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The assessment of smoothness, range of motion (ROM), and head repositioning accuracy (HRA) has gained attention in identifying sensorimotor impairments. Uncertainty persists on the approach for acquiring reliable measures, including choice of smoothness metric, normalization factors, and the required number of measurements for reliable results. This study aimed to address this uncertainty. Thirty healthy participants were included in this single-session randomized cross-over study. The experiment consisted of two parts. One focused on the test–retest assessment of head ROM into right rotation to the end of range from a neutral position using a self-selected movement speed and the HRA when returning to the start-position. In the other part, participants repeated the previous tasks and performed head rotations at slower and faster speeds than their self-selected pace and to the beat of a metronome. All tasks were repeated ten times. For the test–retest, the inter-class-correlation (ICC) values for ROM were between 0.84–0.91, 0.20–0.31 for HRA, and 0.65–0.90 for jerk for 1–10 repetitions. Normalizing jerk through vmean and vpeak had similar variability and appeared equally valid for our data. However, normalizing by vmax ensures desirable properties in the smoothness metric. Lower variability was observed when standardizing movements using a metronome. Based on test–retest findings, three repetitions are recommended, as ICC values show marginal improvement beyond 2–3 repetitions, providing limited additional value.

Signals recorded from motor cortex—through an intracortical implant—can be linked in real-time to activation of forearm muscles to restore movement in a paralysed human. Part restoration of muscle response in quadriplegia This paper demonstrates that signals recorded from motor cortex — through an intracortical implant — can be linked in real-time to activation of forearm muscles in order to restore movement in a paralysed human. Motor cortex signals were decoded and used to control a neuromuscular electrical stimulation system contained in a sleeve wrapped around the patient's arm. The system provided isolated finger movements and the patient, a 24-year-old male who had sustained a spinal cord injury, could make six different volitional wrist and hand motions, enabling him to grasp, manipulate and release objects. Millions of people worldwide suffer from diseases that lead to paralysis through disruption of signal pathways between the brain and the muscles. Neuroprosthetic devices are designed to restore lost function and could be used to form an electronic ‘neural bypass’ to circumvent disconnected pathways in the nervous system. It has previously been shown that intracortically recorded signals can be decoded to extract information related to motion, allowing non-human primates and paralysed humans to control computers and robotic arms through imagined movements 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 . In non-human primates, these types of signal have also been used to drive activation of chemically paralysed arm muscles 12 , 13 . Here we show that intracortically recorded signals can be linked in real-time to muscle activation to restore movement in a paralysed human. We used a chronically implanted intracortical microelectrode array to record multiunit activity from the motor cortex in a study participant with quadriplegia from cervical spinal cord injury. We applied machine-learning algorithms to decode the neuronal activity and control activation of the participant’s forearm muscles through a custom-built high-resolution neuromuscular electrical stimulation system. The system provided isolated finger movements and the participant achieved continuous cortical control of six different wrist and hand motions. Furthermore, he was able to use the system to complete functional tasks relevant to daily living. Clinical assessment showed that, when using the system, his motor impairment improved from the fifth to the sixth cervical (C5–C6) to the seventh cervical to first thoracic (C7–T1) level unilaterally, conferring on him the critical abilities to grasp, manipulate, and release objects. This is the first demonstration to our knowledge of successful control of muscle activation using intracortically recorded signals in a paralysed human. These results have significant implications in advancing neuroprosthetic technology for people worldwide living with the effects of paralysis.

This study aimed to determine the effect of plyometric training (PT) when added to habitual gymnastic training (HT) on handspring vault (HV) performance variables. Twenty youth female competitive gymnasts (Age: 12.5 ± 1.67 y) volunteered to participate and were randomly assigned to two independent groups. The experimental plyometric training group (PTG) undertook a six-week plyometric program, involving two additional 45 min PT sessions a week, alongside their HT, while the control group (CG) performed regular HT only. Videography was used (120 Hz) in the sagittal plane to record both groups performing three HVs for both the baseline and post-intervention trials. Furthermore, participants completed a countermovement jump test (CMJ) to assess the effect of PT on functional power. Through the use of Quintic biomechanics software, significant improvements (P < 0.05) were found for the PTG for run-up velocity, take-off velocity, hurdle to board distance, board contact time, table contact time and post-flight time and CMJ height. However, there were no significant improvements on pre-flight time, shoulder angle or hip angle on the vault for the PTG. The CG demonstrated no improvement for all HV measures. A sport-specific PT intervention improved handspring vault performance measures and functional power when added to the habitual training of youth female gymnasts. The additional two hours plyometric training seemingly improved the power generating capacity of movement-specific musculature, which consequently improved aspects of vaulting performance. Future research is required to examine the whether the improvements are as a consequence of the additional volume of sprinting and jumping activities, as a result of the specific PT method or a combination of these factors.

Sounds can arise from the environment and also predictably from many of our own movements, such as vocalizing, walking, or playing music. The capacity to anticipate these movement-related (reafferent) sounds and distinguish them from environmental sounds is essential for normal hearing 1 , 2 , but the neural circuits that learn to anticipate the often arbitrary and changeable sounds that result from our movements remain largely unknown. Here we developed an acoustic virtual reality (aVR) system in which a mouse learned to associate a novel sound with its locomotor movements, allowing us to identify the neural circuit mechanisms that learn to suppress reafferent sounds and to probe the behavioural consequences of this predictable sensorimotor experience. We found that aVR experience gradually and selectively suppressed auditory cortical responses to the reafferent frequency, in part by strengthening motor cortical activation of auditory cortical inhibitory neurons that respond to the reafferent tone. This plasticity is behaviourally adaptive, as aVR-experienced mice showed an enhanced ability to detect non-reafferent tones during movement. Together, these findings describe a dynamic sensory filter that involves motor cortical inputs to the auditory cortex that can be shaped by experience to selectively suppress the predictable acoustic consequences of movement. Training of mice to associate a particular sound frequency with locomotion results in selective suppression of cortical responses to that frequency during movement, consistent with a motor-dependent form of auditory cortical plasticity.

This study focuses on the individual and joint contributions of two nonverbal channels (i.e., face and upper body) in avatar mediated-virtual environments. 140 dyads were randomly assigned to communicate with each other via platforms that differentially activated or deactivated facial and bodily nonverbal cues. The availability of facial expressions had a positive effect on interpersonal outcomes. More specifically, dyads that were able to see their partner’s facial movements mapped onto their avatars liked each other more, formed more accurate impressions about their partners, and described their interaction experiences more positively compared to those unable to see facial movements. However, the latter was only true when their partner’s bodily gestures were also available and not when only facial movements were available. Dyads showed greater nonverbal synchrony when they could see their partner’s bodily and facial movements. This study also employed machine learning to explore whether nonverbal cues could predict interpersonal attraction. These classifiers predicted high and low interpersonal attraction at an accuracy rate of 65%. These findings highlight the relative significance of facial cues compared to bodily cues on interpersonal outcomes in virtual environments and lend insight into the potential of automatically tracked nonverbal cues to predict interpersonal attitudes.