Neural reactivations during sleep determine network credit assignment.

A fundamental goal of motor learning is to establish the neural patterns that produce a desired behavioral outcome. It remains unclear how and when the nervous system solves this 'credit assignment' problem. Using neuroprosthetic learning, in which we could control the causal relationship between neurons and behavior, we found that sleep-dependent processing was required for credit assignment and the establishment of task-related functional connectivity reflecting the casual neuron-behavior relationship. Notably, we observed a strong link between the microstructure of sleep reactivations and credit assignment, with downscaling of non-causal activity. Decoupling of spiking to slow oscillations using optogenetic methods eliminated rescaling. Thus, our results suggest that coordinated firing during sleep is essential for establishing sparse activation patterns that reflect the causal neuron-behavior relationship

Emergent modular neural control drives coordinated motor actions.

A remarkable feature of motor control is the ability to coordinate movements across distinct body parts into a consistent, skilled action. To reach and grasp an object, 'gross' arm and 'fine' dexterous movements must be coordinated as a single action. How the nervous system achieves this coordination is currently unknown. One possibility is that, with training, gross and fine movements are co-optimized to produce a coordinated action; alternatively, gross and fine movements may be modularly refined to function together. To address this question, we recorded neural activity in the primary motor cortex and dorsolateral striatum during reach-to-grasp skill learning in rats. During learning, the refinement of fine and gross movements was behaviorally and neurally dissociable. Furthermore, inactivation of the primary motor cortex and dorsolateral striatum had distinct effects on skilled fine and gross movements. Our results indicate that skilled movement coordination is achieved through emergent modular neural control

Low-frequency cortical activity is a neuromodulatory target that tracks recovery after stroke.

Recent work has highlighted the importance of transient low-frequency oscillatory (LFO; <4 Hz) activity in the healthy primary motor cortex during skilled upper-limb tasks. These brief bouts of oscillatory activity may establish the timing or sequencing of motor actions. Here, we show that LFOs track motor recovery post-stroke and can be a physiological target for neuromodulation. In rodents, we found that reach-related LFOs, as measured in both the local field potential and the related spiking activity, were diminished after stroke and that spontaneous recovery was closely correlated with their restoration in the perilesional cortex. Sensorimotor LFOs were also diminished in a human subject with chronic disability after stroke in contrast to two non-stroke subjects who demonstrated robust LFOs. Therapeutic delivery of electrical stimulation time-locked to the expected onset of LFOs was found to significantly improve skilled reaching in stroke animals. Together, our results suggest that restoration or modulation of cortical oscillatory dynamics is important for the recovery of upper-limb function and that they may serve as a novel target for clinical neuromodulation

Reactivation of task-related neural ensembles during slow-wave sleep.

<p><b>(A)</b> Example of reactivation events prior to and after learning (i.e., Sleep₁ versus Sleep₂ events in respective blue and red boxes). Also shown are the activation strengths of reactivation events during the initial NREM epochs from Sleep₁ and Sleep₂. <b>(B)</b> Across all animals, there was a significant reactivation of task-related ensembles (<i>p</i> < 0.001, sign-rank test). Quantification was based on the entire recorded NREM sleep. <b>(C)</b> Linear correlation between single neuron reactivations and neural modulation during Reach₂. To estimate “single neuron reactivation,” we first calculated the overall ensemble reactivation [(mean activation during NREM from Sleep₂)–(mean activation during NREM from Sleep₁)] and then multiplied this value with the principle component weight for each neuron. Plot shows the regression analysis between each neuron’s reactivation and subsequent temporal shift (r = -0.41, <i>p</i> < 0.001). Error bars show S.E.M. * <i>p</i> < 0.05, ** <i>p</i> < 0.01,*** <i>p</i> < 0.001.</p

Sleep-Dependent Reactivation of Ensembles in Motor Cortex Promotes Skill Consolidation

<div><p>Despite many prior studies demonstrating offline behavioral gains in motor skills after sleep, the underlying neural mechanisms remain poorly understood. To investigate the neurophysiological basis for offline gains, we performed single-unit recordings in motor cortex as rats learned a skilled upper-limb task. We found that sleep improved movement speed with preservation of accuracy. These offline improvements were linked to both replay of task-related ensembles during non-rapid eye movement (NREM) sleep and temporal shifts that more tightly bound motor cortical ensembles to movements; such offline gains and temporal shifts were not evident with sleep restriction. Interestingly, replay was linked to the coincidence of slow-wave events and bursts of spindle activity. Neurons that experienced the most consistent replay also underwent the most significant temporal shift and binding to the motor task. Significantly, replay and the associated performance gains after sleep only occurred when animals first learned the skill; continued practice during later stages of learning (i.e., after motor kinematics had stabilized) did not show evidence of replay. Our results highlight how replay of synchronous neural activity during sleep mediates large-scale neural plasticity and stabilizes kinematics during early motor learning.</p></div

Comparison of online and offline changes in neural activation.

