Transcranial magnetic stimulation combined with endogenous human hippocampal and motor cortical activity enhances memory.

The hippocampus is a fundamental cortical structure in the memory process of encoding, retaining, and recalling information. Transcranial Magnetic Stimulation (TMS) following a Paired Associative Stimulation (PAS) enhances nervous system excitability and promotes cortical plasticity mechanisms by synchronizing two stimuli in the same neural pathway. However, PAS has not been shown to improve memorization capacity yet. Here, we present an innovative protocol stemming from the PAS paradigm, which combines single-pulse TMS to the hippocampus with endogenous hippocampal activity during a working memory (WM) task. 96 volunteers were randomized across one experimental group and three parallel groups (motor cortex stimulation, sham stimulation, and no stimulation) in a single session. This combined-stimuli configuration resulted in an increased memorization capacity in the WM task, which was dependent on the stimulated brain location and subjects' basal memory performance. These results are potentially significant for clinical research on memory dysfunction and its related neurological disorders. Future research on paired associative or combined stimulation is required to unveil stimulation-derived neural mechanisms that enhance the ability to memorize.

Automatic Detection of Magnetic Disturbances in Magnetic Inertial Measurement Unit Sensors Based on Recurrent Neural Networks.

This paper proposes a new methodology for the automatic detection of magnetic disturbances from magnetic inertial measurement unit (MIMU) sensors based on deep learning. The proposed approach considers magnetometer data as input to a long short-term memory (LSTM) neural network and obtains a labeled time series output with the posterior probabilities of magnetic disturbance. We trained our algorithm on a data set that reproduces a wide range of magnetic perturbations and MIMU motions in a repeatable and reproducible way. The model was trained and tested using 15 folds, which considered independence in sensor, disturbance direction, and signal type. On average, the network can adequately detect the disturbances in 98% of the cases, which represents a significant improvement over current threshold-based detection algorithms.

Components of hostility and discrimination in cardiology.

The Gender-Diverse (TGD) population in Mexico faces significant health challenges, such as limited access to healthcare and a prevalence of adverse conditions. Cardiology is crucial for this population due to a high prevalence of risk factors and cardiovascular diseases. Despite a lack of precise data, it is estimated that 0.5-1.5% of the population identifies as TGD. They encounter sociocultural challenges, including discrimination and stigma, contributing to health issues and a lack of treatment access. Cardiovascular risk factors, hormone self-administration, and barriers to healthcare access are prominent concerns. Opportunities for improvement involve strengthening medical education, implementing inclusive policies, and promoting research and data collection. The development of specific clinical guidelines and inclusive health programs is suggested. The theory of minority stress emphasizes addressing chronic psychosocial stressors and recognizing the influence of stress factors on health. Integrating healthcare services for all populations in cardiology and fostering resilience are key strategies. In summary, a comprehensive approach is needed to reduce disparities in cardiac care and enhance the health of all populations in Mexico.

La población de diferentes géneros en México se enfrenta a desafíos significativos en salud, cómo el acceso limitado a la atención médica y la prevalencia de malas condiciones. La cardiología es crucial para esta población debido a la alta prevalencia de factores de riesgo y enfermedades cardiovasculares. A pesar de la falta de datos precisos, se estima que el 0.5-1.5% de la población se identifica como transgénero. Enfrentan desafíos socioculturales, como discriminación y estigmatización que contribuyen a problemas de salud y falta de acceso a tratamientos. Los factores de riesgo cardiovascular, la autoadministración de hormonas, y las barreras en el acceso a la atención médica son preocupaciones destacadas. Las oportunidades para mejorar incluyen fortalecer la educación médica, implementar políticas inclusivas, y promover la investigación y recopilación de datos. Se sugiere el desarrollo de guías clínicas específicas y programas de salud inclusivos. La teoría de la minorización destaca la importancia de abordar tensiones psicosociales crónicas y reconocer la influencia de factores estresantes en la salud. Integrar servicios de salud para todas las poblaciones en cardiología y fomentar la resiliencia son estrategias clave. En resumen, se necesita un enfoque integral para reducir las disparidades en la atención cardíaca y mejorar la salud de todas las poblaciones en México.

