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OBJECTIVE: Brain state-dependent transcranial magnetic stimulation (TMS) requires real-time identification of cortical excitability states. Current state-of-the-art approaches deliver TMS during brain states shown to 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 subjects using power spectral features obtained from low-density electroencephalography (EEG) signals. METHODS: We analyzed a pre-existing publicly available dataset that delivered 600 single TMS pulses to the right M1 during EEG and electromyography (EMG) recordings. Personalized multivariate pattern classification was used to discriminate between brain states during which TMS evoked small and large motor-evoked potentials (MEPs). These brain states were labeled as low and high M1 excitability states, respectively. RESULTS: Personalized classifiers successfully discriminated between low and high M1 excitability states in every subject tested. MEPs elicited during classifier-predicted high excitability states were significantly larger than those elicited during classifier-predicted low excitability states in all subjects. Personalized classifiers trained using EEG features obtained immediately before each TMS pulse performed better than personalized classifiers trained using EEG features obtained from earlier time points. Personalized classifiers generalized weakly but significantly across subjects. CONCLUSION: Individual subjects exhibit unique brain activity patterns that correspond to low and high M1 excitability states. These patterns can be efficiently captured using power spectral features obtained from low-density EEG signals. Deploying individualized classifiers during brain state-dependent TMS may enable effective, fully personalized neuromodulation in the future.

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.

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.

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.

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.

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.

Voluntary movement requires motor commands to be released from motor cortex (M1) and transmitted to spinal motoneurons and effector muscles. M1 activity oscillates between brief excitatory and inhibitory states that correlate with single neuron spiking rates. Here, we asked if the motor commands needed to produce voluntary, self-paced finger movements are preferentially released from M1 during restricted phases of this ongoing sensorimotor oscillatory activity. 21 healthy adults performed a self-paced finger movement task while EEG and EMG signals were recorded. For each finger movement, we identified the individual sensorimotor mu (8-12 Hz) and beta (13-35 Hz) oscillatory phase at the estimated time of motor command release from M1 by subtracting individually-defined MEP latencies from EMG-determined movement onset times. We report that motor commands were preferentially released at ~120° along the beta cycle but were released uniformly across the mu cycle. These results suggest that motor commands are preferentially released from M1 near optimal peak phases of endogenous beta rhythms.

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.

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.

OBJECTIVETranscranial magnetic stimulation (TMS) is extensively used in basic and clinical neuroscience. Previous work has shown substantial residual variability in TMS effects even despite use of on-line visual feedback monitoring of coil position. Here, we aimed to evaluate if off-line denoising of variability induced by neuronavigated coil position and orientation deviations can enhance detection of TMS effects.METHODSRetrospective modeling was used to denoise the impact of common neuronavigated coil position and rotation deviations during TMS experimental sessions on motor evoked potentials (MEP) to single pulse TMS.RESULTSNeuronavigated coil deviations explained approximately 44% of total MEP amplitude variability. Offline denoising led to a 136.71% improvement in the signal to noise ratio (SNR) of corticospinal excitability measurements. CONCLUSIONS: Offline modeling enhanced detection of TMS effects by removing variability introduced by neuronavigated coil deviations.SIGNIFICANCEThis approach could allow more accurate determination of TMS effects in cognitive and interventional neuroscience.HIGHLIGHTSCoil deviations impact TMS effects despite use of on-line neuronavigation feedback.Offline denoising of coil deviation impacts on TMS effects significantly reduced variability at trial level.Offline denoising also significantly improved overall SNR of TMS effects.

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.

