Longitudinal structural MRI (sMRI) of the human brain has revealed experience-dependent local changes in estimates of gray matter volume (GMV) (Draganski et al., 2004). Recent evidence suggests that these changes in GMV follow a nonlinear pattern throughout training (Wenger et al., 2017b). The specific biological components that elicit these volumetric changes are not understood. From observations in rodents, it has been suggested that the cellular basis of changes in GMV detected using sMRI may, in part, be due to learning-dependent changes in dendritic spine density (Keifer et al., 2015), alterations in astrocyte volume (Kleim et al., 2007; Woo et al., 2018), and adaptations within intracortical myelin (McKenzie et al., 2014; Keiner et al., 2017; Kougioumtzidou et al., 2017; Zatorre et al., 2012). Macroscopic plasticity of brain structure determined by sMRI has been directly associated with neuronal dendritic spine plasticity together with astrocyte reorganization in the absence of cell proliferation (Keifer et al., 2015; Schmidt et al., 2021), in which these plastic changes were observed to be transient. Furthermore, observations using two-session sMRI (before vs. after training) indicated macrostructural volumetric increases within areas involved in motor control, sensory processing, learning, and memory (Badea et al., 2019). At the microstructural level, using ex vivo diffusion tensor imaging (DTI), significant white matter (WM) differences have been observed in WM underlying motor cortex (Sampaio-Baptista et al., 2013), as well as experience-dependent DTI differences observed in GM structure (Sampaio-Baptista et al., 2020). Estimates of changes in GMV using sMRI reflect a composite mixture of these biological changes. For example, changes in astrocytes or synaptic remodeling may explain changes in regional GMV. However, changes in intracortical myelin may also affect estimates of GMV, such that increases in intracortical myelin may reduce estimates of GMV, and the inverse, for which decreases in intracortical myelin may increase estimates of GMV. Despite more recent developments of quantitative MRI (Natu et al., 2019; Weiskopf et al., 2013), the mix and interplay between GM and WM changes, with respect to time from the start of the learning paradigm, that result in nonlinear, experience-dependent, adaptive brain responses observed both in rodents and human studies remain unknown.

Myelin plasticity is critical for learning and memory, and myelin plasticity driven by both neuronal activity and experience has been described (Gibson et al., 2014; Mensch et al., 2015; Swire and Ffrench-Constant, 2018; Bacmeister et al., 2020; Wake et al., 2011; Makinodan et al., 2012). Active myelination by newly recruited oligodendrocytes has been shown to be necessary for learning and memory (McKenzie et al., 2014; Geraghty et al., 2019; Pan et al., 2020; Steadman et al., 2020). In addition, the learning of a novel skilled reaching task in rodents is associated with functional reorganization of cortical motor maps, including an expanded representation of the trained limb (Kleim et al., 1998; Kleim et al., 2004; Tennant et al., 2011). This functional remapping is accompanied by a variety of structural and functional changes, including synaptogenesis, increased spine formation, and glial changes (Kleim et al., 2004; Xu et al., 2009). It was very recently described that learning in a forelimb skilled-reaching paradigm transiently suppresses oligodendrogenesis while increasing oligodendrocyte precursor cell (OPC) differentiation, oligodendrocyte maturation, and myelin sheath remodeling in forelimb motor cortex (Bacmeister et al., 2020). Learning-induced suppression of oligodendrocytes was transient but left OPC differentiation unaffected, which suggests that learning may temporarily decrease survival and integration of differentiated OPCs as mature myelinating oligodendrocytes (Bacmeister et al., 2020), in line with previous work in the developing central nervous system (Hill et al., 2014). However, it is unknown whether adaptive myelination is restricted to discrete brain areas to enable fine-tuning of adaptive circuit responses. Furthermore, the temporal dynamics of myelin plasticity and how it relates to sMRI-based measurements of structural plasticity remain unknown.

In this study, we used the single-pellet skilled reaching task, a well-established paradigm for motor skill learning research in rodents (Whishaw et al., 1986), combined with longitudinal MRI and cross-sectional immunohistochemical analyses, to evaluate the time course of experience-dependent brain changes and the related links between micro- and macrostructural changes. To analyze MRI data, we used voxel-based morphometry (VBM), a key technique to evaluate macroscopic changes in GMV that is frequently used to investigate a broad spectrum of neurological processes spanning from learning (Badea et al., 2019) and memory (Keifer et al., 2015) to neurodegeneration (Kim et al., 2020; Sawiak et al., 2009) and cognitive impairment (Bagdatlioglu et al., 2020). This morphometric technique calculates voxel-wise estimates of GMV, and in the cortex, GMV is dependent upon local cortical thickness and surface area (Ashburner and Friston, 2000), and on local tissue composition (Asan et al., 2021). Studies in brain plasticity using sMRI often rely on T1-weighted MR imaging that are sensitive to myelin, rendering the signal of a single voxel highly dependent on the presence of myelin, thereby influencing the estimated volume.

Gray matter (GM) and WM are classical anatomical terms to denote myelin-poor and myelin-rich regions of the brain. As part of the VBM analysis, the brain is segmented into different components reflecting GM, WM, and cerebral spinal fluid (CSF) with the use of study-specific tissue priors. The metric used for comparison in VBM depends in part on the water content in a voxel, which is influenced by the fraction of myelin to other tissue. GM regions of the brain contain myelin to a varying degree (e.g., intracortical myelin) and thus, both GMV and WMV can be estimated in GM regions of the brain. Therefore, myelin within a GM region of the brain may affect both GMV and WMV estimates in that region. To study morphometric changes in the brain during motor skill learning, we used both GMV and WMV estimates in GM regions and WMV estimates in WM regions. Throughout this article, we will employ GM and WM terms to refer to the classical anatomical regions of the brain and GMV and WMV to refer to the quantitative estimates calculated from the segmented images.

