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Motor learning requires myelination to reduce asynchrony and spontaneity in neural activity

Abstract Myelination increases the conduction velocity in long‐range axons and is prerequisite for many brain functions. Impaired myelin regulation or impairment of myelin itself is frequently associated with deficits in learning and cognition in neurological and psychiatric disorders. However, it has not been revealed what perturbation of neural activity induced by myelin impairment causes learning deficits. Here, we measured neural activity in the motor cortex during motor learning in transgenic mice with a subtle impairment of their myelin. This deficit in myelin impaired motor learning, and was accompanied by a decrease in the amplitude of movement‐related activity and an increase in the frequency of spontaneous activity. Thalamocortical axons showed variability in axonal conduction with a large spread in the timing of postsynaptic cortical responses. Repetitive pairing of forelimb movements with optogenetic stimulation of thalamocortical axon terminals restored motor learning. Thus, myelin regulation helps to maintain the synchrony of cortical spike‐time arrivals through long‐range axons, facilitating the propagation of the information required for learning. Our results revealed the pathological neuronal circuit activity with impaired myelin and suggest the possibility that pairing of noninvasive brain stimulation with relevant behaviors may ameliorate cognitive and behavioral abnormalities in diseases with impaired myelination.

information required for learning. Our results revealed the pathological neuronal circuit activity with impaired myelin and suggest the possibility that pairing of noninvasive brain stimulation with relevant behaviors may ameliorate cognitive and behavioral abnormalities in diseases with impaired myelination.

K E Y W O R D S
axonal conduction, calcium imaging, motor learning, myelination, neuron-glia interactions, oligodendrocyte, optogenetics

