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Objective: Surface electromyography (sEMG) can sense the motor commands transmitted to the muscles. This work presents a deep learning method that can decode the electrophysiological activity of the forearm muscles into the movements of the human hand., Methods: We have recorded the kinematics and kinetics of the hand during a wide range of grasps and individual digit movements that cover 22 degrees of freedom of the hand at slow (0.5 Hz) and comfortable (1.5 Hz) movement speeds in 13 healthy participants. The input of the model consists of 320 non-invasive EMG sensors placed on the extrinsic hand muscles., Results: Our network achieves accurate continuous estimation of both kinematics and kinetics, surpassing the performance of comparable networks reported in the literature. By examining the latent space of the network, we find evidence that it mapped EMG activity into the anatomy of the hand at the individual digit level. In contrast to what is observed from the low-pass filtered EMG and linear decoding approaches, we found that the full-bandwidth EMG (monopolar unfiltered) signals during synergistic and individual digit movements contain distinct neural embeddings that encode each movement of the human hand. These manifolds consistently represent the anatomy of the hand and are generalized across participants. Moreover, we found a task-specific distribution of the embeddings without any presence of correlated activations during multi- and individual-digit tasks., Conclusion/significance: The proposed method could advance the control of assistive hand devices by providing a robust and intuitive interface between muscle signals and hand movements.

Introduction: Chronic refractory pain of various origin occurs in 30-45% of pain patients, and a considerable proportion remains resistant to pharmacological and behavioral therapies, requiring adjunctive neurostimulation therapies. Chronic pain is known to stimulate sympathetic outflow, yet the impact of burst motor cortex stimulation (burstMCS) on objectifiable autonomic cardiovascular parameters in chronic pain remains largely unknown., Methods: In three patients with chronic pain (2 facial pain/1 post-stroke pain), we compared pain intensity using a visual analog scale (VAS 1-10) and parameters of autonomic cardiovascular modulation at supine rest, during parasympathetic challenge with six cycles per minute of metronomic deep breathing, and during sympathetic challenge (active standing) at baseline and after 4 months of burstMCS compared to age-/gender-matched healthy controls., Results: While two out of three patients were responsive after 4 months of adjunctive burstMCS (defined as pain reduction of > 30%), no differences were found in any of the three patients regarding the R-R intervals of adjacent QRS complexes (RRI, 642 vs. 676 ms) and blood pressure (BP, 139/88 vs. 141/90 mmHg). Under resting conditions, parameters of parasympathetic tone [normalized units of high-frequency oscillations of RRI (RRI-HFnu power) 0.24 vs. 0.38, root-mean-square differences of successive RRI (RRI-RMSSD) 7.7 vs. 14.7 ms], total autonomic cardiac modulation [RRI total power 129.3 vs. 406.2 ms 2 , standard deviation of RRI (RRI-SD) 11.6 vs. 18.5 ms, coefficient of variation of RRI (RRI-CV) 1.9 vs. 3.7%], and baroreceptor reflex sensitivity (BRS, 1.9 vs. 2.3 ms/mmHg) increased, and parameters of sympathetic tone [normalized units of low-frequency oscillations of RRI (RRI-LFnu power) 0.76 vs. 0.62] and sympatho-vagal balance [ratio of RR-LF to RRI-HF power (RRI-LF/HF ratio) 3.4 vs. 1.9] decreased after 4 months of burstMCS. Low-frequency oscillations of systolic blood pressure (SBP-LF power), a parameter of sympathetic cardiovascular modulation, increased slightly (17.6 vs. 20.4 mmHg 2 ). During parasympathetic stimulation, the expiratory-inspiratory ratio (E/I ratio) increased slightly, while upon sympathetic stimulation, the ratio between the shortest RRI around the 15th heartbeat and the longest RRI around the 30th heartbeat after standing up (RRI 30/15 ratio) remained unchanged., Conclusion: Four months of adjunctive burstMCS was associated with an increase in parameters reflecting both total and parasympathetic autonomic modulation and baroreceptor reflex sensitivity. In contrast, sympathetic tone declined in our three patients, suggesting stimulation-associated improvement not only in subjectively perceived VAS pain scores, but also in objectifiable parameters of autonomic cardiovascular modulation., (© 2023. The Author(s).)

