Coupling between human brain activity and body movements: Insights from non-invasive electromagnetic recordings

Test-retest reliability of corticokinematic coherence in young children with cerebral palsy: An observational longitudinal study

OBJECTIVES

To assess the test-retest reliability of the corticokinematic coherence (CKC), an electrophysiological marker of proprioception, in children with cerebral palsy (CP).

METHODS

Electroencephalography (EEG) signals from 15 children with unilateral or bilateral CP aged 23 to 53 months were recorded in two sessions 3 months apart using 128-channel EEG caps. During each session, children's fingers were moved at 2 Hz by an experimenter, in separate recordings for the more-affected (MA) and less-affected (LA) hands. The CKC was computed at the electrode and source levels, at movement frequency F0 (2 Hz) and its first harmonic F1 (4 Hz). A two-way mixed-effects model intraclass-correlation coefficient (ICC) was computed for the maximum CKC strength across electrodes at F0 and F1 obtained during the two sessions.

RESULTS

ICC of the CKC strength acquired from LA and MA hands pooled together were respectively 0.51 (95% CI: 0.30-0.68) at F0 and 0.96 (95% CI: 0.93-0.98) at F1. The mean distances separating the CKC peaks in the source space at the two evaluation times were in the order of a centimeter.

CONCLUSION

CKC is a robust electrophysiologic marker to study the longitudinal changes in cortical processing of proprioceptive afferences in young children with CP.

Dual model transfer learning to compensate for individual variability in brain-computer interface

BACKGROUND AND OBJECTIVE

Recent advancements in brain-computer interface (BCI) technology have seen a significant shift towards incorporating complex decoding models such as deep neural networks (DNNs) to enhance performance. These models are particularly crucial for sophisticated tasks such as regression for decoding arbitrary movements. However, these BCI models trained and tested on individual data often face challenges with limited performance and generalizability across different subjects. This limitation is primarily due to a tremendous number of parameters of DNN models. Training complex models demands extensive datasets. Nevertheless, group data from many subjects may not produce sufficient decoding performance because of inherent variability in neural signals both across individuals and over time METHODS: To address these challenges, this study proposed a transfer learning approach that could effectively adapt to subject-specific variability in cortical regions. Our method involved training two separate movement decoding models: one on individual data and another on pooled group data. We then created a salience map for each cortical region from the individual model, which helped us identify the input's contribution variance across subjects. Based on the contribution variance, we combined individual and group models using a modified knowledge distillation framework. This approach allowed the group model to be universally applicable by assigning greater weights to input data, while the individual model was fine-tuned to focus on areas with significant individual variance RESULTS: Our combined model effectively encapsulated individual variability. We validated this approach with nine subjects performing arm-reaching tasks, with our method outperforming (mean correlation coefficient, r = 0.75) both individual (r = 0.70) and group models (r = 0.40) in decoding performance. In particular, there were notable improvements in cases where individual models showed low performances (e.g., r = 0.50 in the individual decoder to r = 0.61 in the proposed decoder) CONCLUSIONS: These results not only demonstrate the potential of our method for robust BCI, but also underscore its ability to generalize individual data for broader applicability.

The corticomuscular response to experimental pain via blood flow occlusion when applied to the ipsilateral and contralateral leg during an isometric force task

Blood flow occlusion (BFO) has been previously used to investigate physiological responses to muscle ischemia, showing increased perceptual effort (RPE) and pain along with impaired neuromuscular performance. However, at present, it is unclear how BFO alters corticomuscular activities when either applied to the exercising or nonexercising musculature. The present study therefore set out to assess the corticomuscular response to these distinct BFO paradigms during an isometric contraction precision task. In a repeated measures design, fifteen participants (age = 27.00 ± 5.77) completed 15 isometric contractions across three experimental conditions; no occlusion (CNTRL), occlusion of the contralateral (i.e., nonexercising) limb (CON-OCC), and occlusion of the ipsilateral (i.e., exercising) limb (IPS-OCC). Measures of force, electroencephalographic (EEG), and electromyographic (EMG) were recorded during contractions. We observed that IPS-OCC broadly impaired force steadiness, elevated EMG of the vastus lateralis, and heightened RPE and pain. IPSI-OCC also significantly decreased corticomuscular coherence during the early phase of contraction and decreased EEG alpha activity across the sensorimotor and temporoparietal regions during the middle and late phases of contraction compared with CNTRL. By contrast, CON-OCC increased perceived levels of pain (but not RPE) and decreased EEG alpha activity across the prefrontal cortex during the middle and late phases of contraction, with no changes observed for EMG and force steadiness. Together, these findings highlight distinctive psychophysiological responses to experimental pain via BFO showing altered cortical activities (CON-OCC) and altered cortical, corticomuscular, and neuromuscular activities (IPS-OCC) when applied to the lower limbs during an isometric force precision task.