| INTRODUCTION
Myelin formation by oligodendrocytes surrounding axons in the central nervous system regulates conduction velocity (CV), which is a key component of fast and efficient information processing in the brain (Emery, 2010;Fields, 2008;Nave, 2010). Myelinated long-range axons form white matter and transmit information between many cortical and subcortical gray matter regions. Human studies using magnetic resonance imaging (MRI) have shown that white matter plasticity is associated with motor learning (Scholz, Klein, Behrens, & Johansen-Berg, 2009;Zatorre, Fields, & Johansen-Berg, 2012). In the mouse, motor learning or motor behavioral improvements require proliferation and differentiation of oligodendrocyte precursor cells (OPCs) in the white matter and motor cortex (Gibson et al., 2014;McKenzie et al., 2014;Xiao et al., 2016). A pharmacological block of action potentials in the retina inhibits proliferation of OPCs in optic nerves (Barres & Raff, 1993). Electrical stimulation of medullary pyramids (Li, Brus-Ramer, Martin, & McDonald, 2010) and optogenetic stimulation of cortical layer 5 (L5) excitatory neurons (Gibson et al., 2014) promotes the differentiation of OPCs. Inhibition of vesicular release from neurons to mature oligodendrocytes impairs myelination, both in vitro (Wake et al., 2015;Wake, Lee, & Fields, 2011) and in vivo (Hines, Ravanelli, Schwindt, Scott, & Appel, 2015;Mensch et al., 2015). These results indicate that neuronal activity regulates myelination through OPCs and mature oligodendrocytes and thus impairment of activity-dependent myelination or myelin itself may contribute on learning deficits. Given that distinct myelinated longrange axons input to a common cell or circuit, appropriate regulation of the CVs is assumed to help synchronization of these distinct neural inputs (Pajevic, Basser, & Fields, 2014;Roelfsema, Engel, Konig, & Singer, 1997). An assembly of time-locked inputs would consolidate information processing by inducing spike-timing-dependent plasticity (STDP) between optimally myelinated axons and their postsynaptic neurons, or in the local cortical circuit (Feldman, 2012;Nave, 2010;Veniero, Ponzo, & Koch, 2013). This is believed to promote the formation of the cortical ensemble required for behavioral learning. Impaired regulation of myelination or impairment of myelin itself frequently accompanies learning deficits, cognitive dysfunction, and aging (Bennett & Madden, 2014;Liu et al., 2012;Makinodan, Rosen, Ito, & Corfas, 2012;McKenzie et al., 2014 ;Xiao et al., 2016). In MRI using diffusion tensor imaging, white matter changes are frequently observed in dementia and considered as one of the earliest signs of cognitive decline (Amlien & Fjell, 2014;Back et al., 2011). Genomewide analysis reveals differential expression of myelination-related genes in schizophrenia (Hakak et al., 2001). Genetic deletion of myelin regulatory factor causes deficient maturation of OPCs in mature mice, resulting in impaired motor learning (McKenzie et al., 2014;Xiao et al., 2016). Social isolation impairs adult myelination in the mouse prefrontal cortex and causes behavioral deficits (Liu et al., 2012;Makinodan et al., 2012). Although it has been reported that impaired myelin regulation increases the conduction time of myelinated axons in vivo (Bando et al., 2008;Tanaka, Ikenaka, & Isa, 2006), it is poorly understood how this impairs cortical circuit properties that are relevant to learning and cognition. At a single neuron level, cortical activity is high in mice models of Alzheimer's disease (Busche et al., 2008), fragile X syndrome (Goncalves, Anstey, Golshani, & Portera-Cailliau, 2013), and Rett syndrome (Zhang, Peterson, Beyer, Frankel, & Zhang, 2014), and in these mice models, the abnormalities in cortical neuronal activity are associated with defects in inhibition. It is unknown whether a mouse model of impaired myelination shows similar properties.
Here, we hypothesize that impaired myelin disrupts cortical neuronal activity and results in impaired motor learning. As disrupting myelination can have widespread and severe consequences, including inflammation, as seen in the debilitating demyelination disorders and leukodystrophies (Saugier-Veber et al., 1994), an animal model with slight deficits in CV and learning, but without apparent histological or motor deficits, is suitable for testing this hypothesis.
Transgenic mice with extra copies of the myelin proteolipid protein 1 gene (PLP-tg mice [Kagawa et al., 1994]) have reduced CV in spinal ascending and descending tracts at 2 months of age, including the pyramidal tract (Tanaka et al., 2006) due to thinner myelin thickness (Tanaka et al., 2009) and abnormal processes of oligodendrocytes (Shimizu et al., 2013). PLP-tg mice also exhibit some impairment of learning and behavior at the same age, although neither histological demyelination nor motor deficits are apparent (Tanaka et al., 2006;Tanaka et al., 2009). In this study, we used PLP-tg mice at 2 months of age and a forelimb movement learning task to test our hypothesis. We focused on the primary motor cortex (M1), as the proliferation and differentiation of M1 OPCs is accelerated, and the activity of M1 cortical neurons dynamically changes during motor learning (Huber et al., 2012;Masamizu et al., 2014;McKenzie et al., 2014;Peters, Chen, & Komiyama, 2014;Xiao et al., 2016). We performed two-photon Ca 2+ imaging, electrical recording, and optogenetics to identify the abnormalities in M1 activity in these mice. Then, we demonstrated that the repetitive pairing of forelimb movements with synchronous activation of thalamocortical axon terminals was able to restore learning.  (Kagawa et al., 1994;Tanaka et al., 2009) and parvalbumin (PV)-Cre mice (C57BL/6 background) (Tanahira et al., 2009) were anesthetized by intraperitoneal injection of ketamine (74 mg/kg) and xylazine (10 mg/kg). Prior to surgery, the skin was disinfected with 70% alcohol and the skull was exposed and cleaned, and a head plate was firmly attached with dental cement (Fuji luto BC; GC, Tokyo, Japan, Bistite II; Tokuyama Dental, Tokyo, Japan). The surface of the intact skull was subsequently coated with acrylic-based dental adhesive resin cement (Super bond; Sun Medical, Shiga, Japan) to avoid drying, as previously described (Masamizu et al., 2014). Mice were allowed to recover for 1 day before virus injection. Up to five animals were kept together in a cage before the head plate was attached.
After surgery, mice were housed singly to avoid damage to the head plate and the glass window.

| Self-initiated lever-pull task
The voluntary (self-initiated) forelimb movement task was modified from a previously described procedure (Hira et al., 2013;Masamizu et al., 2014). Mice with head plates were habituated to the task device for 1 hr per day for 2 days before the start of the experiment. The mice were inserted into a body chamber and their head was fixed by placing the head plate between holders attached to a stage. After habituation, mice were water-deprived in their home cages and maintained at 85% of their normal body weight throughout the experiments. Each mouse was trained to perform the voluntary rightforelimb movement for 1 hr per day for 12 training days. When mice pulled the lever by 5 mm and held it there for 600 ms, they were rewarded with a 4 μL drop of water from a spout near their mouths.
The lever was then quickly returned to the wait position by a solenoid valve. The lever could not be moved again from the wait position for 1 s. After this immobilization period, mice were allowed to pull the lever again. If a lever pull failed to be held for the full 600 ms (i.e., a failed trial), the lever was not immobilized and the mice were allowed to again pull the lever at any time. A program written in LabVIEW (National Instruments, Austin, TX) was used to control the timing of the reward, the lever return, and the immobilization of the lever, and continuously monitor the lever position. Mouse behavior and performance was monitored with an infrared video camera. Mice were allowed ad libitum access to 1 ml water following each training session. For Figure 1b-d, 20 wild-type (WT) mice (10 with and 10 without AAV transduction) and 19 PLP-tg mice (9 with and 10 without AAV transduction) were used.