The complex landscape of TMS devices: A brief overview.

The increasing application of TMS in research and therapy has spawned an ever-growing number of commercial and non-commercial TMS devices and technology development. New CE-marked devices appear at a rate of approximately one every two years, with new FDA-approved application of TMS occurring at a similar rate. With the resulting complex landscape of TMS devices and their application, accessible information about the technological characteristics of the TMS devices, such as the type of their circuitry, their pulse characteristics, or permitted protocols would be beneficial. We here present an overview and open access database summarizing key features and applications of available commercial and non-commercial TMS devices (http://www.tmsbase.info). This may guide comparison and decision making about the use of these devices. A bibliometric analysis was performed by identifying commercial and non-commercial TMS devices from which a comprehensive database was created summarizing their publicly available characteristics, both from a technical and clinical point of view. In this document, we introduce both the commercial devices and prototypes found in the literature. The technical specifications that unify these devices are briefly analysed in two separate tables: power electronics, waveform, protocols, and coil types. In the prototype TMS systems, the proposed innovations are focused on improving the treatment regarding the patient: noise cancellation, controllable parameters, and multiple stimulation. This analysis shows that the landscape of TMS is becoming increasingly fragmented, with new devices appearing ever more frequently. The review provided here can support development of benchmarking frameworks and comparison between TMS systems, inform the choice of TMS platforms for specific research and therapeutic applications, and guide future technology development for neuromodulation devices. This standardisation strategy will allow a better end-user choice, with an impact on the TMS manufacturing industry and a homogenisation of patient samples in multi-centre clinical studies. As an open access repository, we envisage the database to grow along with the dynamic development of TMS devices and applications through community-lead curation.

Muscle synergies analysis shows altered neural strategies in women with patellofemoral pain during walking.

Several studies suggest that the central nervous system coordinates muscle activation by modulating neural commands directed to groups of muscles combined to form muscle synergies. Individuals with patellofemoral pain (PFP) move differently from asymptomatic individuals. Understanding the neural strategies involved in the execution of tasks such as walking can help comprehend how the movement is planned and better understand this clinical condition. The objective of this study was to compare muscle synergies between women with and without PFP during walking. Eleven women with PFP and thirteen asymptomatic women were assessed using three-dimensional kinematics and electromyography (EMG) while walking at self-selected speed. Kinematics of the trunk, pelvis and lower limbs were analyzed through the Movement Deviation Profile. Muscle synergies were extracted from the EMG signals of eight lower limb muscles collected throughout the whole gait cycle. Kinematic differences between the two groups (p<0.001, z-score = 3.06) were more evident during loading response, terminal stance, and pre-swing. PFP group presented a lower number of muscle synergies (p = 0.037), and greater variability accounted for (VAFtotal) when using 3 (p = 0.017), 4 (p = 0.004), and 5 (p = 0.012) synergies to reconstruct all EMG signals. The PFP group also presented higher VAFmuscle for rectus femoris (p = 0.048) and gastrocnemius medialis (p = 0.019) when considering 4 synergies. Our results suggest that women with PFP show lower motor complexity and deficit in muscle coordination to execute gait, indicating that gait in PFP is the result of different neural commands compared to asymptomatic women.

Haptic Human-Human Interaction During an Ankle Tracking Task: Effects of Virtual Connection Stiffness.