Multiscale Canonical Coherence for Functional Corticomuscular Coupling Analysis

Functional corticomuscular coupling (FCMC) probes multi-level information communication in the sensorimotor system. The canonical Coherence (caCOH) method has been leveraged to measure the FCMC between two multivariate signals within the single scale. In this paper, we propose the concept of multiscale canonical Coherence (MS-caCOH) to disentangle complex multi-layer information and extract coupling features in multivariate spaces from multiple scales. Then, we verified the reliability and effectiveness of MS-caCOH on two types of data sets, i.e., a synthetic multivariate data set and a real-world multivariate data set. Our simulation results showed that compared with caCOH, MS-caCOH enhanced coupling detection and achieved lower pattern recovery errors at multiple frequency scales. Further analysis on experimental data demonstrated that the proposed MS-caCOH method could also capture detailed multiscale spatial-frequency characteristics. This study leverages the multiscale analysis framework and multivariate method to give a new insight into corticomuscular coupling analysis.

A review of combined functional neuroimaging and motion capture for motor rehabilitation

BACKGROUND

Technological advancements in functional neuroimaging and motion capture have led to the development of novel methods that facilitate the diagnosis and rehabilitation of motor deficits. These advancements allow for the synchronous acquisition and analysis of complex signal streams of neurophysiological data (e.g., EEG, fNIRS) and behavioral data (e.g., motion capture). The fusion of those data streams has the potential to provide new insights into cortical mechanisms during movement, guide the development of rehabilitation practices, and become a tool for assessment and therapy in neurorehabilitation.

RESEARCH OBJECTIVE

This paper aims to review the existing literature on the combined use of motion capture and functional neuroimaging in motor rehabilitation. The objective is to understand the diversity and maturity of technological solutions employed and explore the clinical advantages of this multimodal approach.

METHODS

This paper reviews literature related to the combined use of functional neuroimaging and motion capture for motor rehabilitation following the PRISMA guidelines. Besides study and participant characteristics, technological aspects of the used systems, signal processing methods, and the nature of multimodal feature synchronization and fusion were extracted.

RESULTS

Out of 908 publications, 19 were included in the final review. Basic or translation studies were mainly represented and based predominantly on healthy participants or stroke patients. EEG and mechanical motion capture technologies were most used for biomechanical data acquisition, and their subsequent processing is based mainly on traditional methods. The system synchronization techniques at large were underreported. The fusion of multimodal features mainly supported the identification of movement-related cortical activity, and statistical methods were occasionally employed to examine cortico-kinematic relationships.

CONCLUSION

The fusion of motion capture and functional neuroimaging might offer advantages for motor rehabilitation in the future. Besides facilitating the assessment of cognitive processes in real-world settings, it could also improve rehabilitative devices' usability in clinical environments. Further, by better understanding cortico-peripheral coupling, new neuro-rehabilitation methods can be developed, such as personalized proprioceptive training. However, further research is needed to advance our knowledge of cortical-peripheral coupling, evaluate the validity and reliability of multimodal parameters, and enhance user-friendly technologies for clinical adaptation.

Scale-invariant changes in corticospinal excitability reflect multiplexed oscillations in the motor output

In the absence of disease, humans produce smooth and accurate movement trajectories. Despite such 'macroscopic' aspect, the 'microscopic' structure of movements reveals recurrent (quasi-rhythmic) discontinuities. To date, it is unclear how the sensorimotor system contributes to the macroscopic and microscopic architecture of movement. Here, we investigated how corticospinal excitability changes in relation to microscopic fluctuations that are naturally embedded within larger macroscopic variations in motor output. Participants performed a visuomotor tracking task. In addition to the 0.25 Hz modulation that is required for task fulfilment (macroscopic scale), the motor output shows tiny but systematic fluctuations at ∼2 and 8 Hz (microscopic scales). We show that motor-evoked potentials (MEPs) elicited by transcranial magnetic stimulation (TMS) during task performance are consistently modulated at all (time) scales. Surprisingly, MEP modulation covers a similar range at both micro- and macroscopic scales, even though the motor output differs by several orders of magnitude. Thus, corticospinal excitability finely maps the multiscale temporal patterning of the motor output, but it does so according to a principle of scale invariance. These results suggest that corticospinal excitability indexes a relatively abstract level of movement encoding that may reflect the hierarchical organisation of sensorimotor processes. KEY POINTS: Motor behaviour is organised on multiple (time)scales. Small but systematic ('microscopic') fluctuations are engrained in larger and slower ('macroscopic') variations in motor output, which are instrumental in deploying the desired motor plan. Corticospinal excitability is modulated in relation to motor fluctuations on both macroscopic and microscopic (time)scales. Corticospinal excitability obeys a principle of scale invariance, that is, it is modulated similarly at all (time)scales, possibly reflecting hierarchical mechanisms that optimise motor encoding.

Volitional muscle activation intensifies neuronal processing of proprioceptive afference in the primary sensorimotor cortex: an EEG study