Introduction TMS-evoked EEG potentials (TEPs) can be used to probe connectivity and consciousness in healthy and diseased brains. A downside of this technique is that TMS causes neural artifacts, such as auditory and somatosensory-evoked responses, which may mask the true response to cortical stimulation, particularly at longer latencies. To avoid misinterpreting TEPs, methods for detecting neural artifact are required. One potentially helpful assumption is that neural artifacts have stereotyped topographies, while the true response to focal cortical stimulation will be spatially differentiated between stimulation conditions. Based on this assumption we used cosine similarity as a measure of response similarity and compared waveforms from real and sham stimulation at three scalp locations. Methods In 20 healthy volunteers, we delivered 150 active and 50 sham pulses to the right dorsolateral prefrontal cortex, left primary motor cortex, and left parietal cortex at 100% resting motor threshold. Sham stimulation was performed by placing a ∼2 cm spacer between the coil and scalp. TEPs were recorded with a 32-channel TMS-compatible EEG system and preprocessed using Fieldtrip® software. The derivatives of the preprocessed TEPs were calculated and then binarized for every time point at each channel. Cosine similarity at each time-point was calculated between conditions using all channels. Results The cosine similarity metric showed that for each comparison (e.g. motor vs. parietal, real vs. sham, etc.), the early ( ∼ 100 ms across all comparisons, coinciding with the N100 component. These quantitative results closely match qualitative assessment of the whole-brain topographies, lending credibility to our use of the cosine similarity technique as a measure of stereotypy. Conclusion Our results indicate that while early TEP components reflect site-specific responses to direct cortical stimulation, later responses become progressively stereotyped across stimulation sites, suggesting that these late components are neural artifact. Due to the exponential-like decay in differentiation seen in each comparison, we propose that only TEP components occurring earlier than 50–70 ms are safely interpretable as responses to cortical stimulation. Auditory and somatosensory stimulation controls and/or further refinements in signal processing may be necessary to extract true responses to cortical stimulation from later components.

Introduction Neuronal networks exhibit oscillatory activity at various frequencies, which are thought to coordinate local neuronal processing and cortico-cortical communication through alternating periods of excitation and inhibition. In the motor cortex, single neuron spiking rates (Haegens et al., 2011) and neuronal population activity (Miller et al., 2012) are highest at mu and beta oscillatory troughs (maximum surface negativity) and lowest at peaks (maximum surface positivity). This relationship is strongest when subjects are at rest, when power in these frequency bands is known to be high (Pfurtscheller and Da Silva, 1999). Yet, how sensorimotor oscillatory phase and power interact to influence corticospinal excitability in intact humans is not known. Here, we explored this issue in an open-loop TMS design (i.e., timing of TMS delivery was determined by the passage of time, but not brain oscillatory state), focusing on mu and beta oscillatory activity due to their relevance to human motor control. Methods We collected 600 MEPs for each of 20 healthy subjects using single-pulse TMS (Magstim, UK) at 120% or resting motor threshold delivered to the scalp hotspot for the right first dorsal interosseous during simultaneous EEG recordings (BrainProducts GmbH, Germany). Subsequently, data from the C4 sensor were Hjorth-transformed (central: C4; surround: FC2, FC6, CP2, CP6) and the instantaneous oscillatory phase and pre-stimulus power within 150 ms of the TMS pulse in the mu and beta bands was calculated. Peak and trough trials were defined as trials where TMS occurred at maximum surface positivity (45–135°) and maximum surface negativity (225–315°) of the oscillatory cycle, respectively. The influence of sensorimotor oscillatory phase (categorical variable, peak vs. trough), power (continuous variable) and their interaction on MEP amplitudes was statistically evaluated using separate trial-by-trial linear mixed-effects models for each frequency band. Results For mu activity, there was a significant PHASE x POWER interaction (p = 0.001), evident as a significantly more positive relationship between pre-stimulus mu power and MEP amplitudes for troughs than peaks (mu peak slope = −0.001 [95% CI = −0.029 to 0.027]; mu trough slope = 0.063 [95% CI = 0.034 to 0.090]). Notably, when mu power was high, MEPs were larger at troughs versus peaks, while the opposite was true when mu power was low. For beta activity, there was a significant main effect of POWER (p = 0.007), revealing a positive relationship between pre-stimulus beta power and MEP amplitudes independent of beta phase. Conclusion Corticospinal excitability was highest at mu troughs during periods of high mu power, and during periods of high beta power regardless of beta phase. We conclude that sensorimotor oscillatory power and phase interact to shape human motor function, supporting the view that sensorimotor rhythms dynamically gate corticospinal excitability.

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.

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.