<p><b>(A)</b> Change in movement-related activation of a single neuron during Reach_1early, Reach_1late, and Reach_2early. Dotted line is the time of reach onset. Traces below represent a Bayesian adaptive regression spline fit of the respective PETH. <b>(B)</b> Curves show respective cumulative distributions of the single neuron PETH time to peak (red dot represents median timing). While there was not a significant change in the distribution for online learning (Kolmogorov-Smirnoff test, <i>p</i> = 0.9 comparing Reach_1early versus Reach_1late, there was a significant shift after sleep (<i>p</i> < 0.001 comparing Reach_2early to both Reach_1early and Reach_1late, <i>n</i> = 4 animals, 99 units). <b>(C)</b> Change in task-related neural modulation. Task related modulation index is ratio of baseline firing to the peak instantaneous firing rate <i>n</i> = 4 animals, 99 units). Error bars show standard error of the mean (S.E.M.) * <i>p</i> < 0.05, *** <i>p</i> < 0.001.</p

Relationship of ensemble reactivation to NREM oscillations.

<p><b>(A)</b> Event-triggered z-scored LFP for fast-spindle oscillatory frequencies filtered at 13–16 Hz. Post-hoc differences (<i>p</i> < 0.05) indicated by points above graph. <b>(B)</b> Event-triggered LFP for slow-spindle frequencies (9–12 Hz). There were no significant differences across any time point. (<b>C)</b> Event-triggered LFP for slow oscillation frequencies (0.5–4 Hz). <b>(D)</b> Comparison of the coefficient of variation (CV) for each oscillatory band. For fast spindles and slow oscillations, for each point that was significantly different between the two groups, we compared the CV. After learning, the CV was significantly reduced, suggesting more consistent temporal locking after learning. For slow-spindle oscillations, because no points were different between the two groups, we instead used the points that were significantly different in the fast-spindle frequency. <b>(E)</b> Comparison of the z-scored temporal coupling between reactivation-triggered slow wave and fast-spindle oscillation before and after learning. <b>(F)</b> Changes in mean event-triggered spindle amplitude. We calculated the instantaneous analytic amplitude of the fast spindles at the slow-wave trough for each reactivation. There was a significant increase in spindle amplitude. <b>(G)</b> Increase in the probability that a reactivation event was associated with a fast-spindle oscillation. Error bars show S.E.M. * <i>p</i> < 0.05, ** <i>p</i> < 0.01,*** <i>p</i> < 0.001.</p

Behavioral paradigm.

<p><b>(A)</b> Illustration of behavioral task [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002263#pbio.1002263.ref033" target="_blank">33</a>], in which animals are required to reach through a narrow slit to grasp and retrieve a pellet into their cage. <b>(B)</b> Picture of animals grasping a pellet. Green line shows an example reach trajectory from which kinematic analyses are performed. <b>(C)</b> Overall behavioral/recording paradigm involved four blocks: a 2-h block of spontaneous recording (Sleep₁), a block of skilled motor training (Reach₁), another 2-h block of spontaneous recording (Sleep₂), followed by a final reach block (Reach₂).</p

Sleep restriction prevents offline gains and temporal shifts.

<p><b>(A)</b> Representative example of lack of improvement in speed after sleep-restriction (moving window average of 10 trials). <b>(B)</b> Comparison of effects of sleep restriction versus sleep (<i>n</i> = 5 each) on changes in motor speed and “neural speed” (i.e., shift in PETH peak; <i>n</i> = 99 neurons from sleep animals, <i>n</i> = 80 neurons from sleep-restricted animals). Error bars show S.E.M. *** <i>p</i> < 0.001 <b>(C)</b> Cumulative distribution of single neuron peak of PETH (non-significant based on Kolmogorov Smirnoff test).</p