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Motor learning drives dynamic patterns of intermittent myelination on learning-activated axons

Abstract

Myelin plasticity occurs when newly formed and pre-existing oligodendrocytes remodel existing patterns of myelination. Myelin remodeling occurs in response to changes in neuronal activity and is required for learning and memory. However, the link between behavior-induced neuronal activity and circuit-specific changes in myelination remains unclear. Using longitudinal in vivo two-photon imaging and targeted labeling of learning-activated neurons in mice, we explore how the pattern of intermittent myelination is altered on individual cortical axons during learning of a dexterous reach task. We show that behavior-induced myelin plasticity is targeted to learning-activated axons and occurs in a staged response across cortical layers in the mouse primary motor cortex. During learning, myelin sheaths retract, which results in lengthening of nodes of Ranvier. Following motor learning, addition of newly formed myelin sheaths increases the number of continuous stretches of myelination. Computational modeling suggests that motor learning-induced myelin plasticity initially slows and subsequently increases axonal conduction speed. Finally, we show that both the magnitude and timing of nodal and myelin dynamics correlate with improvement of behavioral performance during motor learning. Thus, learning-induced and circuit-specific myelination changes may contribute to information encoding in neural circuits during motor learning.

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Fig. 1: Learning-induced sheath retraction modifies node length.
Fig. 2: Myelin and nodal plasticity occur in distinct phases in response to learning.
Fig. 3: Learning-induced changes to myelin occur at specific locations along axons.
Fig. 4: Learning induces myelin and nodal dynamics broadly across cortical layers.
Fig. 5: Modeled effect of learning-induced sheath and nodal dynamics on conduction.
Fig. 6: Learning-activated axons exhibit heightened myelin dynamics.
Fig. 7: The extent and timing of myelin dynamics is correlated with behavioral performance.

Data availability

All data that support the findings, tools and reagents will be shared on an unrestricted basis; requests should be directed to the corresponding author.

Code availability

Code for analysis associated with the manuscript is available at https://github.com/EthanHughesLab/Bacmeisteretal2022.

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Acknowledgements

We thank M. Hall for machining expertise, H. Barr for the conversation that reinitiated interest in labeling activated axons, and past and current members of the Hughes and Welle labs and the CU Anschutz Myelin Group for discussions. M.A.T. is supported by the National Institutes of Health Institutional Neuroscience Graduate Training Grant (5T32NS099042-17). Funding was provided by the Boettcher Foundation Webb-Waring Biomedical Research Award, the Whitehall Foundation and the National Multiple Sclerosis Society (RG-1701–26733) and NINDS (NS115975, NS125230) to E.G.H.

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Authors

Contributions

E.G.H. and C.M.B. conceived the project. C.M.B. designed and performed experiments, analyzed data and generated all figures. R.H. performed all viral injections and performed surgeries. L.A.O. performed experiments and analyzed data for Fig. 7 and Extended Data Figs. 2 and 3. M.A.T. performed surgeries. L.C. performed experiments and additional imaging analyses for Figs. 4, 6 and 7 and Extended Data Figs. 2 and 3. A.R.C. provided technical support for all mouse lines. A.P.-P. supervised the computational modulating and E.G.H. supervised the project. C.M.B. and E.G.H. wrote the manuscript with input from other authors.

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Correspondence to Ethan G. Hughes.

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Nature Neuroscience thanks Michelle Monje and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Near-infrared branding identifies the same oligodendrocytes and myelin sheaths in longitudinally, in vivo imaged areas and posthoc stained tissue.

a, The same field of view imaged in vivo (‘Live Tissue’, left) and in fixed, sectioned, and stained tissue (‘Fixed Tissue’, right). Patterns of cell bodies (examples outlined in white dotted lines) were maintained across live and processed tissue. Note the new oligodendrocyte generated at 60d and delineated with a white arrowhead. b, A newly generated oligodendrocyte in vivo (‘Live Tissue’, left) and fixed, sectioned tissue (‘Fixed Tissue’, right) stained for oligodendrocytes and myelin (GFP, green) and axons (NFH, magenta). Note the same T-junction across live and fixed samples is marked with the white arrowhead.