While treating sensorimotor impairments, a therapist may provide physical assistance by guiding their patient's limb to teach a desired movement. In this scenario, a key aspect is the compliance of the interaction, as the therapist can provide subtle cues or impose a movement as demonstration. One approach to studying these interactions involves haptically connecting two individuals through robotic interfaces. Upper-limb studies have shown that pairs of connected individuals estimate one another's goals during tracking tasks by exchanging haptic information, resulting in improved performance dependent on the ability of one's partner and the stiffness of the virtual connection. In this study, our goal was to investigate whether these findings generalize to the lower limb during an ankle tracking task. Pairs of healthy participants (i.e., dyads) independently tracked target trajectories with and without connections rendered between two ankle robots. We tested the effects of connection stiffness as well as visual noise to manipulate the correlation of tracking errors between partners. In our analysis, we compared changes in task performance across conditions while tracking with and without the connection. We found that tracking improvements while connected increased with connection stiffness, favoring the worse partner in the dyad during hard connections. We modeled the interaction as three springs in series, considering the stiffness of the connection and each partners' ankle, to show that improvements were likely due to a cancellation of random tracking errors between partners. These results suggest a simplified mechanism of improvements compared to what has been reported during upper-limb dyadic tracking.

Can we manipulate brain connectivity? A systematic review of cortico-cortical paired associative stimulation effects.

OBJECTIVE: Cortico-cortical paired associative stimulation (ccPAS) is a form of dual-site transcranial magnetic stimulation (TMS) entailing a series of single-TMS pulses paired at specific interstimulus intervals (ISI) delivered to distant cortical areas. The goal of this article is to systematically review its efficacy in inducing plasticity in humans focusing on stimulation parameters and hypotheses of underlying neurophysiology. METHODS: A systematic review of the literature from 2009-2023 was undertaken to identify all articles utilizing ccPAS to study brain plasticity and connectivity. Six electronic databases were searched and included. RESULTS: 32 studies were identified. The studies targeted connections within the same hemisphere or between hemispheres. 28 ccPAS studies were in healthy participants, 1 study in schizophrenia, and 1 in Alzheimer's disease (AD) patients. 2 additional studies used cortico-cortical repetitive paired associative stimulation (cc-rPAS) in generalized anxiety disorder (GAD) patients. Outcome measures include electromyography (EMG), behavioral measures, electroencephalography (EEG), and functional magnetic resonance imaging (fMRI). ccPAS seems to be able to modulate brain connectivity depending on the ISI. CONCLUSIONS: ccPAS can be used to modulate corticospinal excitability, brain activity, and behavior. Although the stimulation parameters used across studies reviewed in this paper are varied, ccPAS is a promising approach for basic research and potential clinical applications. SIGNIFICANCE: Recent advances in neuroscience have caused a shift of interest from the study of single areas to a more complex approach focusing on networks of areas that orchestrate brain activity. Consequently, the TMS community is also witnessing a change, with a growing interest in targeting multiple brain areas rather than a single locus, as evidenced by an increasing number of papers using ccPAS. In light of this new enthusiasm for brain connectivity, this review summarizes existing literature and stimulation parameters that have proven effective in changing electrophysiological, behavioral, or neuroimaging-derived measures.

Two motor neuron synergies, invariant across ankle joint angles, activate the triceps surae during plantarflexion.

Recent studies have suggested that the nervous system generates movements by controlling groups of motor neurons (synergies) that do not always align with muscle anatomy. In this study, we determined whether these synergies are robust across tasks with different mechanical constraints. We identified motor neuron synergies using principal component analysis (PCA) and cross-correlations between smoothed discharge rates of motor neurons. In part 1, we used simulations to validate these methods. The results suggested that PCA can accurately identify the number of common inputs and their distribution across active motor neurons. Moreover, the results confirmed that cross-correlation can separate pairs of motor neurons that receive common inputs from those that do not receive common inputs. In part 2, 16 individuals performed plantarflexion at three ankle angles while we recorded EMG signals from the gastrocnemius lateralis (GL) and medialis (GM) and the soleus (SOL) with grids of surface electrodes. The PCA revealed two motor neuron synergies. These motor neuron synergies were relatively stable, with no significant differences in the distribution of motor neuron weights across ankle angles (P = 0.62). When the cross-correlation was calculated for pairs of motor units tracked across ankle angles, we observed that only 13.0% of pairs of motor units from GL and GM exhibited significant correlations of their smoothed discharge rates across angles, confirming the low level of common inputs between these muscles. Overall, these results highlight the modularity of movement control at the motor neuron level, suggesting a sensible reduction of computational resources for movement control. KEY POINTS: The CNS might generate movements by activating groups of motor neurons (synergies) with common inputs. We show here that two main sources of common inputs drive the motor neurons innervating the triceps surae muscles during isometric ankle plantarflexions. We report that the distribution of these common inputs is globally invariant despite changing the mechanical constraints of the tasks, i.e. the ankle angle. These results suggest the functional relevance of the modular organization of the CNS to control movements.