Through longitudinal in vivo sMRI of rodent brain, using a specific MR sequence with a magnetization transfer (MT) pulse to increase contrast between water-rich and water-poor tissue, combined with a cross-sectional immunohistochemical investigation, we describe structural plasticity dynamics during motor skill learning and the associated adaptive cortical myelination. This was studied in wild-type (WT) animals during learning of a skilled, single-pellet forelimb-reaching task. Structural MRI water-rich signal putatively reflects the abundance of myelinated axons, whereas sMRI water-poor signal putatively reflects the scarcity of myelinated axons. Thus, the specific sMRI sequence with MT allows the investigation of changes in putative myelin content in GM and WM regions of the brain. Plasticity in human brain structure has been typically assumed to follow a continuous linear or asymptotic increase throughout the time of training. However, a recent study in humans revealed GM expansion in motor cortex during the first 4 weeks of training followed by partial renormalization (Wenger et al., 2017b). To evaluate the fit of these three alternative models to the data, we tested the three corresponding regression models (Figure 2—figure supplement 2): (1) a linear model to reflect a continuous formation of candidate circuits, (2) an asymptotic model to reflect the recruitment of new candidate circuits followed by stabilization and, (3) a quadratic model to reflect an initial expansion phase followed by a complete renormalization. In this study, we found that motor learning dynamically modulates macrostructural brain plasticity, identified nonlinear decreases in GMV juxtaposed to nonlinear increases in WMV, and that these changes are associated with adaptive myelination in forelimb sensorimotor cortex.

Myelination, like synaptic plasticity, contributes to learning by activity‐dependent modification of an initially ‘hard‐wired’ circuitry (Bechler et al., 2018). The dynamics of myelin plasticity and how it relates to volumetric-based measurements of experience-dependent brain changes remain unknown. In this study, we combined motor skill learning in mice with longitudinal sMRI and immunohistochemistry to study the nature of structural changes that take place in the brain during learning.

Longitudinal in vivo sMRI acquired throughout learning a skilled, single-pellet forelimb reach task revealed bilateral nonlinear decreases in GMV juxtaposed to nonlinear increases in WMV modeled by an asymptotic time-course function. Specifically, using an atlas-based cortical mask, we found bilateral nonlinear changes with learning in primary and secondary motor areas and in somatosensory cortex. Supporting these results, a cross-sectional analysis unveiled an increase in myelin immunoreactivity in the somatosensory cortex for the forelimb, followed by a return toward baseline. Specifically, using confocal microscopy, we found a significant increase in the length density of myelinated axons from baseline to experimental day 6. Thereafter, the length density of myelinated axons decreases from experimental 6 to experimental day 14. The complementary cortical thickness analysis did not reveal any significant difference between trained animals and nontrained controls. This multimodal approach indicates that nonlinear changes observed in cortical GMV and WMV using VBM are likely caused by changes in tissue composition (e.g., more intracortical myelin, as suggested by the immunodetection analyses) rather than changes in cortical thickness or surface areas of SSp-ul, which are unlikely to happen in the relatively short period of a 15-day learning paradigm. These results are further corroborated by the additional finding of a significant correlation between morphometric WMV and myelin immunoreactivity in the same cortical area. Therefore, WMV calculated from WM-segmented T1-weighted MRI using an MT pulse represents myelin to a substantial degree. Altogether, these observations indicate a nonlinear increase of intracortical myelin with learning. Taking into consideration the observations from confocal microscopy of myelinated axons, we speculate that the nonlinear increase of intracortical myelin observed at experimental day 6 is produced by increased, or de novo, myelination of axons in cortical circuits rather than increases in the diameter of the existing myelinated axons.

Consistent with this idea, oligodendrocyte development has been reported to be required for motor learning in adult mice within the first hours after being introduced to the complex wheel running learning paradigm (Xiao et al., 2016). Furthermore, a recent study demonstrated that forelimb-skill reaching dynamically modulates myelination through OPC differentiation (Bacmeister et al., 2020). As well, the nonlinear changes observed in somatosensory cortex for the forelimb match with the well-characterized reorganization of forelimb representation during motor skill learning using electrophysiological measurements (Tennant et al., 2011).

As previously described in humans (Wenger et al., 2017b), and in mice (Badea et al., 2019), our in vivo morphometric analysis revealed bilateral changes in several brain regions, including SSp-ul. While structural changes contralateral to the trained forelimb were expected, strong cortical changes ipsilateral to the trained limb are still controversial. Changes in ipsilateral motor cortices are consistent with fMRI findings in humans showing bilateral MOp activation during the execution of a unilateral high-precision motor task (Buetefisch et al., 2014). This bilateral activation was later attributed to an inhibitory effect from ipsilateral motor cortex on contralateral motor cortex. Interestingly, this inhibitory effect was modulated by the demand on accuracy of the motor task (Wischnewski et al., 2016). Thus, ipsilateral changes in GMV and WMV with learning may be attributed to existing interhemispheric circuits that are strengthened or modified through adaptive myelination. Possible enhanced ipsilateral inhibitory activity may also lead to volumetric changes.

In contrast with our findings of GMV decreases, the study of experience-dependent volumetric changes in primary motor cortex in human adults, trained to write and trace with their nondominant hand, revealed a bilateral GMV expansion followed by a partial renormalization (Wenger et al., 2017b). It is possible that the different motor skill learning paradigm used here triggered different structural changes in the brain. On the other hand, these differences could also be attributed to the use of a higher magnetic field in mice than in humans (9.4 T vs. 3 T, respectively) or, perhaps most likely, a consequence of the MRI sequence, optimized for an enhanced detection of myelin. The use of in vivo sMRI in mice using two-session sMRI (before vs. after training) unveiled significant enlargements in GMV in multiple brain regions (Badea et al., 2019). In addition, rodent studies using ex vivo DTI with similar motor skill paradigms reported an overall increase in WM in cerebellum (Badea et al., 2019) and in corpus callosum directly below primary motor cortex (Sampaio-Baptista et al., 2013) after either 23–27 or 11 days of training, respectively. These studies were limited to trained animals versus nontrained control animals at endpoint, and therefore, it is not possible to draw conclusions regarding the temporal dynamics of brain plasticity with learning.