| Two-photon imaging
Two-photon images were acquired from the left M1 during performance of the lever-pull task (on Days 1, 3, 5, 7, and 9) using either an FV1000-MPE microscope (Olympus, Tokyo, Japan) with a ×25 objective (XLPlan, NA 1.05; Olympus) and a mode-locked Ti:sapphire laser (MaTai HP; Spectra Physics, CA) at a wavelength of 910-920 nm, or an LSM 7 MP system (Zeiss, Jena, Germany) with a ×20 objective (XLPlan, NA 1.0; Zeiss) and a mode-locked Ti:sapphire Chameleon Ultra II laser (Coherent, Santa Clara, CA) tuned to 920 nm. Fluorescence was collected with GaAsP photomultiplier tubes (Hamamatsu Photonics, Shizuoka, Japan). Laser intensity was 20-40 mW. The imaged fields were 508.93 × 508.93 μm at 150-200 μm depth below the cortical surface. Pixel size was 1.988 μm. Frame duration was 420 ms. Continuous 1,000-frame imaging was repeated in each field for 10-30 min. The lever position and solenoid signal were recorded simultaneously with the Ca 2+ imaging. Two-photon imaging in WT/PV-Cre and PLP-tg/PV-Cre mice was performed on Day 1.
Fields in which motion artifacts remained after correction and crowded fields were removed. The outlines of the cell-based regions of interest (ROIs) were determined using a semi-automated algorithm based on maximization of the correlation coefficients of the intensities of nearby pixels over time, along with cell sizes and shapes, and were confirmed by visual inspection (Masamizu et al., 2014). The numbers of analyzed ROIs were as follows: WT mice (five fields from five mice on each day), 1, 203 on Day 3, 213 on Day 5, 240 on Day 7, and 199 on Day 9. The numbers of analyzed PV-positive ROIs were as follows: WT mice, 115 (five fields from four mice); PLP-tg mice, 145 (nine fields from five mice). The fluorescence within each ROI was averaged, and background fluorescence was subtracted from this average value. Ca 2+ transients were defined according to the method in a previous report (Eto et al., 2011). All frames were divided into a sequence of segments with monotonically increasing fluorescence values that started at the initial time point and finished at the end time point. For each segment, when the increase in the fluorescence value from the initial to end time points was more than fourfold the SD of the 35th percentile of total fluorescence, one Ca 2+ transient was assigned to the end time point. We then defined the task-related Ca 2+ transient as the time point existing 1.2 s before the start point of successful lever pulling behavior and 1.6 s after the start point of successful lever pulling behavior. Imaging sessions with more than three successful lever pulls were analyzed. If the time point of a Ca 2+ transient was outside of the period from 1.2 s before the start point of any lever pull to 1.6 s after the start point of any lever pull, it was defined as a spontaneous Ca 2+ transient.

| In vivo electrophysiology
Sixteen-channel silicone probes with 177 μm 2 recording sites (NeuroNexus Technologies, Ann Arbor, MI) spaced 25 μm apart at depths of 500-600 μm below the cortical surface were used to record L2/3 and L5 neuronal activity in the left M1 in 8-9-week-old mice. All in vivo recordings were acquired using the Omniplex system (Plexon, Dallas, TX). Spike signals were filtered at a bandpass of 300 Hz to 8 kHz. Spikes were detected by threshold-level crossing, generally set at 50 μV, and sorting of single units was carried out using principal component analysis in an offline sorter (Plexon). In L2/3, 68 units from 10 awake WT mice, 63 units from 10 anesthetized WT mice, 56 units from 13 awake PLP-tg mice, and 51 units from 12 anesthetized PLP-tg mice were recorded. In L5, 55 units from nine awake WT mice, 55 units from nine anesthetized WT mice, 58 units from nine awake PLP-tg mice, and 58 units from nine anesthetized PLP-tg mice were recorded. In the CNQX experiment, 10 units were recorded from four PLP-tg mice.

| Optogenetic stimulation of thalamic neurons
A total of 500 nl AAV2/1-Syn-ChR2 (H134R)-EYFP (2.11 × 10 13 vector genomes/ml, UPenn Vector Core) was injected into the left thalamus (1 mm posterior and 1 mm lateral from the bregma, and 3.2 mm below the pia). After 2-3 weeks of virus injection, electrophysiological experiments with optogenetic stimulation were performed using planar electrodes under 0.8-1% isoflurane anesthesia to reduce behavioral effects in the awake state. For optogenetic stimulation of thalamic afferents in the cortex, the fiber-optic cable was placed close to the cortical surface that had been exposed by the craniotomy. For optogenetic stimulation of the thalamus, a fiber-optic cable (~500 μm diameter) was acutely implanted at the same coordinates as the thalamic viral injection but at a depth of 2.2 mm. Then, 20 × 20 ms pulses of a 470 nm LED (Lumencor, Beaverton, OR) were delivered at 1 Hz to elicit responses. The power at the fiber-optic tip was approximately 4 mW. Units that showed a stable (at least one) response to every optogenetic stimulation were recorded, and spikes that occurred within 50 ms after the onset of the stimulation were analyzed.