Cardiometabolic risk factors and antithrombotic treatment in a Mexican population with atrial fibrillation and heart failure with reduced ejection fraction.

INTRODUCTION: Heart failure in patients with non-valvular atrial fibrillation (NVAF) is two to three times more common than in individuals without NVAF. OBJECTIVE: To identify cardiometabolic risk factors (CMRF) and antithrombotic treatment in patients with NVAF and heart failure with reduced ejection fraction (HFrEF), and to determine if there were differences according to gender. METHODS: CMRF, pro-thrombotic risk, bleeding risk, and antithrombotic therapy were globally analyzed and according to gender. RESULTS: Out of 1,423 patients with NVAF, 336 had HFrEF. On average, females were older than males. There was no difference between genders with regard to the type of NVAF or direct oral anticoagulants use. Hypertension was more common in women. History of transient ischemic attack was reported in 3.6% of the patients and cerebrovascular event in 10%, without differences in terms of gender. The percentage of men with elevated embolic risk was higher, but without antithrombotic treatment, in comparison with women. CONCLUSIONS: Significant differences were found according to gender in patients with NVAF and HFrEF, both in CMRF and some comorbidities, as well as in antithrombotic treatment according to embolic and bleeding risk.

INTRODUCCIÓN: La insuficiencia cardiaca en pacientes con fibrilación auricular no valvular (FANV) es de dos a tres veces más frecuente que en individuos sin FANV. OBJETIVO: Identificar los factores de riesgo cardiometabólico (FRCM) y el tratamiento antitrombótico de pacientes con FANV e insuficiencia cardiaca con fracción de expulsión reducida (IC-FEr), y determinar si existen diferencias conforme al sexo. MÉTODOS: En forma global y de acuerdo con el sexo se analizaron FRCM, riesgo protrombótico, riesgo de sangrado y terapia antitrombótica. RESULTADOS: De 1423 pacientes con FANV, 336 tuvieron IC-FEr. Las mujeres promediaron mayor edad que los hombres. No hubo diferencia entre los sexos respecto al tipo de FANV o uso de anticoagulantes orales directos. La hipertensión arterial sistémica fue más frecuente en mujeres. Un 3.6 % de los pacientes reportó antecedente de ataque isquémico transitorio y 10 % de evento vascular cerebral, sin diferencias en cuanto al sexo. El porcentaje de hombres con riesgo embólico elevado fue mayor, pero sin tratamiento antitrombótico, en comparación con las mujeres. CONCLUSIONES: Se encontraron diferencias significativas de acuerdo con el sexo en pacientes con FANV e IC-FEr, tanto en FRCM y algunas comorbilidades, como en el tratamiento antitrombótico de acuerdo con el riesgo embólico y de sangrado.

