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BACKGROUND: Transcranial magnetic stimulation (TMS) interventions could feasibly treat stroke-related motor impairments, but their effects are highly variable. Brain state-dependent TMS approaches are a promising solution to this problem, but inter-individual variation in lesion location and oscillatory dynamics can make translating them to the poststroke brain challenging. Personalized brain state-dependent approaches specifically designed to address these challenges are therefore needed. METHODS: As a first step towards this goal, we tested a novel machine learning-based EEG-TMS system that identifies personalized brain activity patterns reflecting strong and weak corticospinal tract (CST) output (strong and weak CST states) in healthy adults in real-time. Participants completed a single-session study that included the acquisition of a TMS-EEG-EMG training dataset, personalized classifier training, and real-time EEG-informed single pulse TMS during classifier-predicted personalized CST states. RESULTS: MEP amplitudes elicited in real-time during personalized strong CST states were significantly larger than those elicited during personalized weak and random CST states. MEP amplitudes elicited in real-time during personalized strong CST states were also significantly less variable than those elicited during personalized weak CST states. Personalized CST states lasted for ~1-2 seconds at a time and ~1 second elapsed between consecutive similar states. Individual participants exhibited unique differences in spectro-spatial EEG patterns between personalized strong and weak CST states. CONCLUSION: Our results show for the first time that personalized whole-brain EEG activity patterns predict CST activation in real-time in healthy humans. These findings represent a pivotal step towards using personalized brain state-dependent TMS interventions to promote poststroke CST function.

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Motor cortical (M1) transcranial magnetic stimulation (TMS) increases corticospinal output and improves motor learning when delivered during sensorimotor mu rhythm trough but not peak phases, suggesting that mechanisms supporting motor learning may be most active during mu trough phases. If so, learning-related corticospinal plasticity should be most evident during mu trough phases. Healthy adults were assigned to either a sequence or control group. Participants in the sequence group practiced the implicit serial reaction time task (SRTT), which contained an embedded, repeating 12-item sequence. Participants in the control group practiced a version of the SRTT that contained no sequence. We measured mu phase-independent and phase-dependent MEP amplitudes using EEG-informed single-pulse TMS before, immediately, and 30 minutes after the SRTT in both groups. All participants performed a retention test one hour after SRTT acquisition. In both groups, mu phase-independent MEP amplitudes increased following SRTT acquisition, but the pattern of mu phase-dependent MEP amplitude increases after SRTT acquisition differed between groups. MEP amplitude changes from baseline to 30 minutes after SRTT acquisition more strongly differed across phases in the control relative to the sequence group, with the control group showing smaller increases in peak- than trough-specific MEPs. Contrary to our original hypothesis, results revealed that sequence learning recruits peak- rather than trough-specific neurophysiological mechanisms. Overall, these findings suggest that mu peak phases may provide protected time windows for motor memory consolidation and demonstrate the presence of a mu phase-dependent motor learning mechanism in the human brain. Significance statement: Recent work suggests that the neurophysiological mechanisms supporting motor learning may be most active during sensorimotor mu rhythm trough phases. Here, we evaluated this possibility by measuring mu phase-dependent corticospinal plasticity induced by motor sequence learning. Results provide first evidence that motor sequence learning produced corticospinal plasticity that was more pronounced during mu peak than trough phases, demonstrating the presence of a phase-dependent learning mechanism within the human motor system.

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Stroke is one of the most common causes of disability, and there are few treatments that can improve recovery after stroke. Therapeutic development has been hindered because of a lack of understanding of precisely how neural circuits are affected by stroke, and how these circuits change to mediate recovery. Indeed, some of the hypotheses for how the CNS changes to mediate recovery, including remapping, redundancy, and diaschisis, date to more than a century ago. Recent technological advances have enabled the interrogation of neural circuits with ever greater temporal and spatial resolution. These techniques are increasingly being applied across animal models of stroke and to human stroke survivors, and are shedding light on the molecular, structural, and functional changes that neural circuits undergo after stroke. Here we review these studies and highlight important mechanisms that underlie impairment and recovery after stroke. We begin by summarizing knowledge about changes in neural activity that occur in the peri-infarct cortex, specifically considering evidence for the functional remapping hypothesis of recovery. Next, we describe the importance of neural population dynamics, disruptions in these dynamics after stroke, and how allocation of neurons into spared circuits can restore functionality. On a more global scale, we then discuss how effects on long-range pathways, including interhemispheric interactions and corticospinal tract transmission, contribute to post-stroke impairments. Finally, we look forward and consider how a deeper understanding of neural circuit mechanisms of recovery may lead to novel treatments to reduce disability and improve recovery after stroke.