| Statistics
All data are presented as the mean ± SEM. Unpaired, paired, and onesample t tests, Wilcoxon rank sum tests, ANOVA followed by Tukey's post hoc test and Pearson's correlation tests were used to test for statistical significance. The variances within each group of data were estimated. For Ca 2+ imaging and electrophysiology data, the D'Agostino and Pearson normality test was first performed. Then, parametric or nonparametric tests were used to test for significance, depending on the data distribution.

| RESULTS
3.1 | PLP-tg mice show impairments of learning in a lever-pull task We examined whether 2-month-old PLP-tg mice show impaired learning of a motor task: a self-initiated lever-pull task using the right forelimb (Hira et al., 2013;Masamizu et al., 2014) (Figure 1a). In this task, mice had to pull the lever and hold it there for 600 ms to receive a drop of water. Mice performed this task for 60 min per session (one session per day) during 12 sessions. First, we compared the time course of the 60 min-averaged task performance between wild-type (WT) and PLP-tg mice. In the early training stage (Days 1-4), there was no difference between WT littermates and PLP-tg mice in either the success rate or the number of successful lever pulls (Figure 1b,c).
In addition, the total number of lever-pull trials did not differ ( Figure 1b,c). Thus, PLP-tg mice had the ability to move the forelimb, and recognized the association between the action (the lever-pull movement) and outcome (water reward). This is consistent with the lack of deficits exhibited by 2-month-old PLP-tg mice in other motor tests (wire hang, grip strength, and rotarod tests) (Tanaka et al., 2009).
After sessions 1-4, the success rate of lever-pull trials continued to increase in both strains of mice ( Figure 1b). However, in the late training stage (Days 9-12), the success rate and success number were lower in PLP-tg mice than in WT mice, although the total number of lever-pull trials was not different between WT and PLP-tg mice (Figure 1b,c). Even when we used the behavioral data during the initial 10 min of the session, when the mice were thought to be more highly motivated than during the following 50 min period, the success rate and success number in the late stage were lower in PLP-tg mice than in WT mice (Figure 1d). This indicates that although the motivation to receive the reward was not apparently different between WT and PLP-tg mice, the late-stage improvement in motor performance was impaired in PLP-tg mice.
It is possible that this impairment could be caused by enhanced anxiety (Tanaka et al., 2009); when the experiment was repeated with mice that had been habituated to the chamber for 1 week prior to the start of training, the difference in the success number in the late stage disappeared, but the difference in the success rate remained ( Figure 1e). In the early sessions, the success rate or the number of successful trials did not differ (Figure 1e). Even when the training period was extended, the success rate in PLP-tg mice did not reach the level of that achieved by WT mice (Figure 1f). Furthermore, the number of successful or total lever pulls was not different between these two groups ( Figure 1f). These results indicate that the low success rate in the late training stage in PLP-tg mice was not caused by Although the 60 min or initial 10 min-averaged task performance in the early sessions was not different between WT and PLP-tg mice ( Figure 1b-d), changes in the performance within a session could still be different. If the memory for the task acquired in a session was maintained, the success rate should be high at the beginning of the next session (Costa, Cohen, & Nicolelis, 2004;Yin et al., 2009) and gradually decrease within the session as the water consumption increased. We divided the 60 min period into two 30 min periods, and compared the success rate between the initial 30 min period and the last 30 min periods. In WT mice, from session 4, the initial 30 min period showed a higher success rate than the last 30 min period (Supporting Information Figure S1). By contrast, in PLP-tg mice, the success rate of the initial 30 min was higher than that of the last 30 min period after session 7 (Supporting Information Figure S2). This result suggests that PLPtg mice were relatively slow in retaining the motor memory from an earlier session in comparison with the WT mice.
In the following experiments, we used the success rate (60 minaveraged) as a robust behavioral indicator for the degree of motor learning, and examined what abnormal cellular functioning in the brain was associated with the low success rate in PLP-tg mice.
3.2 | Increase in myelin-related mRNA correlates with improvement of the task performance in WT mice 3.3 | Abnormalities of spontaneous cortical activity and task-related activity in PLP-tg mice We examined how the deficits in the regulation of myelination in PLPtg mice impaired the neural activity required for motor learning. We tested two simple possibilities in M1 neurons: whether PLP-tg mice showed lower task-related activity than WT mice, which would directly impair the motor execution, and whether PLP-tg mice showed higher spontaneous activity than WT mice, which would decrease the signal-to-noise ratio in the learning-relevant circuits. First, in the early and late training sessions, we conducted two-photon imaging of layer 2/3 (L2/3) neurons in M1, which expressed GCaMP3 (Tian et al., 2009) (Figure 3a). Two to 3 weeks before the training started, we injected an adeno-associated virus (AAV) carrying the GCaMP3 gene into the left M1. We used synapsin I promotor so that GCaMP3 was mainly expressed in excitatory neurons (Masamizu et al., 2014;O'Connor, Peron, Huber, & Svoboda, 2010). Many GCaMP3-expressing neurons showed Ca 2+ transients, both during the lever-pull movement and outside of the movement (Figure 3b). We divided each imaging session into three periods: a task-related period that started 1.2 s before and ended 1.6 s after the start point of successful lever pulling behavior, a spontaneous period that was outside of the period from 1.2 s before and ending 1.6 s after the start point of any lever pull, and a third "other" period, which included failed trials (Figure 3c). We then calculated the frequency and amplitude of Ca 2+ transients in the task-  Table 1). When the task-related period was set to be longer or shorter than that stated above, the results were similar (Table 1). Thus, in both early and late sessions, PLP-tg mice showed lower task-related Ca 2+ transients, and a higher frequency and smaller amplitude of spontaneous Ca 2+ transients than WT mice.
We confirmed the increased spontaneous firing in awake PLP-tg mice without any training by performing multi-unit recordings from L2/3 and L5 in M1 (Figure 4a-c). When excitatory synaptic transmission was blocked, spontaneous firing in PLP-tg mice reduced ( Figure 4d,e). This was similar to the result obtained in a mouse model of Alzheimer's disease (Busche et al., 2008). Anesthesia substantially reduced the frequency of spontaneous firing and abolished the difference between WT and PLP-tg mice (Figure 4f,g). The frequency of spontaneous Ca 2+ transients on each day of training was inversely correlated with the change in success rate from that day to the next, with this being the case in any WT and PLP-tg mice, which was statistically significant (Figure 3f). This implies that day-by-day fluctuations in spontaneous activity could affect the motor learning performance at each day.