Factores de riesgo cardiometabólico y tratamiento antitrombótico en población mexicana con fibrilación auricular e insuficiencia cardiaca con fracción de expulsión reducida / Cardiometabolic risk factors and antithrombotic treatment in a Mexican population with atrial fibrillation and heart failure with reduced ejection fraction

Resumen Introducción: La insuficiencia cardiaca en pacientes con fibrilación auricular no valvular (FANV) es de dos a tres veces más frecuente que en individuos sin FANV. Objetivo: Identificar los factores de riesgo cardiometabólico (FRCM) y el tratamiento antitrombótico de pacientes con FANV e insuficiencia cardiaca con fracción de expulsión reducida (IC-FEr), y determinar si existen diferencias conforme al sexo. Métodos: En forma global y de acuerdo con el sexo se analizaron FRCM, riesgo protrombótico, riesgo de sangrado y terapia antitrombótica. Resultados: De 1423 pacientes con FANV, 336 tuvieron IC-FEr. Las mujeres promediaron mayor edad que los hombres. No hubo diferencia entre los sexos respecto al tipo de FANV o uso de anticoagulantes orales directos. La hipertensión arterial sistémica fue más frecuente en mujeres. Un 3.6 % de los pacientes reportó antecedente de ataque isquémico transitorio y 10 % de evento vascular cerebral, sin diferencias en cuanto al sexo. El porcentaje de hombres con riesgo embólico elevado fue mayor, pero sin tratamiento antitrombótico, en comparación con las mujeres. Conclusiones: Se encontraron diferencias significativas de acuerdo con el sexo en pacientes con FANV e IC-FEr, tanto en FRCM y algunas comorbilidades, como en el tratamiento antitrombótico de acuerdo con el riesgo embólico y de sangrado.

Abstract Introduction: Heart failure in patients with non-valvular atrial fibrillation (NVAF) is two to three times more common than in individuals without NVAF. Objective: To identify cardiometabolic risk factors (CMRF) and antithrombotic treatment in patients with NVAF and heart failure with reduced ejection fraction (HFrEF), and to determine if there were differences according to gender. Methods: CMRF, pro-thrombotic risk, bleeding risk, and antithrombotic therapy were globally analyzed and according to gender. Results: Out of 1,423 patients with NVAF, 336 had HFrEF. On average, females were older than males. There was no difference between genders with regard to the type of NVAF or direct oral anticoagulants use. Hypertension was more common in women. History of transient ischemic attack was reported in 3.6% of the patients and cerebrovascular event in 10%, without differences in terms of gender. The percentage of men with elevated embolic risk was higher, but without antithrombotic treatment, in comparison with women. Conclusions: Significant differences were found according to gender in patients with NVAF and HFrEF, both in CMRF and some comorbidities, as well as in antithrombotic treatment according to embolic and bleeding risk.

Classification of Kinematic and Electromyographic Signals Associated with Pathological Tremor Using Machine and Deep Learning.

Peripheral Electrical Stimulation (PES) of afferent pathways has received increased interest as a solution to reduce pathological tremors with minimal side effects. Closed-loop PES systems might present some advantages in reducing tremors, but further developments are required in order to reliably detect pathological tremors to accurately enable the stimulation only if a tremor is present. This study explores different machine learning (K-Nearest Neighbors, Random Forest and Support Vector Machines) and deep learning (Long Short-Term Memory neural networks) models in order to provide a binary (Tremor; No Tremor) classification of kinematic (angle displacement) and electromyography (EMG) signals recorded from patients diagnosed with essential tremors and healthy subjects. Three types of signal sequences without any feature extraction were used as inputs for the classifiers: kinematics (wrist flexion-extension angle), raw EMG and EMG envelopes from wrist flexor and extensor muscles. All the models showed high classification scores (Tremor vs. No Tremor) for the different input data modalities, ranging from 0.8 to 0.99 for the f1 score. The LSTM models achieved 0.98 f1 scores for the classification of raw EMG signals, showing high potential to detect tremors without any processed features or preliminary information. These models may be explored in real-time closed-loop PES strategies to detect tremors and enable stimulation with minimal signal processing steps.

Effect of Dyadic Haptic Collaboration on Ankle Motor Learning and Task Performance.