Extended Data Fig. 2 Learning trajectory of mice engaging in forelimb reach training.

a, Illustration of forelimb reach training box environment. Mice learn to reach by extending their left hand through a slit in a plexiglass box to grab a pellet and return it to their mouth. b, Imaging and training timelines for untrained (top) and learning (bottom) mice. c, The majority of mice learn to perform the forelimb reach task (learners, green). Learners improve their success rate gradually over the course of seven days of training. In contrast, non-learners maintain a low success rate across time and do not attain higher than a 15% success rate at the end of the training regimen (purple). d, Successful learners of the task perform significantly better on the last day of training (Paired Student’s t-test, t(15)=11.72, p < 0.0001) and achieve higher than 15% success rate, in contrast to non-learners which do not improve significantly and do not attain above 15% success on the last day of training. Mice were excluded from data analysis if they did not succeed in at least 10% of reaches across the seven days of training (n = 2 mice). e, f, Breakdown of all mice included in each of the figures, where each dot is a unique color that represents a single mouse and is consistent across graphs. Neither success rate (e) nor change in success rate (f) differ across figures (REML with post hoc Tukey’s HSD). Bars and error bars represent mean ± s.e.m unless otherwise noted. For detailed statistics, see Supplementary Table 3.

Extended Data Fig. 3 Identifying and tracing nodes and axons across live and fixed tissue.

a, The same node in live imaged (‘Live Tissue’, left) and fixed, sectioned tissue (‘Fixed Tissue’, right) stained for oligodendrocytes and myelin (GFP, green), sodium channels (NaV, magenta), and axons (NFH, yellow). White arrow points to node across time and in fixed tissue. b,c, 90.90 ± 3.73% of nodes visualized in vivo possess NaV staining characteristic of nodes of Ranvier (b, c), comparable to values of nodes identified fixed tissue of age-matched controls (c; random sample, nmice = 6; longitudinally imaged, nmice = 3). d, No difference in the proportion of gaps in myelination (larger than 3 microns) with and without appreciable sodium channel distributions in fixed tissue of age-matched controls (nmice = 8; Paired Student’s t-test). e, Representative in vivo imaging and post hoc immunostaining of a lengthening node with sodium channels (left) and without appreciable sodium channels (right). f, The same node confirmed with NaV staining in (a) is identified in live imaging by a marked decrease in autofluorescence between two GFP-labeled myelin paranodes. g, Axon morphology is accurately identified in vivo as confirmed by post hoc immunostaining of the same tissue. A lengthening node in vivo (‘Live Tissue’, left) and fixed, sectioned tissue (‘Fixed Tissue’, middle). Axon trajectory is identified using the shape of the myelin sheath prior to node lengthening (‘Live axon morphology’, magenta) and confirmed using post hoc immunostaining of the same sheaths for axons (NFH, magenta) and myelin (GFP, green) and reconstructions of the axons (‘Fixed axon morphology’, magenta). In vivo node morphology (‘In vivo nodal morphology’, top right) is reconstructed and confirmed using post hoc immunostaining and morphological reconstruction (‘Fixed nodal morphology’, bottom right). h, Sheath length and change in sheath length is determined by aligning fields of view using fiduciary marks (for example cell soma) which extend across the entire duration of the study, including pre-existing oligodendrocytes, which maintain their position throughout the course of imaging (left). Change in sheath length is mirrored by a change in paranode position (right). To determine the change in sheath length, the distance between the initial and final paranodal position is traced using Simple Neurite Tracer and overlayed stacks. Bars and error bars represent mean ± s.e.m unless otherwise noted. For detailed statistics, see Supplementary Table 3.

Extended Data Fig. 4 Sheath retraction, but not sheath growth, is affected by learning a new skill.

a, Sheath length is similar across sheaths with 2, 1, or 0 neighbors. b, Sheaths of many ages initiate sheath dynamics in young adult mice. c, Proportion of dynamic sheaths engaging in growth three weeks before learning, during learning (one week), and in the two weeks after learning. d, Proportion of dynamic sheaths engaging in retraction before, during and after learning. Learning modulates sheath retraction (F2,14 = 6.76, p = 0.0088). During learning, more sheaths retract relative to untrained mice (p = 0.0095; Tukey’s HSD) and relative to prelearning values in trained mice (p = 0.016; Tukey’s HSD). Two weeks after learning, more sheaths retract relative to prelearning values in trained mice (p = 0.0358; Tukey’s HSD). e, In learning mice, there are fewer stable sheaths with 2 (p = 0.013, t(5.91)=3.49), 1 (p = 0.0089, t(2.47)=7.62), and 0 neighbors (p = 0.043, t(2.04)=4.55). f, In learning mice, there are more growing sheaths with 1 neighbor (p = 0.0071, t(6.00)=−4.00). g, In learning mice, there are more retracting sheaths with 2 (p = 0.024, t(4.00)=−3.62) and 1 neighbors (p = 0.0050, t(6.00)=−4.31). h, Nodes and gaps in myelin that shorten are not modulated by learning (REML with post hoc Tukey’s HSD). Bars and error bars represent mean ± s.e.m unless otherwise noted. For detailed statistics, see Supplementary Table 3.

