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Background Unusual behavioral reactions to sensory stimuli are frequently reported in individuals on the autism spectrum (AS). Despite the early emergence of sensory features ( Methods Here, we used electroencephalography (EEG) to investigate tactile cortical processing in young children aged 3–6 years with autism and in neurotypical (NT) children. Scalp EEG was recorded from 33 children with autism, including those with low cognitive and/or verbal abilities, and 45 age- and sex-matched NT children during passive tactile fingertip stimulation. We compared properties of early and later somatosensory-evoked potentials (SEPs) and their adaptation with repetitive stimulation between autistic and NT children and assessed whether these neural measures are linked to “real-world” parent-reported tactile reactivity. Results As expected, we found elevated tactile reactivity in children on the autism spectrum. Our findings indicated no differences in amplitude or latency of early and mid-latency somatosensory-evoked potentials (P50, N80, P100), nor adaptation between autistic and NT children. However, latency of later processing of tactile information (N140) was shorter in young children with autism compared to NT children, suggesting faster processing speed in young autistic children. Further, correlational analyses and exploratory analyses using tactile reactivity as a grouping variable found that enhanced early neural responses were associated with greater tactile reactivity in autism. Limitations The relatively small sample size and the inclusion of a broad range of autistic children (e.g., with low cognitive and/or verbal abilities) may have limited our power to detect subtle group differences and associations. Hence, replications are needed to verify these results. Conclusions Our findings suggest that electrophysiological somatosensory cortex processing measures may be indices of “real-world” tactile reactivity in early childhood autism. Together, these findings advance our understanding of the neurophysiological mechanisms underlying tactile reactivity in early childhood autism and, in the clinical context, may have therapeutic implications.

Movie-watching is becoming a popular acquisition method to increase compliance and enable neuroimaging data collection in challenging populations such as children, with potential to facilitate studying the somatosensory system. However, relatively little is known about the possible crossmodal (audiovisual) influence of movies on cortical somatosensory processing. In this study, we examined the impact of dynamic audiovisual movies on concurrent cortical somatosensory processing using electroencephalography (EEG). Forty healthy young adults (18-25 years) received passive tactile fingertip stimulation while watching an "entertaining" movie and a novel "low-demand" movie called 'Inscapes' compared to eyes-open rest. Watching a movie did not modulate properties of early or late somatosensory-evoked potentials (SEPs). Similarly, no crossmodal influence on somatosensory adaptation, denoted by a reduction in SEP amplitude with repetitive tactile stimulation, was found. The prominent oscillatory responses in the alpha and beta frequency bands following tactile stimulation differed as a function of viewing condition, with stronger alpha/beta event-related desynchronization (ERD) during movie-watching compared to rest. These findings highlight that movie-watching is a valid acquisition method during which SEPs can be measured in basic research and clinical studies, but that the attentional demands of movies need to be taken into account when performing oscillatory analyses.

Recovery of skilled movement after stroke is assumed to depend on motor learning. However, the capacity for motor learning and factors that influence motor learning after stroke have received little attention. In this study, we first compared motor skill acquisition and retention between well-recovered stroke patients and age- and performance-matched healthy controls. We then tested whether beta oscillations (15–30 Hz) from sensorimotor cortices contribute to predicting training-related motor performance. Eighteen well-recovered chronic stroke survivors (mean age 64 ± 8 years, range: 50–74 years) and 20 age- and sex-matched healthy controls were trained on a continuous tracking task and subsequently retested after initial training (45–60 min and 24 h later). Scalp electroencephalography was recorded during the performance of a simple motor task before each training and retest session. Stroke patients demonstrated capacity for motor skill learning, but it was diminished compared to age- and performance-matched healthy controls. Furthermore, although the properties of beta oscillations prior to training were comparable between stroke patients and healthy controls, stroke patients did show less change in beta measures with motor learning. Lastly, although beta oscillations did not help to predict motor performance immediately after training, contralateral (ipsilesional) sensorimotor cortex post-movement beta rebound measured after training helped predict future motor performance, 24 h after training. This finding suggests that neurophysiological measures such as beta oscillations can help predict response to motor training in chronic stroke patients and may offer novel targets for therapeutic interventions., Espenhahn et al. report that motor skill learning ability is preserved after stroke, but diminished compared to healthy controls. Movement-related beta dynamics after training helped predict future motor performance, suggesting that beta oscillations may have value as markers of motor system (dys)function after stroke., Graphical Abstract Graphical Abstract