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Sensory perception and memory are enhanced during restricted phases of ongoing brain rhythms, but whether voluntary movement is constrained by brain rhythm phase is not known. Voluntary movement requires motor commands to be released from motor cortex (M1) and transmitted to spinal motoneurons and effector muscles. Here, we tested the hypothesis that motor commands are preferentially released from M1 during circumscribed phases of ongoing sensorimotor rhythms. Healthy humans of both sexes performed a self-paced finger movement task during electroencephalography (EEG) and electromyography (EMG) recordings. We first estimated the time of motor command release preceding each finger movement by subtracting individually measured corticomuscular transmission latencies from EMG-determined movement onset times. Then, we determined the phase of ipsilateral and contralateral sensorimotor mu (8-12 Hz) and beta (13-35 Hz) rhythms during release of each motor command. We report that motor commands were most often released between 120 and 140° along the contralateral beta cycle but were released uniformly along the contralateral mu cycle. Motor commands were also released uniformly along ipsilateral mu and beta cycles. Results demonstrate that motor command release coincides with restricted phases of the contralateral sensorimotor beta rhythm, suggesting that sensorimotor beta rhythm phase may sculpt the timing of voluntary human movement.SIGNIFICANCE STATEMENT Perceptual and cognitive function is optimal during specific brain rhythm phases. Although brain rhythm phase influences motor cortical neuronal activity and communication between the motor cortex and spinal cord, its role in voluntary movement is poorly understood. Here, we show that the motor commands needed to produce voluntary movements are preferentially released from the motor cortex during contralateral sensorimotor beta rhythm phases. Our findings are consistent with the notion that sensorimotor rhythm phase influences the timing of voluntary human movement.

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Brain state-dependent transcranial magnetic stimulation (TMS) requires real-time identification of cortical excitability states. Current approaches deliver TMS during brain states that correlate with motor cortex (M1) excitability at the group level. Here, we hypothesized that machine learning classifiers could successfully discriminate between high and low M1 excitability states in individual participants using information obtained from low-density electroencephalography (EEG) signals. To test this, we analyzed a publicly available dataset that delivered 600 single TMS pulses to the right M1 during EEG and electromyography (EMG) recordings in 20 healthy adults. Multivariate pattern classification was used to discriminate between brain states during which TMS evoked small and large motor-evoked potentials (MEPs). Results show that personalized classifiers successfully discriminated between low and high M1 excitability states in 80% of tested participants. MEPs elicited during classifier-predicted high excitability states were significantly larger than those elicited during classifier-predicted low excitability states in 90% of tested participants. Personalized classifiers did not generalize across participants. Overall, results show that individual participants exhibit unique brain activity patterns which predict low and high M1 excitability states and that these patterns can be efficiently captured using low-density EEG signals. Our findings suggest that deploying individualized classifiers during brain state-dependent TMS may enable fully personalized neuromodulation in the future.

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BACKGROUND: Skill learning engages offline activity in the primary motor cortex (M1). Sensorimotor cortical activity oscillates between excitatory trough and inhibitory peak phases of the mu (8-12 Hz) rhythm. We recently showed that these mu phases influence the magnitude and direction of neuroplasticity induction within M1. However, the contribution of M1 activity during mu peak and trough phases to human skill learning has not been investigated. OBJECTIVE: To evaluate the effects of phase-dependent TMS during mu peak and trough phases on offline learning of a newly-acquired motor skill. METHODS: On Day 1, three groups of healthy adults practiced an explicit motor sequence learning task with their non-dominant left hand. After practice, phase-dependent TMS was applied to the right M1 during either mu peak or mu trough phases. The third group received sham TMS during random mu phases. On Day 2, all subjects were re-tested on the same task to evaluate offline learning. RESULTS: Subjects who received phase-dependent TMS during mu trough phases showed increased offline skill learning compared to those who received phase-dependent TMS during mu peak phases or sham TMS during random mu phases. Additionally, phase-dependent TMS during mu trough phases elicited stronger whole-brain broadband oscillatory power responses than phase-dependent TMS during mu peak phases. CONCLUSIONS: We conclude that sensorimotor mu trough phases reflect brief windows of opportunity during which TMS can strengthen newly-acquired skill memories.