Proprioception refers to the ability to perceive the position and movement of body segments in space. The cortical aspects of the proprioceptive afference from the body can be investigated using corticokinematic coherence (CKC). CKC accurately quantifies the degree of coupling between cortical activity and limb kinematics, especially if precise proprioceptive stimulation of evoked movements is used. However, there is no evidence on how volitional muscle activation during proprioceptive stimulation affects CKC strength. Twenty-five healthy volunteers (28.8 ± 7 yr, 11 females) participated in the experiment, which included electroencephalographic (EEG), electromyographic (EMG), and kinematic recordings. Ankle-joint rotations (2-Hz) were elicited through a movement actuator in two conditions: passive condition with relaxed ankle and active condition with constant 5-Nm plantar flexion exerted during the stimulation. In total, 6 min of data were recorded per condition. CKC strength was defined as the maximum coherence value among all the EEG channels at the 2-Hz movement frequency for each condition separately. Both conditions resulted in significant CKC peaking at the Cz electrode over the foot area of the primary sensorimotor (SM1) cortex. Stronger CKC was found for the active (0.13 ± 0.14) than the passive (0.03 ± 0.04) condition (P < 0.01). The results indicated that volitional activation of the muscles intensifies the neuronal proprioceptive processing in the SM1 cortex. This finding could be explained both by peripheral sensitization of the ankle joint proprioceptors and central modulation of the neuronal proprioceptive processing at the spinal and cortical levels.NEW & NOTEWORTHY The current study is the first to investigate the effect of volitional muscle activation on CKC-based assessment of cortical proprioception of the ankle joint. Results show that the motor efference intensifies the neuronal processing of proprioceptive afference of the ankle joint. This is a significant finding as it may extend the use of CKC method during active tasks to further evaluate the motor efference-proprioceptive afference relationship and the related adaptations to exercise, rehabilitation, and disease.

Decoding movement frequencies and limbs based on steady-state movement-related rhythms from noninvasive EEG

Objective.Decoding different types of movements noninvasively from electroencephalography (EEG) is an essential topic in neural engineering, especially in brain-computer interface. Although the widely used sensorimotor rhythm (SMR) is efficient in limb decoding, it lacks efficacy in decoding movement frequencies. Accumulating evidence supports the notion that the movement frequency is encoded in the steady-state movement-related rhythm (SSMRR). Our study has two primary objectives: firstly, to investigate the spatial-spectral representation of SSMRR in EEG during voluntary movements; secondly, to assess whether movement frequencies and limbs can be effectively decoded based on SSMRR.Approach.To comprehensively examine the representation of SSMRR, we investigated the frequency characteristics and spatial patterns associated with various rhythmic finger movements. Coherence analysis was performed between the sensor or source domain EEG and finger movements recorded by data gloves. A fusion model based on spectral SNR features and filter-bank common spatial pattern features was utilized to decode movement frequencies and limbs.Main results.At the group-level, sensor domain, and source domain coherence maps demonstrated that the accurate movement frequency (f0) and its first harmonic (f1) were encoded in the contralateral motor cortex. For the four-class classification, including two movement frequencies for both hands, the decoding accuracies for externally paced and internally paced movements were 73.14 ± 15.86% and 66.30 ± 17.26% (averaged across ten subjects, chance levels at 31.05% and 30.96%). Notably, the average results of five subjects with the highest decoding accuracies reached 87.21 ± 7.44% and 80.44 ± 7.99%.Significance.Our results verified the EEG representation of SSMRR and proved that the movement frequency and limb could be effectively decoded based on spatial-spectral features extracted from SSMRR. We suggest that SSMRR can serve as a complement to SMR to expand the range of decodable movement types and the approaches of limb decoding.

Proprioceptive and tactile processing in individuals with Friedreich ataxia: an fMRI study

Objective

Friedreich ataxia (FA) neuropathology affects dorsal root ganglia, posterior columns in the spinal cord, the spinocerebellar tracts, and cerebellar dentate nuclei. The impact of the somatosensory system on ataxic symptoms remains debated. This study aims to better evaluate the contribution of somatosensory processing to ataxia clinical severity by simultaneously investigating passive movement and tactile pneumatic stimulation in individuals with FA.

Methods

Twenty patients with FA and 20 healthy participants were included. All subjects underwent two 6 min block-design functional magnetic resonance imaging (fMRI) paradigms consisting of twelve 30 s alternating blocks (10 brain volumes per block, 120 brain volumes per paradigm) of a tactile oddball paradigm and a passive movement paradigm. Spearman rank correlation tests were used for correlations between BOLD levels and ataxia severity.

Results

The passive movement paradigm led to the lower activation of primary (cSI) and secondary somatosensory cortices (cSII) in FA compared with healthy subjects (respectively 1.1 ± 0.78 vs. 0.61 ± 1.02, p = 0.04, and 0.69 ± 0.5 vs. 0.3 ± 0.41, p = 0.005). In the tactile paradigm, there was no significant difference between cSI and cSII activation levels in healthy controls and FA (respectively 0.88 ± 0.73 vs. 1.14 ± 0.99, p = 0.33, and 0.54 ± 0.37 vs. 0.55 ± 0.54, p = 0.93). Correlation analysis showed a significant correlation between cSI activation levels in the tactile paradigm and the clinical severity (R = 0.481, p = 0.032).

Interpretation

Our study captured the difference between tactile and proprioceptive impairments in FA using somatosensory fMRI paradigms. The lack of correlation between the proprioceptive paradigm and ataxia clinical parameters supports a low contribution of afferent ataxia to FA clinical severity.