The WMV enlargement we observed in GM by VBM and by myelin immunohistochemistry exhibits an initial expansion, followed by a (partial) renormalization. These observations support the hypothesis that myelin is, at least in part, a component of an expansion-renormalization model comprising exploration, selection, and refinement stages (Wenger et al., 2017a; Makino et al., 2016; Lindenberger and Lövdén, 2019). It is likely that newly formed connections are strengthened in response to repetitive firing of neural circuits produced by a discrete sequence of movements (Milner et al., 1998). The increased activity within these circuits likely stimulates additional myelination of involved axons to provide increased efficiency. Experiments utilizing two-photon imaging after inducing monocular deprivation revealed neuron-class-specific myelin plasticity, suggesting that reconfiguration of network connectivity after sensory deprivation requires precise tuning of individual myelination profiles instead of a broad addition of myelin (Yang et al., 2020). Thus, the exploration phase of learning involves an expected rise in myelination during initial motor skill improvement, or acquisition. In later stages of motor learning, optimal circuitry is selected to perform the motor task that is subsequently refined through reinforcement and the nonefficient candidate circuits are pruned away (Kilgard, 2012). Interestingly, once the optimal circuitry is selected and refined, active myelination is no longer required to support recall and execution of a pre-learned skill (McKenzie et al., 2014). Our whole-brain analysis comparing the different possible time-course models identified that the majority of structural changes with learning follow a nonlinear pattern, suggesting that expansion followed by (partial) renormalization is a rather general phenomenon across regions and may be a common principle that unites many manifestations of structural plasticity. Moreover, there is an interestingly large, yet nonsignificant increase in myelin immunoreactivity during the pre-training days, suggesting that pre-training and task familiarization is sufficient to trigger formation of new neuronal connections, stimulating myelination. Consistent with this idea, a previous study in mice showed that the proliferation of OPCs was accelerated in motor cortex within just 4 hr of exposure to the complex wheel. In addition, the introduction to a novel skill learning paradigm stimulated OPC differentiation into newly forming oligodendrocytes after just 2.5 hr of self-training (Xiao et al., 2016). Although previous results using the SRT (Bacmeister et al., 2020) suggested that production of myelin does not appear until days or weeks later (for review, see Bonetto et al., 2021), here we observe that myelination is a rapid and dynamic plastic change throughout learning.

Higher learning rates were associated with a nonlinear/asymptotic model of the changes in the MRI-derived WMV. We could speculate that the selection and refinement phases of the expansion-renormalization model highly influence the proficiency of learning at the individual level. During the exploration phase, a large production of candidate circuits that could potentially elicit effective movements takes place, increasing myelination/myelin levels. However, the magnitude of increase in candidate circuits does not imply a better performance. With a large pool of candidate circuits, the serial activation of the different candidate circuits could lead to behavioral variability (a wide range of diverse performances). Nevertheless, the selection and reinforcement of the circuits that can reliably produce the target movement and the activity-dependent pruning (Faust et al., 2021) of the nonefficient candidates to carry out the task lead to a decrease in myelin levels and appear to be related with higher learning rates. Decreasing the number of circuits reduces the behavioral variability and implies better performance. Alternatively, it is possible that the learning-induced reinforcement of selected circuits by myelination contributes to improved communication and oscillatory synchrony within or between distributed neural populations (Noori et al., 2020). This plasticity may serve to stabilize oscillatory neural activity across brain regions to facilitate and preserve learned behaviors.

The quadratic changes in myelin immunoreactivity observed by densitometry appear to be influenced by an increase in the length density of myelinated axons followed by a decrease toward baseline levels. The observed increase in length density during the acquisition phase of learning goes in accordance with the idea of an exploration phase of learning associated with the production of substantial number of novel candidate circuits (myelinated axons). These newly formed connections would be lengthy but short in diameter. Afterward, during the consolidation phase of learning, optimal candidate circuits to carry out the task will be selected and refined, for which increases in myelin sheath diameter may occur.

VBM, a method that has been widely used during the past two decades to identify and characterize brain changes among populations, is used to detect systematic density differences of a particular tissue class. In VBM, each voxel in smoothed images contains the average amount of GM around that voxel (Ashburner and Friston, 2000). Although modulated GM density (mGM) and cortical thickness are completely different measures, both are commonly used to assess GMV. However, Chung and colleagues found discordances between mGM and cortical thickness analyses (Chung et al., 2017). A strong correlation between T1-weighted signal intensity and mGM was reported while no correlation was found between signal intensity and cortical thickness. In cortical VBM, if each voxel is a combination of mGM density, cortical thickness, and surface area, the interpretation of VBM results as volume alone is not entirely correct. Since we did not find significant changes in cortical thickness during learning and that it is unlikely that changes in surface area were produced during the 15-day learning paradigm, we can assume that the morphometric changes observed during learning are consequence of changes in GM and WM density and not volume. Furthermore, this assumption is in line with the changes we observed in myelin immunoreactivity at the histological level.

This study combined longitudinal in vivo MRI with cross-sectional analysis of myelin immunoreactivity in male mice. We limited our experiments to mice with the same sex to focus our study on the learning variable. Some studies incorporating females have indicated differences in certain basic biological processes (e.g., pain processing [Mogil et al., 2003] and mechanisms in neuromodulation [Huang and Woolley, 2012]). Thus, by limiting our study to males, any sex differences in the learning process both at the behavioral and structural levels are missing. Future experimental designs should include male and female subjects to identify whether differences exist between sexes.

In this study, we observed the temporal dynamics of experience-dependent macrostructural brain changes during motor skill learning, identified nonlinear decreases in GMV juxtaposed to nonlinear increases in WMV, and found that these changes are associated with adaptive myelination in forelimb sensorimotor cortex. Our results empirically back up the idea that myelination is a rapid initial and partly transient plastic change in learning and supports the use of VBM on WM structural data to evaluate myelinated fibers in cortex.