| PLP-tg mice had no defects in spontaneous activity of inhibitory neurons
The increased spontaneous activity in mouse models of Alzheimer's disease (Busche et al., 2008) and fragile X syndrome (Goncalves et al., 2013) is related to defects in inhibition, and is detected even in the anesthetized state. Thus, we tested whether the origin of the increased spontaneous activity in PLP-tg mice was related to the abnormality in inhibitory neurons. We performed two-photon Ca 2+ imaging of L2/3 PV-positive neurons on Day 1 of training (Figure 5a, b). We crossbred PLP-tg with PV-Cre mice (Tanahira et al., 2009), and injected an AAV coding GCaMP6f (Chen et al., 2013), which expressed in a Cre-dependent manner. The frequency and amplitude of the spontaneous Ca 2+ transients of these neurons were not different between WT and PLP-tg mice (Figure 5c and Table 1). The frequency and amplitude of task-related Ca 2+ transients also showed no differences between these two groups ( Figure 5c and Table 1). These results indicate that the learning impairment in PLP-tg mice is not mainly caused by a defect in the activity of inhibitory neurons.
In the following experiments, we tested whether changes in the axonal conduction of myelinated long-range axons in PLP-tg mice were associated with the abnormal M1 neuronal activity and low success rate.   Kimura, 2003). Therefore, we measured the conduction time of antidromic spikes of thalamic axons projecting to M1 in PLP-tg mice. We injected an AAV carrying the channelrhodopsin-2 (ChR2) gene into the thalamus, including the motor thalamus (Figure 6a).

| Temporal dispersion of axonal conduction in PLP-tg mice
We then recorded antidromically evoked spikes in the thalamus, while optogenetically stimulating the thalamic axons in M1 with blue light at 1 Hz (Figure 6a,b). In both WT and PLP-tg mice, spikes with very short jitter (~0.15 ms) and high fidelity (~80%) were evoked (Figure 6c,d). These parameters did not differ between WT and PLP-tg mice (Figure 6d). Thus, optogenetic activation reliably evoked antidromic spikes in the same thalamic neurons in both mice. In the antidromic spikes, the spike latency from the onset of the stimulation was longer in PLP-tg mice than in WT mice ( Figure 6e). Furthermore, the temporal dispersion of the antidromic spike latency across different recording channels in the same animal was broader in PLP-tg mice than in WT mice (Figure 6f). These results demonstrate that in PLP-tg mice the axonal conduction in thalamocortical axons was not only reduced but also temporally spread.