Recovery of skilled movement after stroke is assumed to depend on motor learning. However, the capacity for motor learning and factors that influence motor learning after stroke have received little attention. In this study we firstly compared motor skill acquisition and retention between well-recovered stroke patients and age- and performance-matched healthy controls. We then tested whether beta oscillations (15–30Hz) from sensorimotor cortices contribute to predicting training-related motor performance.Eighteen well-recovered chronic stroke survivors (mean age 64±8 years, range 50–74 years) and twenty age- and sex-matched healthy controls were trained on a continuous tracking task and subsequently retested after initial training (45–60 min and 24 hours later). Scalp EEG was recorded during the performance of a simple motor task before each training and retest session. Stroke patients demonstrated capacity for motor skill learning, but it was diminished compared to age- and performance-matched healthy controls. Further, although the properties of beta oscillations prior to training were comparable between stroke patients and healthy controls, stroke patients did show less change in beta measures with motor learning. Lastly, although beta oscillations did not help to predict motor performance immediately after training, contralateral (ipsilesional) sensorimotor cortex post-movement beta rebound (PMBR) measured after training helped predict future motor performance, 24 hours after training. This finding suggests that neurophysiological measures such as beta oscillations can help predict response to motor training in chronic stroke patients and may offer novel targets for therapeutic interventions.

© 2019 The Authors People vary in their capacity to learn and retain new motor skills. Although the relationship between neuronal oscillations in the beta frequency range (15–30 Hz) and motor behaviour is well established, the electrophysiological mechanisms underlying individual differences in motor learning are incompletely understood. Here, we investigated the degree to which measures of resting and movement-related beta power from sensorimotor cortex account for inter-individual differences in motor learning behaviour in the young and elderly. Twenty young (18–30 years) and twenty elderly (62–77 years) healthy adults were trained on a novel wrist flexion/extension tracking task and subsequently retested at two different time points (45–60 min and 24 h after initial training). Scalp EEG was recorded during a separate simple motor task before each training and retest session. Although short-term motor learning was comparable between young and elderly individuals, there was considerable variability within groups with subsequent analysis aiming to find the predictors of this variability. As expected, performance during the training phase was the best predictor of performance at later time points. However, regression analysis revealed that movement-related beta activity significantly explained additional variance in individual performance levels 45–60 min, but not 24 h after initial training. In the context of disease, these findings suggest that measurements of beta-band activity may offer novel targets for therapeutic interventions designed to promote rehabilitative outcomes.

Oscillatory activity in the beta frequency range (15–30 Hz) recorded from human sensorimotor cortex is of increasing interest as a putative biomarker of motor system function and dysfunction. Despite its increasing use in basic and clinical research, surprisingly little is known about the test-retest reliability of spectral power and peak frequency measures of beta oscillatory signals from sensorimotor cortex. Establishing that these beta measures are stable over time in healthy populations is a necessary precursor to their use in the clinic. Here, we used scalp electroencephalography (EEG) to evaluate intra-individual reliability of beta-band oscillations over six sessions, focusing on changes in beta activity during movement (Movement-Related Beta Desynchronization, MRBD) and after movement termination (Post-Movement Beta Rebound, PMBR). Subjects performed visually-cued unimanual wrist flexion and extension. We assessed Intraclass Correlation Coefficients (ICC) and between-session correlations for spectral power and peak frequency measures of movement-related and resting beta activity. Movement-related and resting beta power from both sensorimotor cortices was highly reliable across sessions. Resting beta power yielded highest reliability (average ICC=0.903), followed by MRBD (average ICC=0.886) and PMBR (average ICC=0.663). Notably, peak frequency measures yielded lower ICC values compared to the assessment of spectral power, particularly for movement-related beta activity (ICC=0.386–0.402). Our data highlight that power measures of movement-related beta oscillations are highly reliable, while corresponding peak frequency measures show greater intra-individual variability across sessions. Importantly, our finding that beta power estimates show high intra-individual reliability over time serves to validate the notion that these measures reflect meaningful individual differences that can be utilised in basic research and clinical studies., Highlights • Movement-related beta-band activity shows high test-retest reliability. • Spectral power measures are more reliable than the corresponding peak frequencies. • Peak frequency measures at rest display higher reliability than during movement

Much of our knowledge on the physiological mechanisms of transcranial magnetic stimulation (TMS) stems from studies which targeted the human motor cortex. However, it is still unclear which part of the motor cortex is predominantly affected by TMS. Considering that the motor cortex consists of functionally and histologically distinct subareas, this also renders the hypotheses on the physiological TMS effects uncertain. We use the finite element method (FEM) and magnetic resonance image-based individual head models to get realistic estimates of the electric field induced by TMS. The field changes in different subparts of the motor cortex are compared with electrophysiological threshold changes of 2 hand muscles when systematically varying the coil orientation in measurements. We demonstrate that TMS stimulates the region around the gyral crown and that the maximal electric field strength in this region is significantly related to the electrophysiological response. Our study is one of the most extensive comparisons between FEM-based field calculations and physiological TMS effects so far, being based on data for 2 hand muscles in 9 subjects. The results help to improve our understanding of the basic mechanisms of TMS. They also pave the way for a systematic exploration of realistic field estimates for dosage control in TMS.