Mental fatigue (MF) does not only affect cognitive but also physical performance. This study aimed to explore the effects of MF on muscle endurance, rate of perceived exertion (RPE), and motor units' activity. Ten healthy males participated in a randomised crossover study. The subjects attended two identical experimental sessions separated by 3 days with the only difference of a cognitive task (incongruent Stroop task [ST]) and a control condition (watching a documentary). Perceived MF and motivation were measured for each session at baseline and after each cognitive task. Four contractions at 20% of maximal voluntary contraction (MVIC) were performed at baseline, after each cognitive and after muscle endurance task while measuring motor units by high-density surface electromyography. Muscle endurance until failure at 50% of MVIC was measured after each cognitive task and the RPE was measured right after failure. ST significantly increased MF (p = 0.001) reduced the motivation (p = 0.008) for the subsequent physical task and also impaired physical performance (p = 0.044). However, estimates of common synaptic inputs and motor unit discharge rates as well as RPE were not affected by MF (p > 0.11). In conclusion, MF impairs muscle endurance and motivation for the physical task but not the neural drive to the muscle at any frequency bands. Although it is physiologically possible for mentally fatigued subjects to generate an optimal neuromuscular function, the altered motivation seems to limit physical performance. Preliminarily, our results suggest that the corticospinal pathways are not affected by MF., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Despite available, advanced pharmacological and behavioral therapies, refractory chronic facial pain of different origins still poses a therapeutic challenge. In circumstances where there is insufficient responsiveness to pharmacological/behavioral therapies, deep brain stimulation should be considered as a potential effective treatment option. We performed an individual participant data (IPD) meta-analysis including searches on PubMed, Embase, and the Cochrane Library (2000-2022). The primary endpoint was the change in pain intensity (visual analogue scale; VAS) at a defined time-point of ≤3 months post-DBS. In addition, correlation and regression analyses were performed to identify predictive markers (age, duration of pain, frequency, amplitude, intensity, contact configuration, and the DBS target). A total of seven trials consisting of 54 screened patients met the inclusion criteria. DBS significantly reduced the pain levels after 3 months without being related to a specific DBS target, age, contact configuration, stimulation intensity, frequency, amplitude, or chronic pain duration. Adverse events were an infection or lead fracture (19%), stimulation-induced side effects (7%), and three deaths (unrelated to DBS-from cancer progression or a second stroke). Although comparable long-term data are lacking, the current published data indicate that DBS (thalamic and PVG/PAG) effectively suppresses facial pain in the short-term. However, the low-quality evidence, reporting bias, and placebo effects must be considered in future randomized-controlled DBS trials for facial pain.

The human hand possesses a large number of degrees of freedom. Hand dexterity is encoded by the discharge times of spinal motor units (MUs). Most of our knowledge on the neural control of movement is based on the discharge times of MUs during isometric contractions. Here we designed a noninvasive framework to study spinal motor neurons during dynamic hand movements with the aim to understand the neural control of MUs during sinusoidal hand digit flexion and extension at different rates of force development. The framework included 320 high-density surface EMG electrodes placed on the forearm muscles, with markerless 3D hand kinematics extracted with deep learning, and a realistic virtual hand that displayed the motor tasks. The movements included flexion and extension of individual hand digits at two different speeds (0.5 Hz and 1.5 Hz) for 40 seconds. We found on average 4.7±1.7 MUs across participants and tasks. Most MUs showed a biphasic pattern closely mirroring the flexion and extension kinematics. Indeed, a factor analysis method (non-negative matrix factorization) was able to learn the two components (flexion/extension) with high accuracy at the individual MU level ( R=0.87±0.12). Although most MUs were highly correlated with either flexion or extension movements, there was a smaller proportion of MUs that was not task-modulated and controlled by a different neural module (7.1% of all MUs with ). This work shows a noninvasive visually guided framework to study motor neurons controlling the movement of the hand in human participants during dynamic hand digit movements.