A Dynamical Systems Approach to Characterizing Brain-Body Interactions during Movement: Challenges, Interpretations, and Recommendations

Brain-body interactions (BBIs) have been the focus of intense scrutiny since the inception of the scientific method, playing a foundational role in the earliest debates over the philosophy of science. Contemporary investigations of BBIs to elucidate the neural principles of motor control have benefited from advances in neuroimaging, device engineering, and signal processing. However, these studies generally suffer from two major limitations. First, they rely on interpretations of 'brain' activity that are behavioral in nature, rather than neuroanatomical or biophysical. Second, they employ methodological approaches that are inconsistent with a dynamical systems approach to neuromotor control. These limitations represent a fundamental challenge to the use of BBIs for answering basic and applied research questions in neuroimaging and neurorehabilitation. Thus, this review is written as a tutorial to address both limitations for those interested in studying BBIs through a dynamical systems lens. First, we outline current best practices for acquiring, interpreting, and cleaning scalp-measured electroencephalography (EEG) acquired during whole-body movement. Second, we discuss historical and current theories for modeling EEG and kinematic data as dynamical systems. Third, we provide worked examples from both canonical model systems and from empirical EEG and kinematic data collected from two subjects during an overground walking task.

EMG-projected MEG High-Resolution Source Imaging of Human Motor Execution: Brain-Muscle Coupling above Movement Frequencies

Magnetoencephalography (MEG) is a non-invasive functional imaging technique for pre-surgical mapping. However, movement-related MEG functional mapping of primary motor cortex (M1) has been challenging in presurgical patients with brain lesions and sensorimotor dysfunction due to the large numbers of trails needed to obtain adequate signal to noise. Moreover, it is not fully understood how effective the brain communication is with the muscles at frequencies above the movement frequency and its harmonics. We developed a novel Electromyography (EMG)-projected MEG source imaging technique for localizing M1 during ~1 minute recordings of left and right self-paced finger movements (~1 Hz). High-resolution MEG source images were obtained by projecting M1 activity towards the skin EMG signal without trial averaging. We studied delta (1-4 Hz), theta (4-7 Hz), alpha (8-12 Hz), beta (15-30 Hz), and gamma (30-90 Hz) bands in 13 healthy participants (26 datasets) and two presurgical patients with sensorimotor dysfunction. In healthy participants, EMG-projected MEG accurately localized M1 with high accuracy in delta (100.0%), theta (100.0%), and beta (76.9%) bands, but not alpha (34.6%) and gamma (0.0%) bands. Except for delta, all other frequency bands were above the movement frequency and its harmonics. In both presurgical patients, M1 activity in the affected hemisphere was also accurately localized, despite highly irregular EMG movement patterns in one patient. Altogether, our EMG-projected MEG imaging approach is highly accurate and feasible for M1 mapping in presurgical patients. The results also provide insight into movement related brain-muscle coupling above the movement frequency and its harmonics.

Recurrent inhibition contribution to corticomuscular coherence modulation between contraction types

Recent findings provided evidence that spinal regulatory mechanisms were involved in corticomuscular coherence (CMC) modulation between contraction types. Although their relative contributions could not be precisely identified, it was suggested that recurrent inhibition (RI) could modulate CMC by regulating the synchronization of spinal motoneuron activity. To confirm this hypothesis, concurrent modulations of RI and CMC for the soleus (SOL) were compared during submaximal isometric, shortening and lengthening plantar flexions. Submaximal contraction intensity was set at 50% of the maximal SOL EMG activity. CMC was computed in the time-frequency domain between the Cz EEG electrode signal and the nonrectified SOL EMG signal. The RI was quantified through the paired Hoffmann (H) reflex technique by comparing the modulations of the test and conditioning H-reflexes (H' and H1 , respectively). Both beta-band CMC and the ratio between H' and H1 amplitudes were significantly lower in SOL during lengthening compared with isometric and shortening contractions. Furthermore, we observed a negative linear correlation between the RI and beta-band CMC. Finally, a higher RI increase during lengthening contractions compared to either isometric or shortening ones was correlated with a larger decrease in CMC. Collectively, these novel findings provide robust evidence that the RI acts as a neural "filter" that contributes to the modulation of corticomuscular interactions between contraction types, possibly by disrupting the oscillatory muscle activation.

Combined effect of contraction type and intensity on corticomuscular coherence during isokinetic plantar flexions

During isometric contractions, corticomuscular coherence (CMC) may be modulated along with the contraction intensity. Furthermore, CMC may also vary between contraction types due to the contribution of spinal inhibitory mechanisms. However, the interaction between the effect of the contraction intensity and of the contraction type on CMC remains hitherto unknown. Therefore, CMC and spinal excitability modulations were compared during submaximal isometric, shortening and lengthening contractions of plantar flexor muscles at 25, 50, and 70% of the maximal soleus (SOL) EMG activity. CMC was computed in the time-frequency domain between the Cz EEG electrode signal and the SOL or medial gastrocnemius (MG) EMG signals. The results indicated that beta-band CMC was decreased in the SOL only between 25 and 50-70% contractions for both isometric and anisometric contractions, but remained similar for all contraction intensities in the MG. Spinal excitability was similar for all contraction intensities in both muscles. Meanwhile a divergence of the EEG and the EMG signals mean frequency was observed only in the SOL and only between 25 and 50-70% contractions, independently from the contraction type. Collectively, these findings confirm an effect of the contraction intensity on beta-band CMC, although it was only measured in the SOL, between low-level and high-level contraction intensities. Furthermore, the current findings provide new evidence that the observed modulations of beta-band CMC with the contraction intensity does not depend on the contraction type or on spinal excitability variations.

Proprioception: A New Era Set in Motion by Emerging Genetic and Bionic Strategies?