Optimizing skill acquisition during novel motor tasks and regaining lost motor functions have been the interest of many researchers over the past few decades. One approach shown to accelerate motor learning involves haptically coupling two individuals through robotic interfaces. Studies have shown that an individual's solo performance during upper-limb tracking tasks may improve after haptically-coupled training with a partner. In this study, our goal was to investigate whether these findings can be translated to lower-limb motor tasks, more specifically, during an ankle position tracking task. Using one-degree-of-freedom ankle movements, pairs of participants (i.e., dyads) tracked target trajectories independently. Participants alternated between tracking trials with and without haptic coupling, achieved by rendering a virtual spring between two ankle rehabilitation robots. In our analysis, we compared changes in task performance across trials while training with and without haptic coupling. The tracking performance of both individuals (i.e., dyadic task performance) improved during haptic coupling, which was likely due to averaging of random errors of the dyadic pair during tracking. However, we found that dyadic haptic coupling did not lead to faster individual learning for the tracking task. These results suggest that haptic coupling between unimpaired individuals may not be an effective method of training ankle movements during a simple, one-degree-of-freedom task.

Toward a generalizable deep CNN for neural drive estimation across muscles and participants.

Objective.High-density electromyography (HD-EMG) decomposition algorithms are used to identify individual motor unit (MU) spike trains, which collectively constitute the neural code of movements, to predict motor intent. This approach has advanced from offline to online decomposition, from isometric to dynamic contractions, leading to a wide range of neural-machine interface applications. However, current online methods need offline retraining when applied to the same muscle on a different day or to a different person, which limits their applications in a real-time neural-machine interface. We proposed a deep convolutional neural network (CNN) framework for neural drive estimation, which takes in frames of HD-EMG signals as input, extracts general spatiotemporal properties of MU action potentials, and outputs the number of spikes in each frame. The deep CNN can generalize its application without retraining to HD-EMG data recorded in separate sessions, muscles, or participants.Approach.We recorded HD-EMG signals from the vastus medialis and vastus lateralis muscles from five participants while they performed isometric contractions during two sessions separated by ∼20 months. We identified MU spike trains from HD-EMG signals using a convolutive blind source separation (BSS) method, and then used the cumulative spike train (CST) of these MUs and the HD-EMG signals to train and validate the deep CNN.Main results.On average, the correlation coefficients between CST from the BSS and that from deep CNN were0.983±0.006for leave-one-out across-sessions-and-muscles validation and0.989±0.002for leave-one-out across-participants validation. When trained with more than four datasets, the performance of deep CNN saturated at0.984±0.001for cross validations across muscles, sessions, and participants.Significance.We can conclude that the deep CNN is generalizable across the aforementioned conditions without retraining. We could potentially generate a robust deep CNN to estimate neural drive to muscles for neural-machine interfaces.

Componentes de hostilidad y discriminación en cardiología / Components of hostility and discrimination in cardiology

Resumen La población de diferentes géneros en México se enfrenta a desafíos significativos en salud, cómo el acceso limitado a la atención médica y la prevalencia de malas condiciones. La cardiología es crucial para esta población debido a la alta prevalencia de factores de riesgo y enfermedades cardiovasculares. A pesar de la falta de datos precisos, se estima que el 0.5-1.5% de la población se identifica como transgénero. Enfrentan desafíos socioculturales, como discriminación y estigmatización que contribuyen a problemas de salud y falta de acceso a tratamientos. Los factores de riesgo cardiovascular, la autoadministración de hormonas, y las barreras en el acceso a la atención médica son preocupaciones destacadas. Las oportunidades para mejorar incluyen fortalecer la educación médica, implementar políticas inclusivas, y promover la investigación y recopilación de datos. Se sugiere el desarrollo de guías clínicas específicas y programas de salud inclusivos. La teoría de la minorización destaca la importancia de abordar tensiones psicosociales crónicas y reconocer la influencia de factores estresantes en la salud. Integrar servicios de salud para todas las poblaciones en cardiología y fomentar la resiliencia son estrategias clave. En resumen, se necesita un enfoque integral para reducir las disparidades en la atención cardíaca y mejorar la salud de todas las poblaciones en México.