| Stimulation of thalamic neurons evoked asynchronous cortical activity in PLP-tg mice
Second, we measured M1 activity responding to the optogenetic stimulation of the thalamic axons in M1 (Figure 7a). When these were stimulated at 1 Hz, spikes in L2/3 of M1 were evoked in similar T A B L E 1 Results of Ca 2+ image analysis using long and short task-related periods To validate the statistical significance of Figure 3d,e and Figure 5c, two different time windows were used to redefine the task-related Ca 2+ transients and spontaneous Ca 2+ transients. Time window 1 started 0.8 s before and ended 1.2 s after the start of successful lever-pull trials (short). Time window 2 started 1.2 s before and ended 2 s after the start of successful lever-pull trials (long). All data are presented as the mean ± SEM. Unpaired t tests and Wilcoxon rank sum tests were used to test for statistical significance.

| Synchronous stimulation of thalamic afferents rescues the learning performance in PLP-tg mice
If asynchronous activity in thalamocortical axons impaired the latestage success rate in PLP-tg mice, artificial synchronous activity in thalamocortical axons during the lever-pull movement would raise the decreased task-related activity, and could restore the success rate.
ChR2 stimulation of thalamocortical axons at 5 or 10 Hz enhances the recovery of somatosensory cortical circuit function and forepaw sensorimotor abilities after stroke (Tennant, Taylor, White, & Brown, 2017). In our experimental condition, 10 Hz stimulation of thalamocortical axons reliably induced the cortical response in WT The spontaneous firing rates of L2/3 (left) and L5 (right) neurons during the awake state (L2/3: p = 2.3 × 10 −10 , n = 68 units from 10 WT mice, n = 56 units from 13 PLP-tg mice; L5: p = .049, n = 55 units from nine WT mice, n = 58 units from nine PLP-tg mice, Wilcoxon rank sum test). Violin plot shows mean (black lines) and distribution of the data. *p < .05, ***p < .001. (d) Typical traces from recordings in L2/3 neurons in awake PLP-tg mice before and after CNQX application. (e) Spontaneous firing rate before and after CNQX application in awake PLP-tg mice (p = 6.6 × 10 −4 , n = 10 units from four mice, paired t test). ***p < .001. (f) Typical extracellular recordings from L2/3 and L5 in anesthetized WT (left) and PLP-tg (right) mice. (g) The spontaneous firing rates of L2/3 (left) and L5 (right) neurons during anesthetized states (L2/3: p = .36, n = 63 units from 10 WT mice, n = 51 units from 12 PLP-tg mice; L5: p = .66, n = 55 units from nine WT mice, n = 58 units from nine PLP-tg mice, Wilcoxon rank sum test). Violin plot shows mean (black lines) and distribution of the data. n.s.: nonsignificant mice (the average number of M1 unit responses to the stimulation was 1.93 ± 0.08, n = 31 units from two mice). Therefore, for the mice with ChR2 or GFP expression in thalamic neurons, repetitive optogenetic stimulation at 10 Hz (Castro-Alamancos, 2013;Miall et al., 1998) was applied to thalamocortical axons in M1 immediately after the onset of each lever pull in each training session (Figure 8a). In the early training stage, the success rate was not different between ChR2-expressing and GFP-expressing PLP-tg mice. However, in the late stage, ChR2-expressing PLP-tg mice showed slightly but significantly higher lever-pull success rate than GFP-expressing mice (Figure 8b,c). In addition, the success rate in both early and late stage was not different between ChR2-expressing and GFP-expressing WT mice that already received synchronized inputs from thalamus before thalamocortical axons stimulation (Figure 8d,e). Thus, the formation of synchronous activity in thalamocortical axons during learning-relevant movements was able to partially restore the success rate of PLP-tg mice, not WT mice, in the late training stage, indicating that this contribution was only present in myelin deficient mice.
Next, to assess whether activation of thalamic activity also contributes to behavior improvement, we trained the PLP-tg mice having ChR2 or GFP expression in the thalamus with the stimulation of thalamic cell bodies. In the early training stage, the success rate was not different between ChR2-expressing and GFP-expressing mice. However, in the late stage, ChR2-expressing mice showed significantly higher success rates than GFP-expressing mice (Figure 8f,g).
Optogenetic stimulation of thalamic cell bodies with lever pulling might improve thalamic activity by compensating dispersed inputs from basal ganglia and cerebellum that are crucial for motor learning (Bosch-Bouju et al., 2013;Tanaka et al., 2018). Furthermore, in ChR2-expressing mice, from session 5 onward, the initial 30 min period showed a higher success rate than the last 30 min periods (Supporting Information Figure S3), in contrast, in GFP-expressing mice, the success rate of the initial 30 min was higher than that of the last 30 min period after session 9 (Supporting Information Figure S4). Therefore, the facilitation of thalamic activity during learning-relevant movements was also able to restore the success rate in PLP-tg mice.
Finally, we determined whether the optogenetic stimulation of thalamocortical axons was still helpful for the lever-pull movement after 12 day training in PLP-tg mice. A subset of the PLP-tg mice that had performed the task with the stimulation throughout training Days 1-12 performed the task without the stimulation on Days 13 and 14.
The mean success rate over Days 11 and 12 was not different from that over Days 13 and 14 (Figure 8h). This result suggests that the synchronous input of thalamocortical axons was critical to form the M1 neuronal activity required for skill acquisition, and that after training this M1 activity might be induced without the assistance of the synchronous input of thalamocortical axons.