Extended Data Fig. 5 Tracking virally labeled axons across time in vivo.

a, Maximum projection of a cFos+ neuron (AAv8-cFos-ER, red) and surrounding oligodendrocytes and myelin (in vivo, green) (top). Single slice at 159 microns below the pial surface, with white arrow identifying unmyelinated region of a cFos+ axon (middle). Single slice at 185 microns below the pial surface, with pink arrow identifying a myelinated region of a cFos+ axon (bottom). b, 3D projection of the cFos+ neuron and associated myelin sheaths from (a), with dashed lines corresponding to the single slices in (a). c, The same neuron as in (a), reconstructed using Simple Neurite Tracer. d, 3D projection of a longitudinally imaged axon with node lengthening and sheath retraction at the outset of the imaging experiment (0d) and at the final imaging timepoint (88d).

Extended Data Fig. 6 Biological correlates of computational modeling data.

a, Average pre-existing myelin sheath length per mouse (nmice = 4). b, Mean node length of lengthening nodes per mouse, measured across time in prelearning, learning, and postlearning stages (nmice = 3). c, Distribution of the number of consecutive lengthening nodes along a single axon. d-g, Modeled change in conduction speed as a function of the number of consecutively remodeled nodes at different sodium:leak conductance ratios (node with = 35 microns, pink exponential fit; Chi-square goodness of fit test). Conductance ratios were generated using either 0.4 S/cm2 (low) or 3.4 S/cm2 (high) for sodium conductance (gNa) and either 0.01 S/cm2 (low) or 0.08 S/cm2 (high) for leak conductance (gL). h, Length of unmyelinated axon segment that received new sheath addition following learning, measured from one sheath to the nearest neighboring sheath (nmice = 6; box plot, white bar = median, bounds of rectangle are Q1 and Q3; whiskers reflect minima and maxima). i, Proportion of gaps filled by 1, 2, or 3 sheaths following learning. j, Modeled change in conduction speed as a function of proportion of a 184 micron gap filled in by new myelin (pink exponential fit; Chi-square goodness of fit test). Conductance ratios as in d-g. Bars and error bars represent mean ± s.e.m unless otherwise noted.

Extended Data Fig. 7 Characterizing AAv8-cFos-ER virus.

a, Maximum projection of motor cortex injected with the cFos virus 1 day before learning (−1d), one week after learning (7d), three weeks after learning (22d), and 5 weeks after learning (42d). 4-OHT was injected 3 hours after the final day of learning (7d). b, Fit curve for modeled change in cFos+ cell gain (calculated as % of tamoxifen-independent cFos+ neurons, that is the number of neurons labeled at 60 days) in mice that learn and receive tamoxifen (pink), untrained mice (gray), and mice that learn and are injected with sunflower oil (red). Each dot represents proportion of labeled neurons per mouse at a given timepoint. Shading represents 95% confidence interval. c, Rate of cFos+ cell gain in mice that learn and receive tamoxifen (pink), untrained mice (grey), and mice that learn and are injected with sunflower oil (red). Mice that learn have a heightened rate of cFos+ cell gain in the two weeks following learning, while the percentage of cFos+ neurons did not change in untrained mice and oil injected mice over the course of the experiment. Lines and shading represent mean ± s.e.m. Light pink lines in background represent individual learning mice. d, Significantly more cFos+ cells appear in L2/3 relative to L1 following learning and injection of tamoxifen. e, The majority of learning-activated cells are putative L2/3 pyramidal neurons (determined by morphology). f, Distribution of traced axon lengths for cFos+ neurons (nmice = 5; box plot, white bar = median, bounds of rectangle are Q1 and Q3; whiskers reflect minima and maxima). Bars and error bars represent mean ± s.e.m unless otherwise noted. For detailed statistics, see Supplementary Table 3.

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Bacmeister, C.M., Huang, R., Osso, L.A. et al. Motor learning drives dynamic patterns of intermittent myelination on learning-activated axons. Nat Neurosci 25, 1300–1313 (2022). https://doi.org/10.1038/s41593-022-01169-4

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