Abstract The Gender-Diverse (TGD) population in Mexico faces significant health challenges, such as limited access to healthcare and a prevalence of adverse conditions. Cardiology is crucial for this population due to a high prevalence of risk factors and cardiovascular diseases. Despite a lack of precise data, it is estimated that 0.5-1.5% of the population identifies as TGD. They encounter sociocultural challenges, including discrimination and stigma, contributing to health issues and a lack of treatment access. Cardiovascular risk factors, hormone self-administration, and barriers to healthcare access are prominent concerns. Opportunities for improvement involve strengthening medical education, implementing inclusive policies, and promoting research and data collection. The development of specific clinical guidelines and inclusive health programs is suggested. The theory of minority stress emphasizes addressing chronic psychosocial stressors and recognizing the influence of stress factors on health. Integrating healthcare services for all populations in cardiology and fostering resilience are key strategies. In summary, a comprehensive approach is needed to reduce disparities in cardiac care and enhance the health of all populations in Mexico.

A Deep CNN Framework for Neural Drive Estimation From HD-EMG Across Contraction Intensities and Joint Angles.

OBJECTIVE: Previous studies have demonstrated promising results in estimating the neural drive to muscles, the net output of all motoneurons that innervate the muscle, using high-density electromyography (HD-EMG) for the purpose of interfacing with assistive technologies. Despite the high estimation accuracy, current methods based on neural networks need to be trained with specific motor unit action potential (MUAP) shapes updated for each condition (i.e., varying muscle contraction intensities or joint angles). This preliminary step dramatically limits the potential generalization of these algorithms across tasks. We propose a novel approach to estimate the neural drive using a deep convolutional neural network (CNN), which can identify the cumulative spike train (CST) through general features of MUAPs from a pool of motor units. METHODS: We recorded HD-EMG signals from the gastrocnemius medialis muscle under three isometric contraction scenarios: 1) trapezoidal contraction tasks with different intensities, 2) contraction tasks with a trapezoidal or sinusoidal torque target, and 3) trapezoidal contraction tasks at different ankle angles. We applied a convolutive blind source separation (BSS) method to decompose HD-EMG signals to CST and segmented both signals into windows to train and validate the deep CNN. Then, we optimized the structure of the deep CNN and validated its generalizability across contraction tasks within each scenario. RESULTS: With the optimal configuration for the HD-EMG data window (overlap of 20 data points and window length of 40 data points), the deep CNN estimated the CST close to that from BSS, with a correlation coefficient higher than 0.96 and normalized root-mean-square-error lower than 7% with respect to the BSS (golden standard) within each scenario. CONCLUSION: The proposed deep CNN framework can utilize data from different contraction tasks (e.g., different intensities), learn general features of MUAP variants, and estimate the neural drive for other contraction tasks. SIGNIFICANCE: With the proposed deep CNN, we could potentially build a neural-drive-based human-machine interface that is generalizable to different contraction tasks without retraining.

Transcranial Magnetic Stimulation Following a Paired Associative Stimulation Protocol Based on a Video Game Neuromodulates Cortical Excitability and Motor Behavior.

Transcranial Magnetic Stimulation (TMS) can be used to modulate cortico-spinal excitability following a paired associative stimulation (PAS) protocol. Movement-related cortical stimulation (MRCS) is a PAS protocol based on the synchronization of a single-pulse TMS with a movement task. However, plasticity and motor performance potentiation due to MRCS has been related exclusively to single-movement tasks. In order to unveil the effects of an MRCS protocol in complex movements, we applied PAS synchronized with a movement-related dynamic task (MRDT) with a customized video game. In 22 healthy subjects, we measured the reaction time (RT), trajectory error (TE), and the number of collected and avoided items when playing the custom video game to evaluate the task motor performance. Moreover, we assessed the recruitment curve of Motor Evoked Potentials (MEPs) with five different intensities to evaluate the motor corticospinal excitability. MEPs were recorded in Abductor Pollicis Brevis (APB) and Abductor Digiti Minimi (ADM), before, right after, and 30 min after the PAS intervention, in an active versus sham experimental design. The MRCS PAS intervention resulted in RT reduction, and motor corticospinal excitability was modulated, reflected as significant MEP amplitude change at 110% RMT intensity in ADM and at 130% RMT intensity in APB. RTs and ADM MEP amplitudes correlated positively in specific time and intensity assessments. We conclude that the proposed PAS protocol facilitated RT performance in a complex task. This phenomenon might be useful to develop neurorehabilitation strategies with complex movements, similar to activities of daily living.