The generation of an internal body model and its continuous update is essential in sensorimotor control. Although known to rely on proprioceptive sensory feedback, the underlying mechanism that transforms this sensory feedback into a dynamic body percept remains poorly understood. However, advances in the development of genetic tools for proprioceptive circuit elements, including the sensory receptors, are beginning to offer new and unprecedented leverage to dissect the central pathways responsible for proprioceptive encoding. Simultaneously, new data derived through emerging bionic neural machine-interface technologies reveal clues regarding the relative importance of kinesthetic sensory feedback and insights into the functional proprioceptive substrates that underlie natural motor behaviors.

Identification of cerebral cortices processing acceleration, velocity, and position during directional reaching movement with deep neural network and explainable AI

Cerebral cortical representation of motor kinematics is crucial for understanding human motor behavior, potentially extending to efficient control of the brain-computer interface. Numerous single-neuron studies have found the existence of a relationship between neuronal activity and motor kinematics such as acceleration, velocity, and position. Despite differences between kinematic characteristics, it is hard to distinguish neural representations of these kinematic characteristics with macroscopic functional images such as electroencephalography (EEG) and magnetoencephalography (MEG). The reason might be because cortical signals are not sensitive enough to segregate kinematic characteristics due to their limited spatial and temporal resolution. Considering different roles of each cortical area in producing movement, there might be a specific cortical representation depending on characteristics of acceleration, velocity, and position. Recently, neural network modeling has been actively pursued in the field of decoding. We hypothesized that neural features of each kinematic parameter could be identified with a high-performing model for decoding with an explainable AI method. Time-series deep neural network (DNN) models were used to measure the relationship between cortical activity and motor kinematics during reaching movement. With DNN models, kinematic parameters of reaching movement in a 3D space were decoded based on cortical source activity obtained from MEG data. An explainable artificial intelligence (AI) method was then adopted to extract the map of cortical areas, which strongly contributed to decoding each kinematics from DNN models. We found that there existed differed as well as shared cortical areas for decoding each kinematic attribute. Shared areas included bilateral supramarginal gyri and superior parietal lobules known to be related to the goal of movement and sensory integration. On the other hand, dominant areas for each kinematic parameter (the contralateral motor cortex for acceleration, the contralateral parieto-frontal network for velocity, and bilateral visuomotor areas for position) were mutually exclusive. Regarding the visuomotor reaching movement, the motor cortex was found to control the muscle force, the parieto-frontal network encoded reaching movement from sensory information, and visuomotor areas computed limb and gaze coordination in the action space. To the best of our knowledge, this is the first study to discriminate kinematic cortical areas using DNN models and explainable AI.

Cortical processing during robot and functional electrical stimulation

Introduction

Like alpha rhythm, the somatosensory mu rhythm is suppressed in the presence of somatosensory inputs by implying cortical excitation. Sensorimotor rhythm (SMR) can be classified into two oscillatory frequency components: mu rhythm (8-13 Hz) and beta rhythm (14-25 Hz). The suppressed/enhanced SMR is a neural correlate of cortical activation related to efferent and afferent movement information. Therefore, it would be necessary to understand cortical information processing in diverse movement situations for clinical applications.

Methods

In this work, the EEG of 10 healthy volunteers was recorded while fingers were moved passively under different kinetic and kinematic conditions for proprioceptive stimulation. For the kinetics aspect, afferent brain activity (no simultaneous volition) was compared under two conditions of finger extension: (1) generated by an orthosis and (2) generated by the orthosis simultaneously combined and assisted with functional electrical stimulation (FES) applied at the forearm muscles related to finger extension. For the kinematic aspect, the finger extension was divided into two phases: (1) dynamic extension and (2) static extension (holding the extended position).

Results

In the kinematic aspect, both mu and beta rhythms were more suppressed during a dynamic than a static condition. However, only the mu rhythm showed a significant difference between kinetic conditions (with and without FES) affected by attention to proprioception after transitioning from dynamic to static state, but the beta rhythm was not.

Discussion

Our results indicate that mu rhythm was influenced considerably by muscle kinetics during finger movement produced by external devices, which has relevant implications for the design of neuromodulation and neurorehabilitation interventions.

Cortical networks show characteristic recruitment patterns after somatosensory stimulation by pneumatically evoked repetitive hand movements in newborn infants

Controlled assessment of functional cortical networks is an unmet need in the clinical research of noncooperative subjects, such as infants. We developed an automated, pneumatic stimulation method to actuate naturalistic movements of an infant’s hand, as well as an analysis pipeline for assessing the elicited electroencephalography (EEG) responses and related cortical networks. Twenty newborn infants with perinatal asphyxia were recruited, including 7 with mild-to-moderate hypoxic–ischemic encephalopathy (HIE). Statistically significant corticokinematic coherence (CKC) was observed between repetitive hand movements and EEG in all infants, peaking near the contralateral sensorimotor cortex. CKC was robust to common sources of recording artifacts and to changes in vigilance state. A wide recruitment of cortical networks was observed with directed phase transfer entropy, also including areas ipsilateral to the stimulation. The extent of such recruited cortical networks was quantified using a novel metric, Spreading Index, which showed a decrease in 4 (57%) of the infants with HIE. CKC measurement is noninvasive and easy to perform, even in noncooperative subjects. The stimulation and analysis pipeline can be fully automated, including the statistical evaluation of the cortical responses. Therefore, the CKC paradigm holds great promise as a scientific and clinical tool for controlled assessment of functional cortical networks.