The structural MRI raw data and the scripts employed for the image processing and analysis have been uploaded to Dryad. All these files are provided together with an explanatory document that would allow to reproduce our results.

1. 1. Marcellino D
   2. Mediavilla T

   (2022) Dryad Digital Repository

   Skilled Reaching Structural MRI.

   https://doi.org/10.5061/dryad.crjdfn36c

1. 1. Abercrombie M

   (1946) Estimation of nuclear population from microtome sections

   The Anatomical Record 94:239–247.

   https://doi.org/10.1002/ar.1090940210

   • PubMed
   • Google Scholar
2. 1. Asan L
   2. Falfán-Melgoza C
   3. Beretta CA
   4. Sack M
   5. Zheng L
   6. Weber-Fahr W
   7. Kuner T
   8. Knabbe J

   (2021) Cellular correlates of gray matter volume changes in magnetic resonance morphometry identified by two-photon microscopy

   Scientific Reports 11:4234.

   https://doi.org/10.1038/s41598-021-83491-8

   • PubMed
   • Google Scholar
3. 1. Ashburner J
   2. Friston KJ

   (2000) Voxel-Based morphometry—the methods

   NeuroImage 11:805–821.

   https://doi.org/10.1006/nimg.2000.0582

   • PubMed
   • Google Scholar
4. 1. Bacmeister CM
   2. Barr HJ
   3. McClain CR
   4. Thornton MA
   5. Nettles D
   6. Welle CG
   7. Hughes EG

   (2020) Motor learning promotes remyelination via new and surviving oligodendrocytes

   Nature Neuroscience 23:819–831.

   https://doi.org/10.1038/s41593-020-0637-3

   • PubMed
   • Google Scholar
5. 1. Badea A
   2. Ng KL
   3. Anderson RJ
   4. Zhang J
   5. Miller MI
   6. O’Brien RJ

   (2019) Magnetic resonance imaging of mouse brain networks plasticity following motor learning

   PLOS ONE 14:e0216596.

   https://doi.org/10.1371/journal.pone.0216596

   • PubMed
   • Google Scholar
6. 1. Bagdatlioglu E
   2. Porcari P
   3. Greally E
   4. Blamire AM
   5. Straub VW

   (2020) Cognitive impairment appears progressive in the mdx mouse

   Neuromuscular Disorders 30:368–388.

   https://doi.org/10.1016/j.nmd.2020.02.018

   • PubMed
   • Google Scholar
7. 1. Barrière DA
   2. Ella A
   3. Szeremeta F
   4. Adriaensen H
   5. Même W
   6. Chaillou E
   7. Migaud M
   8. Même S
   9. Lévy F
   10. Keller M

   (2021) Brain orchestration of pregnancy and maternal behavior in mice: a longitudinal morphometric study

   NeuroImage 230:117776.

   https://doi.org/10.1016/j.neuroimage.2021.117776

   • PubMed
   • Google Scholar
8. 1. Bates D
   2. Mächler M
   3. Bolker B
   4. Walker S

   (2015) Fitting linear mixed-effects models using lme4

   Journal of Statistical Software 67:48.

   https://doi.org/10.18637/jss.v067.i01

   • Google Scholar
9. 1. Bechler ME
   2. Swire M
   3. Ffrench-Constant C

   (2018) Intrinsic and adaptive myelination-A sequential mechanism for smart wiring in the brain

   Developmental Neurobiology 78:68–79.

   https://doi.org/10.1002/dneu.22518

   • PubMed
   • Google Scholar
10. 1. Bonetto G
    2. Belin D
    3. Káradóttir RT

    (2021) Myelin: a gatekeeper of activity-dependent circuit plasticity?

    Science 374:eaba6905.

    https://doi.org/10.1126/science.aba6905

    • PubMed
    • Google Scholar
11. 1. Buetefisch CM
    2. Revill KP
    3. Shuster L
    4. Hines B
    5. Parsons M

    (2014) Motor demand-dependent activation of ipsilateral motor cortex

    Journal of Neurophysiology 112:999–1009.

    https://doi.org/10.1152/jn.00110.2014

    • PubMed
    • Google Scholar
12. 1. Chung S
    2. Wang X
    3. Lui YW

    (2017) Influence of T1-weighted signal intensity on fsl voxel-based morphometry and freesurfer cortical thickness

    American Journal of Neuroradiology 38:726–728.

    https://doi.org/10.3174/ajnr.A5053

    • PubMed
    • Google Scholar
13. 1. Delora A
    2. Gonzales A
    3. Medina CS
    4. Mitchell A
    5. Mohed AF
    6. Jacobs RE
    7. Bearer EL

    (2016) A simple rapid process for semi-automated brain extraction from magnetic resonance images of the whole mouse head

    Journal of Neuroscience Methods 257:185–193.

    https://doi.org/10.1016/j.jneumeth.2015.09.031

    • PubMed
    • Google Scholar
14. 1. Draganski B
    2. Gaser C
    3. Busch V
    4. Schuierer G
    5. Bogdahn U
    6. May A

    (2004) Neuroplasticity: changes in grey matter induced by training

    Nature 427:311–312.

    https://doi.org/10.1038/427311a

    • PubMed
    • Google Scholar
15. 1. Faust TE
    2. Gunner G
    3. Schafer DP

    (2021) Mechanisms governing activity-dependent synaptic pruning in the developing mammalian CNS

    Nature Reviews. Neuroscience 22:657–673.

    https://doi.org/10.1038/s41583-021-00507-y

    • PubMed
    • Google Scholar
16. 1. Fouard C
    2. Malandain G
    3. Prohaska S
    4. Westerhoff M

    (2006) Blockwise processing applied to brain microvascular network study

    IEEE Transactions on Medical Imaging 25:1319–1328.