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The ability to interpret transcranial magnetic stimulation (TMS)-evoked electroencephalography (EEG) potentials (TEPs) is limited by artifacts, such as auditory evoked responses produced by discharge of the TMS coil. TEPs generated from direct cortical stimulation should vary in their topographical activity pattern according to stimulation site and differ from responses to sham stimulation. Responses that do not show these effects are likely to be artifactual. In 20 healthy volunteers, we delivered active and sham TMS to the right prefrontal, left primary motor, and left posterior parietal cortex and compared the waveform similarity of TEPs between stimulation sites and active and sham TMS using a cosine similarity-based analysis method. We identified epochs after the stimulus when the spatial pattern of TMS-evoked activation showed greater than random similarity between stimulation sites and sham vs. active TMS, indicating the presence of a dominant artifact. To do this, we binarized the derivatives of the TEPs recorded from 30 EEG channels and calculated cosine similarity between conditions at each time point with millisecond resolution. Only TEP components occurring before approximately 80 ms differed across stimulation sites and between active and sham, indicating site and condition-specific responses. We therefore conclude that, in the absence of noise masking or other measures to decrease neural artifact, TEP components before about 80 ms can be safely interpreted as stimulation location-specific responses to TMS, but components beyond this latency should be interpreted with caution due to high similarity in their topographical activity pattern.

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The beta rhythm (15-30 Hz) is a prominent signal of sensorimotor cortical activity. This rhythm is not sustained but occurs non-rhythmically as brief events of a few (1-2) oscillatory cycles. Recent work on the relationship between these events and sensorimotor performance suggests that they are the biologically relevant elements of the beta rhythm. However, the influence of these events on corticospinal excitability, a mechanism through which the primary motor cortex controls motor output, is unknown. Here, we addressed this question by evaluating relationships between beta event characteristics and corticospinal excitability in healthy adults. Results show that the number, amplitude, and timing of beta events preceding transcranial magnetic stimulation (TMS) each significantly predicted motor-evoked potential (MEP) amplitudes. However, beta event characteristics did not explain additional MEP amplitude variance beyond that explained by mean beta power alone, suggesting that conventional beta power measures and beta event characteristics similarly captured natural variation in human corticospinal excitability. Despite this lack of additional explained variance, these results provide first evidence that endogenous beta oscillatory events shape human corticospinal excitability.

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Oscillatory activity within sensorimotor networks is characterized by time-varying changes in phase and power. The influence of interactions between sensorimotor oscillatory phase and power on human motor function, like corticospinal output, is unknown. We addressed this gap in knowledge by delivering transcranial magnetic stimulation (TMS) to the human motor cortex during electroencephalography recordings in 20 healthy participants. Motor evoked potentials, a measure of corticospinal excitability, were categorized offline based on the mu (8-12 Hz) and beta (13-30 Hz) oscillatory phase and power at the time of TMS. Phase-dependency of corticospinal excitability was evaluated across a continuous range of power levels using trial-by-trial linear mixed-effects models. For mu, there was no effect of PHASE or POWER (P > 0.51), but a significant PHASE × POWER interaction (P = 0.002). The direction of phase-dependency reversed with changing mu power levels: corticospinal output was higher during mu troughs versus peaks when mu power was high while the opposite was true when mu power was low. A similar PHASE × POWER interaction was not present for beta oscillations (P > 0.11). We conclude that the interaction between sensorimotor oscillatory phase and power gates human corticospinal output to an extent unexplained by sensorimotor oscillatory phase or power alone.