Feature stability and setup minimization for EEG-EMG-enabled monitoring systems

Delivering health care at home emerged as a key advancement to reduce healthcare costs and infection risks, as during the SARS-Cov2 pandemic. In particular, in motor training applications, wearable and portable devices can be employed for movement recognition and monitoring of the associated brain signals. This is one of the contexts where it is essential to minimize the monitoring setup and the amount of data to collect, process, and share. In this paper, we address this challenge for a monitoring system that includes high-dimensional EEG and EMG data for the classification of a specific type of hand movement. We fuse EEG and EMG into the magnitude squared coherence (MSC) signal, from which we extracted features using different algorithms (one from the authors) to solve binary classification problems. Finally, we propose a mapping-and-aggregation strategy to increase the interpretability of the machine learning results. The proposed approach provides very low mis-classification errors ( < 0.1 ), with very few and stable MSC features ( < 10 % of the initial set of available features). Furthermore, we identified a common pattern across algorithms and classification problems, i.e., the activation of the centro-parietal brain areas and arm's muscles in 8-80 Hz frequency band, in line with previous literature. Thus, this study represents a step forward to the minimization of a reliable EEG-EMG setup to enable gesture recognition.

TV-NARX and Coiflets WPT based time-frequency Granger causality with application to corticomuscular coupling in hand-grasping

The study of the synchronous characteristics and functional connections between the functional cortex and muscles of hand-grasping movements is important in basic research, clinical disease diagnosis and rehabilitation evaluation. The electroencephalogram (EEG) and electromyographic signal (EMG) signals of 15 healthy participants were used to analyze the corticomuscular coupling under grasping movements by holding three different objects, namely, card, ball, and cup by using the time-frequency Granger causality method based on time-varying nonlinear autoregressive with exogenous input (TV-NARX) model and Coiflets wavelet packet transform. The results show that there is a bidirectional coupling between cortex and muscles under grasping movement, and it is mainly reflected in the beta and gamma frequency bands, in which there is a statistically significant difference (p < 0.05) among the different grasping actions during the movement execution period in the beta frequency band, and a statistically significant difference (p < 0.1) among the different grasping actions during the movement preparation period in the gamma frequency band. The results show that the proposed method can effectively characterize the EEG-EMG synchronization features and functional connections in different frequency bands during the movement preparation and execution phases in the time-frequency domain, and reveal the neural control mechanism of sensorimotor system to control the hand-grasping function achievement by regulating the intensity of neuronal synchronization oscillations.

Temporally stable beta sensorimotor oscillations and cortico-muscular coupling underlie force steadiness

As humans, we seamlessly hold objects in our hands, and may even lose consciousness of these objects. This phenomenon raises the unsettled question of the involvement of the cerebral cortex, the core area for voluntary motor control, in dynamically maintaining steady muscle force. To address this issue, we measured magnetoencephalographic brain activity from healthy adults who maintained a steady pinch grip. Using a novel analysis approach, we uncovered fine-grained temporal modulations in the beta sensorimotor brain rhythm and its coupling with muscle activity, with respect to several aspects of muscle force (rate of increase/decrease or plateauing high/low). These modulations preceded changes in force features by ∼40 ms and possessed behavioral relevance, as less salient or absent modulation predicted a more stable force output. These findings have consequences for the existing theories regarding the functional role of cortico-muscular coupling, and suggest that steady muscle contractions are characterized by a stable rather than fluttering involvement of the sensorimotor cortex.

Quantification of cortical proprioceptive processing through a wireless and miniaturized EEG amplifier

Corticokinematic coherence (CKC) is computed between limb kinematics and cortical activity (e.g. MEG, EEG), and it can be used to detect, quantify and localize the cortical processing of proprioceptive afference arising from the body. EEG-based studies on CKC have been limited to lab environments due to bulky, non-portable instrumentations. We recently proposed a wireless and miniaturized EEG acquisition system aimed at enabling EEG studies outside the laboratory. The purpose of this work is to compare the EEG-based CKC values obtained with this device with a conventional wired-EEG acquisition system to validate its use in the quantification of cortical proprioceptive processing. Eleven healthy right-handed participants were recruited (six males, four females, age range: 24-40 yr). A pneumatic-movement actuator was used to evoke right index-finger flexion-extension movement at 3 Hz for 4 min. The task was repeated both with the wireless-EEG and wired-EEG devices using the same 30-channel EEG cap preparation. CKC was computed between the EEG and finger acceleration. CKC peaked at the movement frequency and its harmonics, being statistically significant (p < 0.05) in 8-10 out of 11 participants. No statistically significant differences (p < 0.05) were found in CKC strength between wireless-EEG (range 0.03-0.22) and wired-EEG (0.02-0.33) systems, that showed a good agreement between the recording systems (3 Hz: r = 0.57, p = 0.071, 6 Hz: r = 0.82, p = 0.003). As expected, CKC peaked in sensors above the left primary sensorimotor cortex contralateral to the moved right index finger. As the wired-EEG device, the tested wireless-EEG system has proven feasible to quantify CKC, and thus can be used as a tool to study proprioception in the human neocortex. Thanks to its portability, the wireless-EEG used in this study has the potential to enable the examination of cortical proprioception in more naturalistic conditions outside the laboratory environment. Clinical Relevance-Our study will contribute to provide innovative technological foundations for future unobtrusive EEG recordings in naturalistic conditions to examine human sensorimotor system.