    https://doi.org/10.1109/tmi.2006.880670

    • PubMed
    • Google Scholar
17. 1. Geraghty AC
    2. Gibson EM
    3. Ghanem RA
    4. Greene JJ
    5. Ocampo A
    6. Goldstein AK
    7. Ni L
    8. Yang T
    9. Marton RM
    10. Paşca SP
    11. Greenberg ME
    12. Longo FM
    13. Monje M

    (2019) Loss of adaptive myelination contributes to methotrexate chemotherapy-related cognitive impairment

    Neuron 103:250–265.

    https://doi.org/10.1016/j.neuron.2019.04.032

    • PubMed
    • Google Scholar
18. 1. Gibson EM
    2. Purger D
    3. Mount CW
    4. Goldstein AK
    5. Lin GL
    6. Wood LS
    7. Inema I
    8. Miller SE
    9. Bieri G
    10. Zuchero JB
    11. Barres BA
    12. Woo PJ
    13. Vogel H
    14. Monje M

    (2014) Neuronal activity promotes oligodendrogenesis and adaptive myelination in the mammalian brain

    Science 344:1252304.

    https://doi.org/10.1126/science.1252304

    • PubMed
    • Google Scholar
19. 1. Hamodeh S
    2. Eicke D
    3. Napper RMA
    4. Harvey RJ
    5. Sultan F

    (2010) Population based quantification of dendrites: evidence for the lack of microtubule-associate protein 2A, B in Purkinje cell spiny dendrites

    Neuroscience 170:1004–1014.

    https://doi.org/10.1016/j.neuroscience.2010.08.021

    • PubMed
    • Google Scholar
20. 1. Hamodeh S
    2. Baizer J
    3. Sugihara I
    4. Sultan F

    (2014) Systematic analysis of neuronal wiring of the rodent deep cerebellar nuclei reveals differences reflecting adaptations at the neuronal circuit and internuclear levels

    The Journal of Comparative Neurology 522:2481–2497.

    https://doi.org/10.1002/cne.23545

    • PubMed
    • Google Scholar
21. 1. Hamodeh S
    2. Bozkurt A
    3. Mao H
    4. Sultan F

    (2017) Uncovering specific changes in network wiring underlying the primate cerebrotype

    Brain Structure & Function 222:3255–3266.

    https://doi.org/10.1007/s00429-017-1402-6

    • PubMed
    • Google Scholar
22. 1. Hikishima K
    2. Komaki Y
    3. Seki F
    4. Ohnishi Y
    5. Okano HJ
    6. Okano H

    (2017) In vivo microscopic voxel-based morphometry with a brain template to characterize strain-specific structures in the mouse brain

    Scientific Reports 7:85.

    https://doi.org/10.1038/s41598-017-00148-1

    • PubMed
    • Google Scholar
23. 1. Hill RA
    2. Patel KD
    3. Goncalves CM
    4. Grutzendler J
    5. Nishiyama A

    (2014) Modulation of oligodendrocyte generation during a critical temporal window after NG2 cell division

    Nature Neuroscience 17:1518–1527.

    https://doi.org/10.1038/nn.3815

    • PubMed
    • Google Scholar
24. 1. Huang GZ
    2. Woolley CS

    (2012) Estradiol acutely suppresses inhibition in the hippocampus through a sex-specific endocannabinoid and mGLUR-dependent mechanism

    Neuron 74:801–808.

    https://doi.org/10.1016/j.neuron.2012.03.035

    • PubMed
    • Google Scholar
25. 1. Jung WB
    2. Shim HJ
    3. Kim SG

    (2019) Mouse BOLD fMRI at ultrahigh field detects somatosensory networks including thalamic nuclei

    NeuroImage 195:203–214.

    https://doi.org/10.1016/j.neuroimage.2019.03.063

    • PubMed
    • Google Scholar
26. 1. Keifer OP
    2. Hurt RC
    3. Gutman DA
    4. Keilholz SD
    5. Gourley SL
    6. Ressler KJ

    (2015) Voxel-Based morphometry predicts shifts in dendritic spine density and morphology with auditory fear conditioning

    Nature Communications 6:7582.

    https://doi.org/10.1038/ncomms8582

    • PubMed
    • Google Scholar
27. 1. Keiner S
    2. Niv F
    3. Neumann S
    4. Steinbach T
    5. Schmeer C
    6. Hornung K
    7. Schlenker Y
    8. Förster M
    9. Witte OW
    10. Redecker C

    (2017) Effect of skilled reaching training and enriched environment on generation of oligodendrocytes in the adult sensorimotor cortex and corpus callosum

    BMC Neuroscience 18:31.

    https://doi.org/10.1186/s12868-017-0347-2

    • PubMed
    • Google Scholar
28. 1. Kilgard MP

    (2012) Harnessing plasticity to understand learning and treat disease

    Trends in Neurosciences 35:715–722.

    https://doi.org/10.1016/j.tins.2012.09.002

    • PubMed
    • Google Scholar
29. 1. Kim JS
    2. Lee HJ
    3. Lee S
    4. Lee HS
    5. Jeong YJ
    6. Son Y
    7. Kim JM
    8. Lee YJ
    9. Park MH

    (2020) Conductive hearing loss aggravates memory decline in alzheimer model mice

    Frontiers in Neuroscience 14:843.

    https://doi.org/10.3389/fnins.2020.00843

    • PubMed
    • Google Scholar
30. 1. Kleim JA
    2. Barbay S
    3. Nudo RJ

    (1998) Functional reorganization of the rat motor cortex following motor skill learning

    Journal of Neurophysiology 80:3321–3325.

    https://doi.org/10.1152/jn.1998.80.6.3321

    • PubMed
    • Google Scholar
31. 1. Kleim JA
    2. Hogg TM
    3. VandenBerg PM
    4. Cooper NR
    5. Bruneau R
    6. Remple M

