎ a UR2NF-Neuropsychology and Functional Neuroimaging Research Unit Affiliated at CRCN – Centre for Research in Cognition and Neurosciences, Avenue F.D, Roosevelt 50, Bruxelles 1050, Belgium b UNI-ULB Neuroscience Institute, Université Libre de Bruxelles (ULB), Avenue F.D. Roosevelt 50, Bruxelles 1050, Belgium ARTICLE INFO ABSTRACT PROOF Keywords Neuroplasticity refers to the fact that our brain can partially modify both structure and function to adequately Structural neuroimaging respond to novel environmental stimulations. Neuroplasticity mechanisms are not only operating during the ac- Diffusion weighted imaging (DWI) quisition of novel information (i.e., online) but also during the offline periods that take place after the end of the Neuroplasticity actual learning episode. Structural brain changes as a consequence of learning have been consistently demon- Learning and memory consolidation strated on the long term using non-invasive neuroimaging methods, but short-term changes remained more elu- Sleep sive. 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 im- portant 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 struc- tural measurement-related modifications and the underlying microstructural brain processes, and bidirectional influences between structural and functional brain changes. 1. Introduction decades evidenced functional brain changes in response to learning (e.g., [4,7–10]). In comparison, evidence for structural brain changes Neuroplasticity refers to the fact that our brain can partially mod- remains narrow, a paucity partly due to the fact that non-invasive ify both structure and function to adequately respond to novel envi- brain imaging techniques such as structural magnetic resonance imaging ronmental stimulations [1,2]. Neuroplasticity mechanisms are not only (MRI) have for long exhibited restricted sensitivity to subtle, short-term taking place during the acquisition of novel information (i.e., online) changes in neural architectonics, hence preventing the detection of but also during the offline periods taking place after the end of the ac- rapid structural brain remodelling. We aim here at reviewing the avail- tual learning episode. In the short term, synaptic plasticity involves cel- able evidence establishing a link between different types of training/ lular determinants of synaptic strength and persistence that are trig- learning and the ensuing structural changes, with an emphasis on the gered within individual neurons in the minutes and hours after mem- role of post-training sleep and time in fine-tuning these modifications. ory encoding [3]. Later on, memory consolidation gradually devel- Open questions will be raised such as the role of post-learning sleep in ops at the systems level [4] over extended periods (days to weeks) of macrostructural changes, the link between different MR structural mea- time and sleep to shape and integrate novel memories into pre-existing surement-related modifications, the underlying microstructural brain brain networks [5,6]. In humans, many studies conducted over the past processes, and bidirectional influences between structural and func- tional brain changes. ⁎ Corresponding authors at: UR2NFUNCORRECTED-Neuropsychology and Functional Neuroimaging Research Unit Affiliated at CRCN – Centre for Research in Cognition and Neurosciences, Avenue F.D, Roosevelt 50, Bruxelles 1050, Belgium. E-mail addresses: [email protected] (W. Stee); [email protected] (P. Peigneux) https://doi.org/10.1016/j.bcp.2020.114369 Received 27 October 2020; Received in revised form 10 December 2020; Accepted 11 December 2020 Available online xxx 0006-2952/© 2020. W. Stee and P. Peigneux Biochemical Pharmacology xxx (xxxx) xxx-xxx 1.1. Learning-related macrostructural changes anisotropy [FA] values that estimate tissue density and fibre organiza- tion/directionality, respectively) after various forms of exercise, includ- Longitudinal MRI studies conducted in human participants have dis- ing procedural [24,29,30], working memory [31,32] and intensive closed learning-related changes in cortical thickness and grey matter reading [33,34] training, and second language acquisition [35]. WM (GM) volume (using surface-based [SBM] and voxel-based [VBM] mor- changes were often found to parallel GM alterations when both mea- phometry analyses, respectively) that take place within a timescale rang- surements were available [24,26,36,37]. Also, both correlated with be- ing weeks to months following training. In the motor skill domain, havioural improvement, indicating that both GM and WM are capable of 3 months of juggling practice was initially shown to result in a bilateral structural plasticity and remodelling following the acquisition of novel GM expansion in the occipito-temporal junction (hMT/V5) as well as in information [11,15–17,20,24,25,33]. the left posterior intraparietal sulcus in young adults [11,12] (Fig. 1), or in the left hippocampus and nucleus accumbens in elderly partici- 1.2. Cellular mechanisms underlying macrostructural changes pants [13]. Noticeably, follow-up analyses evidenced structural changes already As shown above, neuroimaging techniques and especially MRI/DWI after 7 days practice in hMT/V5 as well as in the frontal and tem- proved useful to evidence practice-related macrostructural brain re- poral lobes and in the cingulate cortex, bilaterally [14]. In the lan- modelling in human, as they are non-invasive and can be repeatedly guage domain, learning a second language was associated with in- used to monitor changes in the cerebral architecture within a same creased cortical thickness in the left anterior cingulate cortex (ACC) individual. Notwithstanding, standard MRI/DWI measurements are in- [15], right [15] and left middle temporal (MTG), left superior tem- herently limited even if sensitive. Indeed, their outcomes lack speci- poral (STG) and left inferior frontal (IFG) [16] gyri. VBM also evi- ficity and does not provide information about the underlying cellu- denced increased hippocampal volume [16] and GM density in the left lar processes [38,39], leaving unclear the link between neuroimaging IFG and anterior temporal lobe (ATL) [17]. Extending these findings measurements and their underlying biology. The candidate mechanisms onto other domains, GM modifications have been reported following underlying GM, WM andPROOFextra-neuronal modifications in response to cognitive [18,19], action observation [20], video game [21,22], golf learning have been discussed (see e.g. [39] for a detailed review). Re- [23], balance [24] and musical [25] training, as well as conceptual garding GM changes, neurogenesis was amongst the first suggested un- knowledge [12,26,27] and Morse code [28] learning. Similarly, white derlying mechanisms, since adult-born neurons were found to develop matter (WM) changes are observed using standard diffusion weighted [40–43] into fully functional neurons [44] in rodents in response to imaging (DWI) measurements (e.g., using diffusion tensor imaging [DTI] learning. However, this phenomenon is from small nature [40] and analyses computing mean diffusivity [MD] and increased fractional mostly evidenced in hippocampal area [41–44]; neocortical neuroge- nesis remains controversial to this day [45–47]. By contrast, rodent UNCORRECTED Fig. 1. Learning-induced brain structural changes. Statistical parametric maps evidencing a expansion in GM in the left posterior intraparietal sulcus (a) and bilaterally in the mid-temporal area (hMT/V5; b-c) following a 3-months juggling training in young adults [11]. These transient changes tended to decrease within the 3 next months as reflected by the boxplots showing the range, mean and standard deviation of GM change (d). Reprinted from [11], Fig. 1 page 311. 2 W. Stee and P. Peigneux Biochemical Pharmacology xxx (xxxx) xxx-xxx studies show that learning usually elicits synaptogenesis and neuronal 1.3. Sleep and memory consolidation: structural changes morphology changes on a larger scale, including neocortical areas such as the motor cortex [48–52] (Fig. 2). Besides a mere effect of time, post-learning sleep mechanisms play Additionally, sprouting of new mossy fibre terminals was observed an important role in memory consolidation by promoting long-last- following
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