Intermuscular coupling network analysis of upper limbs based on R-vine copula transfer entropy

In the field of neuroscience, it is very important to evaluate the causal coupling characteristics between bioelectrical signals accurately and effectively. Transfer entropy is commonly used to analyze complex data, especially the causal relationship between data with non-linear, multidimensional characteristics. However, traditional transfer entropy needs to estimate the probability density function of the variable, which is computationally complex and unstable. In this paper, a new and effective method for entropy transfer is proposed, by means of applying R-vine copula function estimation. The effectiveness of R-vine copula transfer entropy is first verified on several simulations, and then applied to intermuscular coupling analysis to explore the characteristics of the intermuscular coupling network of muscles in non-fatigue and fatigue conditions. The experiment results show that as the muscle group enters the fatigue state, the community structure can be adjusted and the muscle nodes participating in the exercise are fully activated, enabling the two-way interaction between different communities. Finally, it comes to the conclusion that the proposed method can make accurate inferences about complex causal coupling. Moreover, the characteristics of the intermuscular coupling network in both non-fatigue and fatigue states can provide a new theoretical perspective for the diagnosis of neuromuscular fatigue and sports rehabilitation, which has good application value.

Mutual beta power modulation in dyadic entrainment

Across a broad spectrum of interactions, humans exhibit a prominent tendency to synchronize their movements with one another. Traditionally, this phenomenon has been explained from the perspectives of predictive coding or dynamical systems theory. While these theories diverge with respect to whether individuals hold internal models of each other, they both assume a predictive or anticipatory mechanism enabling rhythmic interactions. However, the neural bases underpinning interpersonal synchronization are still a subject under active investigation. Here we provide evidence that the brain relies on a common oscillatory mechanism to pace self-generated rhythmic movements and to track the movements produced by a partner. By performing dual-electroencephalography recordings during a joint finger-tapping task, we identified an oscillatory component in the beta range (∼ 20 Hz), which was significantly modulated by both self-generated and other-generated movement. In conditions where the partners perceived each other, we observed periodic fluctuations of beta power as a function of the reciprocal movement cycles. Crucially, this modulation occurred both in visually and in auditorily coupled conditions, and was accompanied by recurrent periods of dyadic synchronized behavior. Our results show that periodic beta power modulations may be a critical mechanism underlying interpersonal synchronization, possibly enabling mutual predictions between coupled individuals, leading to co-regulation of timing and overt mutual adaptation. Our findings thus provide a potential bridge between influential theories attempting to explain interpersonal coordination, and a concrete connection to its neurophysiological bases.

Interpersonal synchronization of movement intermittency

Most animal species group together and coordinate their behavior in quite sophisticated manners for mating, hunting, or defense purposes. In humans, coordination at a macroscopic level (the pacing of movements) is evident both in daily life (e.g., walking) and skilled (e.g., music and dance) behaviors. By examining the fine structure of movement, we here show that interpersonal coordination is established also at a microscopic – submovement – level. Natural movements appear as marked by recurrent (2–3 Hz) speed breaks, i.e., submovements, that are traditionally considered the result of intermittency in (visuo)motor feedback-based control. In a series of interpersonal coordination tasks, we show that submovements produced by interacting partners are not independent but alternate tightly over time, reflecting online mutual adaptation. These findings unveil a potential core mechanism for behavioral coordination that is based on between-persons synchronization of the intrinsic dynamics of action-perception cycles.

•

Movements show intermittent speed pulses occurring at 2–3 Hz, called submovements

•

Submovements are actively coordinated in counter-phase by interacting partners

•

Submovements coordination depends on spatial alignment but not movement congruency

•

Behavioral coordination occurs both at macro- and microscopic movement scales

Gratifying Gizmos for Research and Clinical MEG

Experimental designs are of utmost importance in neuroimaging. Experimental repertoire needs to be designed with the understanding of physiology, clinical feasibility, and constraints posed by a particular neuroimaging method. Innovations in introducing natural, ecologically-relevant stimuli, with successful collaboration across disciplines, correct timing, and a bit of luck may cultivate novel experiments, new discoveries, and open pathways to new clinical practices. Here I introduce some gizmos that I have initiated in magnetoencephalography (MEG) and applied with my collaborators in my home laboratory and in several other laboratories. These gizmos have been applied to address neuronal correlates of audiotactile interactions, tactile sense, active and passive movements, speech processing, and intermittent photic stimulation (IPS) in humans. This review also includes additional notes on the ideas behind the gizmos, their evolution, and results obtained.