    (2004) Cortical synaptogenesis and motor MAP reorganization occur during late, but not early, phase of motor skill learning

    The Journal of Neuroscience 24:628–633.

    https://doi.org/10.1523/JNEUROSCI.3440-03.2004

    • PubMed
    • Google Scholar
32. 1. Kleim JA
    2. Markham JA
    3. Vij K
    4. Freese JL
    5. Ballard DH
    6. Greenough WT

    (2007) Motor learning induces astrocytic hypertrophy in the cerebellar cortex

    Behavioural Brain Research 178:244–249.

    https://doi.org/10.1016/j.bbr.2006.12.022

    • PubMed
    • Google Scholar
33. 1. Kluver H
    2. Barrera E

    (1953) A method for the combined staining of cells and fibers in the nervous system

    Journal of Neuropathology and Experimental Neurology 12:400–403.

    https://doi.org/10.1097/00005072-195312040-00008

    • PubMed
    • Google Scholar
34. 1. Kougioumtzidou E
    2. Shimizu T
    3. Hamilton NB
    4. Tohyama K
    5. Sprengel R
    6. Monyer H
    7. Attwell D
    8. Richardson WD

    (2017) Signalling through AMPA receptors on oligodendrocyte precursors promotes myelination by enhancing oligodendrocyte survival

    eLife 6:e28080.

    https://doi.org/10.7554/eLife.28080

    • PubMed
    • Google Scholar
35. 1. Liao PS
    2. Chew TS
    3. Chung PC

    (2001) A fast algorithm for multilevel thresholding

    Journal of Information Science and Engineering 17:713–727.

    https://doi.org/10.6688/JISE.2001.17.5.1

    • Google Scholar
36. 1. Lindenberger U
    2. Lövdén M

    (2019) Brain plasticity in human lifespan development: the exploration–selection–refinement model

    Annual Review of Developmental Psychology 1:197–222.

    https://doi.org/10.1146/annurev-devpsych-121318-085229

    • Google Scholar
37. 1. Makino H
    2. Hwang EJ
    3. Hedrick NG
    4. Komiyama T

    (2016) Circuit mechanisms of sensorimotor learning

    Neuron 92:705–721.

    https://doi.org/10.1016/j.neuron.2016.10.029

    • PubMed
    • Google Scholar
38. 1. Makinodan M
    2. Rosen KM
    3. Ito S
    4. Corfas G

    (2012) A critical period for social experience-dependent oligodendrocyte maturation and myelination

    Science 337:1357–1360.

    https://doi.org/10.1126/science.1220845

    • PubMed
    • Google Scholar
39. 1. McKenzie IA
    2. Ohayon D
    3. Li H
    4. de Faria JP
    5. Emery B
    6. Tohyama K
    7. Richardson WD

    (2014) Motor skill learning requires active central myelination

    Science 346:318–322.

    https://doi.org/10.1126/science.1254960

    • PubMed
    • Google Scholar
40. 1. Mensch S
    2. Baraban M
    3. Almeida R
    4. Czopka T
    5. Ausborn J
    6. El Manira A
    7. Lyons DA

    (2015) Synaptic vesicle release regulates myelin sheath number of individual oligodendrocytes in vivo

    Nature Neuroscience 18:628–630.

    https://doi.org/10.1038/nn.3991

    • PubMed
    • Google Scholar
41. 1. Milner B
    2. Squire LR
    3. Kandel ER

    (1998) Cognitive neuroscience and the study of memory

    Neuron 20:445–468.

    https://doi.org/10.1016/s0896-6273(00)80987-3

    • PubMed
    • Google Scholar
42. 1. Mogil JS
    2. Wilson SG
    3. Chesler EJ
    4. Rankin AL
    5. Nemmani KVS
    6. Lariviere WR
    7. Groce MK
    8. Wallace MR
    9. Kaplan L
    10. Staud R
    11. Ness TJ
    12. Glover TL
    13. Stankova M
    14. Mayorov A
    15. Hruby VJ
    16. Grisel JE
    17. Fillingim RB

    (2003) The melanocortin-1 receptor gene mediates female-specific mechanisms of analgesia in mice and humans

    PNAS 100:4867–4872.

    https://doi.org/10.1073/pnas.0730053100

    • PubMed
    • Google Scholar
43. 1. Molina-Luna K
    2. Pekanovic A
    3. Röhrich S
    4. Hertler B
    5. Schubring-Giese M
    6. Rioult-Pedotti M-S
    7. Luft AR
    8. Lauwereyns J

    (2009) Dopamine in motor cortex is necessary for skill learning and synaptic plasticity

    PLOS ONE 4:e7082.

    https://doi.org/10.1371/journal.pone.0007082

    • PubMed
    • Google Scholar
44. 1. Natu VS
    2. Gomez J
    3. Barnett M
    4. Jeska B
    5. Kirilina E
    6. Jaeger C
    7. Zhen Z
    8. Cox S
    9. Weiner KS
    10. Weiskopf N
    11. Grill-Spector K

    (2019) Apparent thinning of human visual cortex during childhood is associated with myelination

    PNAS 116:20750–20759.

    https://doi.org/10.1073/pnas.1904931116

    • PubMed
    • Google Scholar
45. 1. Noori R
    2. Park D
    3. Griffiths JD
    4. Bells S
    5. Frankland PW
    6. Mabbott D
    7. Lefebvre J

    (2020) Activity-dependent myelination: a glial mechanism of oscillatory self-organization in large-scale brain networks

    PNAS 117:13227–13237.

    https://doi.org/10.1073/pnas.1916646117

    • PubMed
    • Google Scholar
46. 1. Otsu N

    (1979) A threshold selection method from gray-level histograms

    IEEE Transactions on Systems, Man, and Cybernetics 9:62–66.

    https://doi.org/10.1109/TSMC.1979.4310076

    • Google Scholar
47. 1. Pan S
    2. Mayoral SR
    3. Choi HS
    4. Chan JR
    5. Kheirbek MA