| DISCUSSION
This study demonstrates that impaired myelination increases axonal conduction variability in long-range axons, thereby leading to a greater spread in the timing of synaptic transmission and postsynaptic activity, and that this abnormality can be the origin of other abnormalities of the neural circuits, such as weak task-related activity and high basal activity. The weak task-related activity is presumably caused by a decrease in the coincident long-range synaptic inputs required for induction of strong burst firing in postsynaptic neurons (Figure 9). The abnormally high basal activity observed in PLP-tg mice may be caused by occasionally occurring synchronous events of temporally-spread synaptic transmission from many distant areas. These abnormalities would weaken the propagation of information and STDP within cortical networks (Moldakarimov, Bazhenov, & Sejnowski, 2015;Pajevic et al., 2014) and may deprive neurons of the ability to regulate activity-dependent myelination in respect to white matter plasticity The frequency and amplitude of task-related and spontaneous Ca 2+ transients in PV neurons on Day 1 (n = 115 from five fields in four WT/PV-Cre mice, n = 145 from nine fields in five PLP-tg/PV-Cre mice, task-related: frequency, p = .051, Wilcoxon rank sum test; amplitude, p = .63, unpaired t test; spontaneous: frequency, p = .72, Wilcoxon rank sum test; amplitude, p = .86, unpaired t test). Violin plot shows the mean (black lines) and distribution of the data. n.s.: nonsignificant (Hines et al., 2015;Mensch et al., 2015;Wake et al., 2011;Wake et al., 2015). During development, myelination changes the CV of thalamocortical pathways to increase synchronicity of the cortical activity, which may facilitate cortical information processing (Kimura & Itami, 2009;Salami et al., 2003). It should be clarified in the future whether and to what extent the CVs of thalamocortical axons become increasingly synchronized, and their synapses or downstream synapses show STDP during learning processes.
Abnormally high basal activity in the cerebral cortex is also a feature in Alzheimer's disease (Busche et al., 2008) and Fragile X syndrome (Goncalves et al., 2013) mouse models. PLP-tg mice at 2 months of age show behavioral abnormalities related to schizophrenia (Tanaka et al., 2009). Taken together, abnormally high basal activity may be a common phenotype in neurological and psychiatric disorders. Even in WT mice, the low basal activity on a given day tended to be correlated with the increase in the success rate on that day. In WT mice, the basal activity was lower than in PLP-tg mice and varied daily, and therefore this effect may not be distinguished over training sessions. In the mouse models of Alzheimer disease (Busche et al., 2008) and Fragile X syndrome (Goncalves et al., 2013), high basal activity was also detected in the anesthetized state, whereas the basal activity was similar between anesthetized WT and PLP-tg mice. In contrast to the former mouse models, PLPtg mice did not show any apparent defect in inhibition. Thus, the underlying mechanism was probably different. If the abnormal basal activity generally precedes the behavioral deficits, it may be a preclinical phenotype.
The motor impairment in PLP-tg mice was not clearly detected in the early training stage. However, during the early training stage, the success rate of initial 30 min period was higher compared to that of the last 30 min period in PLP-tg mice than in WT mice. This suggests that the improvement in the task performance in an early session was  connecting distant areas, and information processes with a high signal-to-noise ratio, may be important (Gibson et al., 2014;McKenzie et al., 2014;Xiao et al., 2016). In contrast to our results, the effect of impaired myelin regulation was detected from the beginning of motor training in other studies (McKenzie et al., 2014;Xiao et al., 2016).
These assessed motor learning ability using a running wheel with irregularly spaced rungs, in which the mice had to coordinate many parts of the body to avoid missing their step, even in the first session.