A unified scheme for the benchmarking of upper limb functions in neurological disorders.

BACKGROUND: In neurorehabilitation, we are witnessing a growing awareness of the importance of standardized quantitative assessment of limb functions. Detailed assessments of the sensorimotor deficits following neurological disorders are crucial. So far, this assessment has relied mainly on clinical scales, which showed several drawbacks. Different technologies could provide more objective and repeatable measurements. However, the current literature lacks practical guidelines for this purpose. Nowadays, the integration of available metrics, protocols, and algorithms into one harmonized benchmarking ecosystem for clinical and research practice is necessary. METHODS: This work presents a benchmarking framework for upper limb capacity. The scheme resulted from a multidisciplinary and iterative discussion among several partners with previous experience in benchmarking methodology, robotics, and clinical neurorehabilitation. We merged previous knowledge in benchmarking methodologies for human locomotion and direct clinical and engineering experience in upper limb rehabilitation. The scheme was designed to enable an instrumented evaluation of arm capacity and to assess the effectiveness of rehabilitative interventions with high reproducibility and resolution. It includes four elements: (1) a taxonomy for motor skills and abilities, (2) a list of performance indicators, (3) a list of required sensor modalities, and (4) a set of reproducible experimental protocols. RESULTS: We proposed six motor primitives as building blocks of most upper-limb daily-life activities and combined them into a set of functional motor skills. We identified the main aspects to be considered during clinical evaluation, and grouped them into ten motor abilities categories. For each ability, we proposed a set of performance indicators to quantify the proposed ability on a quantitative and high-resolution scale. Finally, we defined the procedures to be followed to perform the benchmarking assessment in a reproducible and reliable way, including the definition of the kinematic models and the target muscles. CONCLUSIONS: This work represents the first unified scheme for the benchmarking of upper limb capacity. To reach a consensus, this scheme should be validated with real experiments across clinical conditions and motor skills. This validation phase is expected to create a shared database of human performance, necessary to have realistic comparisons of treatments and drive the development of new personalized technologies.

Prediction of Pathological Tremor Signals Using Long Short-Term Memory Neural Networks.

Previous implementations of closed-loop peripheral electrical stimulation (PES) strategies have provided evidence about the effect of the stimulation timing on tremor reduction. However, these strategies have used traditional signal processing techniques that only consider phase prediction and might not model the non-stationary behavior of tremor. Here, we tested the use of long short-term memory (LSTM) neural networks to predict tremor signals using kinematic data recorded from Essential Tremor (ET) patients. A dataset comprising wrist flexion-extension data from 12 ET patients was pre-processed to feed the predictors. A total of 180 models resulting from the combination of network (neurons and layers of the LSTM networks, length of the input sequence and prediction horizon) and training parameters (learning rate) were trained, validated and tested. Predicted tremor signals using LSTM-based models presented high correlation values (from 0.709 to 0.998) with the expected values, with a phase delay between the predicted and real signals below 15 ms, which corresponds approximately to 7.5% of a tremor cycle. The prediction horizon was the parameter with a higher impact on the prediction performance. The proposed LSTM-based models were capable of predicting both phase and amplitude of tremor signals outperforming results from previous studies (32--56% decreased phase prediction error compared to the out-of-phase method), which might provide a more robust PES-based closed-loop control applied to PES-based tremor reduction.