    (2020) Preservation of a remote fear memory requires new myelin formation

    Nature Neuroscience 23:487–499.

    https://doi.org/10.1038/s41593-019-0582-1

    • PubMed
    • Google Scholar
48. Software

    1. R Development Core Team

    (2017) R: A language and environment for statistical computing

    R Foundation for Statistical Computing, Vienna, Austria.

    https://www.R-project.org/
49. 1. Saab BJ
    2. MacLean AJB
    3. Kanisek M
    4. Zurek AA
    5. Martin LJ
    6. Roder JC
    7. Orser BA

    (2010) Short-Term memory impairment after isoflurane in mice is prevented by the α5 γ-aminobutyric acid type A receptor inverse agonist L-655,708

    Anesthesiology 113:1061–1071.

    https://doi.org/10.1097/ALN.0b013e3181f56228

    • PubMed
    • Google Scholar
50. 1. Sampaio-Baptista C
    2. Khrapitchev AA
    3. Foxley S
    4. Schlagheck T
    5. Scholz J
    6. Jbabdi S
    7. DeLuca GC
    8. Miller KL
    9. Taylor A
    10. Thomas N
    11. Kleim J
    12. Sibson NR
    13. Bannerman D
    14. Johansen-Berg H

    (2013) Motor skill learning induces changes in white matter microstructure and myelination

    The Journal of Neuroscience 33:19499–19503.

    https://doi.org/10.1523/JNEUROSCI.3048-13.2013

    • PubMed
    • Google Scholar
51. 1. Sampaio-Baptista C
    2. Vallès A
    3. Khrapitchev AA
    4. Akkermans G
    5. Winkler AM
    6. Foxley S
    7. Sibson NR
    8. Roberts M
    9. Miller K
    10. Diamond ME
    11. Martens GJM
    12. De Weerd P
    13. Johansen-Berg H

    (2020) White matter structure and myelin-related gene expression alterations with experience in adult rats

    Progress in Neurobiology 187:101770.

    https://doi.org/10.1016/j.pneurobio.2020.101770

    • PubMed
    • Google Scholar
52. 1. Sawiak SJ
    2. Wood NI
    3. Williams GB
    4. Morton AJ
    5. Carpenter TA

    (2009) Voxel-Based morphometry in the R6/2 transgenic mouse reveals differences between genotypes not seen with manual 2D morphometry

    Neurobiology of Disease 33:20–27.

    https://doi.org/10.1016/j.nbd.2008.09.016

    • PubMed
    • Google Scholar
53. 1. Sawiak SJ
    2. Wood NI
    3. Williams GB
    4. Morton AJ
    5. Carpenter TA

    (2013) Voxel-Based morphometry with templates and validation in a mouse model of Huntington’s disease

    Magnetic Resonance Imaging 31:1522–1531.

    https://doi.org/10.1016/j.mri.2013.06.001

    • PubMed
    • Google Scholar
54. 1. Schindelin J
    2. Arganda-Carreras I
    3. Frise E
    4. Kaynig V
    5. Longair M
    6. Pietzsch T
    7. Preibisch S
    8. Rueden C
    9. Saalfeld S
    10. Schmid B
    11. Tinevez J-Y
    12. White DJ
    13. Hartenstein V
    14. Eliceiri K
    15. Tomancak P
    16. Cardona A

    (2012) Fiji: an open-source platform for biological-image analysis

    Nature Methods 9:676–682.

    https://doi.org/10.1038/nmeth.2019

    • PubMed
    • Google Scholar
55. 1. Schmidt S
    2. Gull S
    3. Herrmann KH
    4. Boehme M
    5. Irintchev A
    6. Urbach A
    7. Reichenbach JR
    8. Klingner CM
    9. Gaser C
    10. Witte OW

    (2021) Experience-dependent structural plasticity in the adult brain: how the learning brain grows

    NeuroImage 225:117502.

    https://doi.org/10.1016/j.neuroimage.2020.117502

    • PubMed
    • Google Scholar
56. 1. Steadman PE
    2. Xia F
    3. Ahmed M
    4. Mocle AJ
    5. Penning ARA
    6. Geraghty AC
    7. Steenland HW
    8. Monje M
    9. Josselyn SA
    10. Frankland PW

    (2020) Disruption of oligodendrogenesis impairs memory consolidation in adult mice

    Neuron 105:150–164.

    https://doi.org/10.1016/j.neuron.2019.10.013

    • PubMed
    • Google Scholar
57. 1. Swire M
    2. Ffrench-Constant C

    (2018) Seeing is believing: myelin dynamics in the adult CNS

    Neuron 98:684–686.

    https://doi.org/10.1016/j.neuron.2018.05.005

    • PubMed
    • Google Scholar
58. 1. Tennant KA
    2. Adkins DL
    3. Donlan NA
    4. Asay AL
    5. Thomas N
    6. Kleim JA
    7. Jones TA

    (2011) The organization of the forelimb representation of the C57BL/6 mouse motor cortex as defined by intracortical microstimulation and cytoarchitecture

    Cerebral Cortex 21:865–876.

    https://doi.org/10.1093/cercor/bhq159

    • PubMed
    • Google Scholar
59. 1. Tustison NJ
    2. Avants BB
    3. Cook PA
    4. Zheng Y
    5. Egan A
    6. Yushkevich PA
    7. Gee JC

    (2010) N4ITK: improved N3 bias correction

    IEEE Transactions on Medical Imaging 29:1310–1320.

    https://doi.org/10.1109/TMI.2010.2046908

    • PubMed
    • Google Scholar
60. 1. Wake H
    2. Lee PR
    3. Fields RD

    (2011) Control of local protein synthesis and initial events in myelination by action potentials

    Science 333:1647–1651.

    https://doi.org/10.1126/science.1206998

    • PubMed
    • Google Scholar
61. 1. Weiskopf N
    2. Suckling J
    3. Williams G
    4. Correia MM
    5. Inkster B
    6. Tait R
    7. Ooi C
    8. Bullmore ET
    9. Lutti A