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Paired-pulse transcranial magnetic stimulation (TMS) and peripheral stimulation combined with TMS can be used to study cortical interneuronal circuitry. By combining these procedures with concurrent transcranial alternating current stimulation (tACS), Guerra and colleagues recently showed that different cortical interneuronal populations are differentially modulated by the phase and frequency of tACS-imposed oscillations (Guerra A, Pogosyan A, Nowak M, Tan H, Ferreri F, Di Lazzaro V, Brown P. Cerebral Cortex 26: 3977-2990, 2016). This work suggests that different cortical interneuronal populations can be characterized by their phase and frequency dependency. Here we discuss how combining TMS and tACS can reveal the frequency at which cortical interneuronal populations oscillate, the neuronal origins of behaviorally relevant cortical oscillations, and how entraining cortical oscillations could potentially treat brain disorders.

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BACKGROUND: The ankle is an important component of the human kinetic chain, and deficits in ankle strength can negatively impact functional tasks such as balance and gait. While peak torque is influenced by joint angle and movement velocity, ankle strength is typically reported for a single angle or movement speed. To better identify deficits and track recovery of ankle strength after injury or surgical intervention, ankle strength across a range of movement velocities and joint angles in healthy adults is needed. Thus, the primary goals of this study were to generate a database of strength values and 3-dimensional strength surface models for plantarflexion (PF) and dorsiflexion (DF) ankle strength in healthy men and women. Secondary goals were to develop a means to estimate ankle strength percentiles as well as examine predictors of maximal ankle strength in healthy adults. METHODS: Using an isokinetic dynamometer, we tested PF and DF peak torques at five joint angles (-10° [DF], 0° [neutral], 10° [PF], 20° [PF] and 30° [PF]) and six velocities (0°/s, 30°/s, 60°/s, 90°/s, 120°/s and 180°/s) in 53 healthy adults. These data were used to generate 3D plots, or "strength surfaces", for males and females for each direction; surfaces were fit using a logistic equation. We also tested predictors of ankle strength, including height, weight, sex, and self-reported physical activity levels. RESULTS: Torque-velocity and torque-angle relationships at the ankle interact, indicating that these relationships are interdependent and best modeled using 3D surfaces. Sex was the strongest predictor of ankle strength over height, weight, and self-reported physical activity levels. 79 to 97 % of the variance in mean peak torque was explained by joint angle and movement velocity using logistic equations, for men and women and PF and DF directions separately. CONCLUSIONS: The 3D strength data and surface models provide a more comprehensive dataset of ankle strength in healthy adults than previously reported. These models may allow researchers and clinicians to quantify ankle strength deficits and track recovery in patient populations, using angle- and velocity-specific ankle strength values and/or strength percentiles from healthy adults.

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BACKGROUND: The theory of homeostatic metaplasticity has significant implications for human motor cortical plasticity and motor learning. Previous work has shown that the extent of recent effector use before exogenously-induced plasticity can affect the direction, magnitude and variability of aftereffects. However, the impact of recent effector use on motor learning and practice-dependent plasticity is not known. HYPOTHESIS: We hypothesized that reducing effector use for 8 hours via hand/wrist immobilization would facilitate practice-dependent changes in corticospinal excitability and TMS-evoked thumb movement kinematics, while also promoting 24-hour retention of a ballistic motor skill. METHODS: Subjects participated in a crossover study involving two conditions. During the immobilization condition, subjects wore a splint that restricted motion of the left hand and thumb for 8 hours. While wearing the splint, subjects were instructed to avoid using their left hand as much as possible. During the control condition, subjects did not wear a splint at any time nor were they instructed to avoid hand use. After either an 8 hour period of immobilization or normal hand use, we collected MEP and TMS-evoked thumb movement recruitment curves, and subjects practiced a ballistic motor skill involving rapid thumb extension. After motor practice, MEP and TMS-evoked thumb movement recruitment curves were re-tested. Retention of the motor skill was tested 30 minutes and 24 hours after motor practice. RESULTS: Reduced effector use did not impact pre-practice corticospinal excitability but did facilitate practice-dependent changes in corticospinal excitability, and this enhancement was specific to the trained muscle. In contrast, reducing effector use did not affect practice-dependent changes in TMS-evoked thumb movements nor did it promote acquisition or retention of the skill. Finally, we detected some associations between pre-practice excitability levels, plasticity effects and learning effects, but these did not reach our adjusted criterion for significance. CONCLUSION: Experimentally enhancing practice-dependent changes in corticospinal excitability is not sufficient to promote learning or memory of a ballistic motor skill.