This task may have a much greater requirement for synchronous longrange axonal transmission than the task we used, and the effect of impaired myelination may be apparent and detectable from the first to the final session. We demonstrated that motor learning could be restored by optogenetically stimulating a population of thalamocortical axons and thus overcoming asynchrony in long-range synaptic inputs. However, the extent of the rescue appeared partial because the late training stage success rate in this experiment (Figure 8c) was lower than that in the normal training schedule in WT mice (Figure 1c). The reason why the restoration was partial may be that other long-range axons such as the pyramidal tracts were involved in the motor learning. In addition, the duration of the optogenetic assistance was limited to the lever-pull movement, so the effect of the high frequency of basal F I G U R E 8 Effects of optogenetic stimulation of thalamic neurons on motor learning. (a) Schematic of the optogenetic stimulation-assisted lever-pull task. Repetitive optogenetic stimulation of thalamocortical axons or thalamic cell bodies was triggered when the lever position crossed a threshold. (b) Time course of the 60 min-averaged lever-pull success rate in PLP-tg mice with optogenetic stimulation of ChR2-(n = 16; light blue) or GFP-(n = 10; green) expressing thalamocortical axons. (c) Success rate in early (Days 1-4) and late (Days 9-12) training stages in PLP-tg mice with blue light illumination of ChR2-(light blue) or GFP-(green) expressing thalamocortical axons (F 1 , 48 = 4.73, p = 1.0 [early], p = 0.0097 [late], two-way ANOVA followed by Tukey's test). Data are presented as the mean ± SEM. **p < .01, n.s.: nonsignificant. (d) Time course of the 60 min-averaged lever-pull success rate in WT mice with optogenetic stimulation of ChR2-(n = 5; light red) or GFP-(n = 5; light green) expressing thalamocortical axons. (e) Success rate in early (Days 1-4) and late (Days 9-12) training stages in WT mice with ChR2-(light red) or GFP-(light green) expressing thalamocortical axons during optogenetic stimulation (F 1 , 16 = 0.09, p = 1.0 [early], p = .97 [late], two-way ANOVA followed by Tukey's test). Data are presented as the mean ± SEM. n.s.: nonsignificant. (f) Time course of the 60 min-averaged lever-pull success rate in PLP-tg mice with optogenetic stimulation of ChR2-(n = 5; light blue) or GFP-(n = 5; green) expressing thalamic cell bodies. (g) Success rate in early (Days 1-4) and late (Days 9-12) training stages in PLP-tg mice with ChR2-(light blue) or GFP-(green) expressing thalamic cell bodies during optogenetic stimulation (F 1,16 = 20.63, p = .063 [early], p = .0098 [late], two-way ANOVA followed by Tukey's test). Data are presented as the mean ± SEM. **p < .01, n.s.: nonsignificant. (h) Success rates averaged over Days 11 and 12 (left) and over Days 13 and 14 (right) in ChR2-expressing PLP-tg mice with optogenetic stimulation during Days 1-12, but not Days 13 and 14 (p = .24, n = 10, paired t test). Black lines indicate the mean ± SEM. n.s.: nonsignificant formed. Another interpretation of this result might be that an antidromic activity in thalamo-cortical axons induced by optogenetic stimulation contributes to activity-dependent myelination that promotes synchrony of spike time arrival. In psychiatric diseases such as schizophrenia, subtle impairment of myelin regulation and thalamocortical dysrhythmia are observed (Hakak et al., 2001;Nave & Ehrenreich, 2014;Schulman et al., 2011). In the much larger human brain, myelin regulation of cortico-cortical long-range axons such as transcallosal cortical projections is also crucial (Nave, 2010), and is necessary for cognition and learning (Nave & Ehrenreich, 2014;Whitford, Ford, Mathalon, Kubicki, & Shenton, 2012). If a population of cortically projecting axons or a subset of cortical neurons can be synchronously stimulated with transcranial magnetic stimulation during relevant behaviors and sensory inputs (Frantseva et al., 2008;Thabit et al., 2010;Zeller et al., 2010), it might be possible to improve cognitive and behavioral abnormalities in the early stages of diseases featuring impaired myelination.

Subtle myelin impairment
Increase in variability of axonal conduction in myelinated axons Dispersion of synaptic transmission from long-range axons Increase in baseline activity (noise) Decrease in signal and synchronization Impairment in motor learning Vulnerability in relevant information transfer Deficit in spike-timing-dependent plasticity Deficit in myelination for motor learning Vulnerability in cell assembly for motor learning F I G U R E 9 Proposed mechanisms by which subtle myelin dysregulation causes motor learning impairment. Optogenetic stimulation of thalamocortical axons was assumed to partially rescue the pathways indicated by thick arrows