    (2013) Quantitative multi-parameter mapping of R1, PD (*), MT, and R2 (*) at 3T: a multi-center validation

    Frontiers in Neuroscience 7:95.

    https://doi.org/10.3389/fnins.2013.00095

    • PubMed
    • Google Scholar
62. 1. Wenger E
    2. Brozzoli C
    3. Lindenberger U
    4. Lövdén M

    (2017a) Expansion and renormalization of human brain structure during skill acquisition

    Trends in Cognitive Sciences 21:930–939.

    https://doi.org/10.1016/j.tics.2017.09.008

    • PubMed
    • Google Scholar
63. 1. Wenger E
    2. Kühn S
    3. Verrel J
    4. Mårtensson J
    5. Bodammer NC
    6. Lindenberger U
    7. Lövdén M

    (2017b) Repeated structural imaging reveals nonlinear progression of experience-dependent volume changes in human motor cortex

    Cerebral Cortex 27:2911–2925.

    https://doi.org/10.1093/cercor/bhw141

    • PubMed
    • Google Scholar
64. 1. Whishaw IQ
    2. O’Connor WT
    3. Dunnett SB

    (1986) The contributions of motor cortex, nigrostriatal dopamine and caudate-putamen to skilled forelimb use in the rat

    Brain 109 (Pt 5):805–843.

    https://doi.org/10.1093/brain/109.5.805

    • PubMed
    • Google Scholar
65. 1. Wischnewski M
    2. Kowalski GM
    3. Rink F
    4. Belagaje SR
    5. Haut MW
    6. Hobbs G
    7. Buetefisch CM

    (2016) Demand on skillfulness modulates interhemispheric inhibition of motor cortices

    Journal of Neurophysiology 115:2803–2813.

    https://doi.org/10.1152/jn.01076.2015

    • PubMed
    • Google Scholar
66. 1. Woo J
    2. Min JO
    3. Kang DS
    4. Kim YS
    5. Jung GH
    6. Park HJ
    7. Kim S
    8. An H
    9. Kwon J
    10. Kim J
    11. Shim I
    12. Kim HG
    13. Lee CJ
    14. Yoon BE

    (2018) Control of motor coordination by astrocytic tonic GABA release through modulation of excitation/inhibition balance in cerebellum

    PNAS 115:5004–5009.

    https://doi.org/10.1073/pnas.1721187115

    • PubMed
    • Google Scholar
67. 1. Xiao L
    2. Ohayon D
    3. McKenzie IA
    4. Sinclair-Wilson A
    5. Wright JL
    6. Fudge AD
    7. Emery B
    8. Li H
    9. Richardson WD

    (2016) Rapid production of new oligodendrocytes is required in the earliest stages of motor-skill learning

    Nature Neuroscience 19:1210–1217.

    https://doi.org/10.1038/nn.4351

    • PubMed
    • Google Scholar
68. 1. Xu T
    2. Yu X
    3. Perlik AJ
    4. Tobin WF
    5. Zweig JA
    6. Tennant K
    7. Jones T
    8. Zuo Y

    (2009) Rapid formation and selective stabilization of synapses for enduring motor memories

    Nature 462:915–919.

    https://doi.org/10.1038/nature08389

    • PubMed
    • Google Scholar
69. 1. Yang SM
    2. Michel K
    3. Jokhi V
    4. Nedivi E
    5. Arlotta P

    (2020) Neuron class-specific responses govern adaptive myelin remodeling in the neocortex

    Science 370:eabd2109.

    https://doi.org/10.1126/science.abd2109

    • PubMed
    • Google Scholar
70. 1. Zatorre RJ
    2. Fields RD
    3. Johansen-Berg H

    (2012) Plasticity in gray and white: neuroimaging changes in brain structure during learning

    Nature Neuroscience 15:528–536.

    https://doi.org/10.1038/nn.3045

    • PubMed
    • Google Scholar

Author details

1. Tomas Mediavilla

   Department of Integrative Medical Biology, Umeå University, Umeå, Sweden

   Contribution

   Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

2. Özgün Özalay

   Department of Integrative Medical Biology, Umeå University, Umeå, Sweden

   Contribution

   Data curation, Software, Formal analysis, Writing – review and editing

   Competing interests

   No competing interests declared

3. Héctor M Estévez-Silva

   Department of Integrative Medical Biology, Umeå University, Umeå, Sweden

   Contribution

   Formal analysis, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

4. Bárbara Frias

   Department of Integrative Medical Biology, Umeå University, Umeå, Sweden

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

5. Greger Orädd

   Department of Integrative Medical Biology, Umeå University, Umeå, Sweden

   Contribution

   Formal analysis, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

6. Fahad R Sultan

   Department of Integrative Medical Biology, Umeå University, Umeå, Sweden

   Contribution

   Resources, Methodology

   Competing interests

   No competing interests declared

7. Claudio Brozzoli

   1. IMPACT, Centre de Recherche en Neurosciences de Lyon, Lyon, France
   2. Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Solna, Sweden

   Contribution

   Conceptualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

8. Benjamín Garzón

   1. Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Solna, Sweden
   2. Department of Psychology, University of Gothenburg, Gothenburg, Sweden

   Contribution

   Conceptualization, Resources, Software, Formal analysis, Funding acquisition, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

9. Martin Lövdén

   1. Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Solna, Sweden
   2. Department of Psychology, University of Gothenburg, Gothenburg, Sweden

   Contribution

   Conceptualization, Supervision, Funding acquisition, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

10. Daniel J Marcellino

    Department of Integrative Medical Biology, Umeå University, Umeå, Sweden

    Contribution

    Conceptualization, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

    For correspondence

    1. daniel.marcellino@umu.se
    2. daniel.marcellino@me.com

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-4618-7267

• 1,137

  views

• 185

  downloads

• 4

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.