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Caffeine is widely consumed throughout the world and appears to indirectly facilitate learning and memory through effects on attention and motivation. Animal work indicates that post-training caffeine administration augments inhibitory avoidance memory, spatial memory, and object memory. In humans, post-training caffeine administration enhances the ability to discern between familiar images and new, similar images. However, the effect of post-training caffeine administration on motor memory has not been examined. Therefore, we tested two groups of low caffeine consumers (average weekly consumption ≤500 mg) in a double-blind, placebo-controlled study involving acquisition of a continuous isometric visuomotor tracking skill. On Day 1, subjects completed 5 blocks (150 repetitions) of training on the continuous isometric visuomotor skill and subsequently ingested either 200 mg of caffeine or placebo. On day 2, subjects completed an additional 5 blocks of training. Day 1 mean performance and performance variability were both similar between groups, suggesting that both groups acquired the motor skill similarly. For mean performance on Day 2, patterns of re-learning, mean performance learning magnitudes, mean performance learning rates, and mean performance retention magnitudes were all similar between groups. For performance variability on Day 2, there was a small trend towards increased variability in the caffeine group during re-learning, but performance variability learning magnitudes and performance variability retention magnitudes did not differ between groups. Because motor skill acquisition can also be conceptualized as a reduction in performance variability, these results suggest that there may be a small negative effect of post-practice caffeine administration on memory of a newly-learned visuomotor skill. Overall, we found no evidence to suggest that post-training caffeine administration enhances 24-hour retention of a newly-learned continuous visuomotor skill, and these results support the notion that memory-enhancing effects of post-training caffeine ingestion may be task-specific.

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Motor adaptation in response to gradual vs. abrupt perturbation schedules may involve different neural mechanisms, potentially leading to different levels of motor memory. However, no study has investigated whether perturbation schedules alter memory of a locomotor adaptation across days. We measured adaptation and retention (memory) of altered interlimb symmetry during walking in two groups of participants over 2 days. On day 1, participants adapted to either a single, large perturbation (abrupt schedule) or a series of small perturbations that increased in size over time (gradual schedule). Retention was examined on day 2. On day 1, initial swing time and foot placement symmetry error sizes differed between groups but overall adaptation magnitudes were similar. On day 2, participants in both groups showed similar retention, readaptation, and aftereffect sizes, although there were some trends for improved memory in the abrupt group. These results conflict with previous data but are consistent with newer studies reporting no behavioral differences following adaptation using abrupt vs. gradual schedules. Although memory levels were very similar between groups, we cannot rule out the possibility that the neural mechanisms underlying this memory storage differ. Overall, it appears that adaptation of locomotor patterns via abrupt and gradual perturbation schedules produces similar expression of locomotor memories across days.

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Locomotor patterns are generally very consistent but also contain a high degree of adaptability. Motor adaptation is a short-term type of learning that utilizes this plasticity to alter locomotor behaviors quickly and transiently. In this study, we used a variation of an adaptation paradigm in order to test whether explicit information as well as the removal of the visual error signal after adaptation could improve retention of a newly learned walking pattern 24 h later. On two consecutive days of testing, participants walked on a treadmill while viewing a visual display that showed erroneous feedback of swing times for each leg. Participants were instructed to use this feedback to monitor and adjust swing times so they appeared symmetric within the display. This was achieved by producing a novel interlimb asymmetry between legs. For both legs, we measured adaptation magnitudes and rates and immediate and 24-h retention magnitudes. Participants showed similar adaptation on both days but a faster rate of readaptation on day 2. There was complete retention of adapted swing times on the increasing leg (i.e., no evidence of performance decay over 24 h). Overall, these findings suggest that the inclusion of explicit information and the removal of the visual error signal are effective in inducing full retention of adapted increases in swing time over a moderate (24 h) interval of time.

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