Functional cortical localization of tongue movements using corticokinematic coherence with a deep learning-assisted motion capture system

Corticokinematic coherence (CKC) between magnetoencephalographic and movement signals using an accelerometer is useful for the functional localization of the primary sensorimotor cortex (SM1). However, it is difficult to determine the tongue CKC because an accelerometer yields excessive magnetic artifacts. Here, we introduce a novel approach for measuring the tongue CKC using a deep learning-assisted motion capture system with videography, and compare it with an accelerometer in a control task measuring finger movement. Twelve healthy volunteers performed rhythmical side-to-side tongue movements in the whole-head magnetoencephalographic system, which were simultaneously recorded using a video camera and examined using a deep learning-assisted motion capture system. In the control task, right finger CKC measurements were simultaneously evaluated via motion capture and an accelerometer. The right finger CKC with motion capture was significant at the movement frequency peaks or its harmonics over the contralateral hemisphere; the motion-captured CKC was 84.9% similar to that with the accelerometer. The tongue CKC was significant at the movement frequency peaks or its harmonics over both hemispheres. The CKC sources of the tongue were considerably lateral and inferior to those of the finger. Thus, the CKC with deep learning-assisted motion capture can evaluate the functional localization of the tongue SM1.

Assessing spino-cortical proprioceptive processing in childhood unilateral cerebral palsy with corticokinematic coherence

OBJECTIVE

To develop an electrophysiological marker of proprioceptive spino-cortical tracts integrity based on corticokinematic coherence (CKC) in young children with unilateral cerebral palsy (UCP), in whom behavioral measures are not applicable.

METHODS

Electroencephalography (EEG) signals from 12 children with UCP aged 19 to 57 months were recorded using 128-channel EEG caps while their fingers were moved at 2 Hz by an experimenter, in separate sessions for the affected and non-affected hands. The coherence between movement kinematics and EEG signals (i.e., CKC) was computed at the sensor and source (using a realistic head model) levels. Peaks of CKC obtained for the affected and non-affected hands were compared for location and strength. The relation between CKC strength on the lesion-side, the lesion-type (cortico-subcortical vs. subcortical) and the level of manual ability were studied with 2-way repeated-measures ANOVA.

RESULTS

At the individual level, a significant CKC peak at the central area contralateral to the moved hand was found in all young children with their non-affected hand and in 8 out of 12 children with their affected hand. At the group level, CKC to the affected hand movements was weaker than CKC to the non-affected hand movements. This difference was influenced by the type of lesion, the effect being predominant in the subgroup (n = 5) with cortico-subcortical lesions.

CONCLUSION

CKC is measurable with EEG in young children with UCP and provides electrophysiological evidence for altered proprioceptive spino-cortical tracts on the lesioned brain hemisphere, particularly in children with cortico-subcortical lesions.

The Role of the Motor Cortex in Tremor Suppression in Parkinson's Disease

BACKGROUND

Patients with Parkinson's disease (PD) and rest tremor may also have tremor during posture holding, with tremor being transiently suppressed during the transition between resting and posture holding. Other PD patients show no tremor suppression between resting and posture holding. The mechanisms responsible for tremor suppression in PD are unknown. Understanding the mechanisms of tremor suppression would expand our knowledge of tremor pathophysiology in PD.

OBJECTIVE

To investigate whether tremor suppression reflects the activity of the primary motor cortex (M1) and assess whether tremor features are different in patients with and without tremor suppression.

METHODS

We compared corticomuscular coherence (CMC) at tremor frequency and transcranial magnetic stimulation tremor resetting between 10 PD patients with tremor suppression and 10 patients without suppression. We also compared tremor spectral features between the two groups.

RESULTS

Patients with tremor suppression had higher CMC at tremor frequency during both rest tremor and postural tremor, and a higher postural tremor resetting index and stability when compared with patients without tremor suppression. Rest tremor frequency was similar between the two groups, but postural tremor frequency was lower in patients with tremor suppression as compared to patients without.

CONCLUSION

M1 plays a major role in tremor suppression in PD, and the mechanisms of postural tremor may differ between patients with and without tremor suppression.

Comparison of undirected frequency-domain connectivity measures for cerebro-peripheral analysis

Analyses of cerebro-peripheral connectivity aim to quantify ongoing coupling between brain activity (measured by MEG/EEG) and peripheral signals such as muscle activity, continuous speech, or physiological rhythms (such as pupil dilation or respiration). Due to the distinct rhythmicity of these signals, undirected connectivity is typically assessed in the frequency domain. This leaves the investigator with two critical choices, namely a) the appropriate measure for spectral estimation (i.e., the transformation into the frequency domain) and b) the actual connectivity measure. As there is no consensus regarding best practice, a wide variety of methods has been applied. Here we systematically compare combinations of six standard spectral estimation methods (comprising fast Fourier and continuous wavelet transformation, bandpass filtering, and short-time Fourier transformation) and six connectivity measures (phase-locking value, Gaussian-Copula mutual information, Rayleigh test, weighted pairwise phase consistency, magnitude squared coherence, and entropy). We provide performance measures of each combination for simulated data (with precise control over true connectivity), a single-subject set of real MEG data, and a full group analysis of real MEG data. Our results show that, overall, WPPC and GCMI tend to outperform other connectivity measures, while entropy was the only measure sensitive to bimodal deviations from a uniform phase distribution. For group analysis, choosing the appropriate spectral estimation method appears to be more critical than the connectivity measure. We discuss practical implications (sampling rate, SNR, computation time, and data length) and aim to provide recommendations tailored to particular research questions.

Disrupted cortico-peripheral interactions in motor disorders

Motor disorders may arise from neurological damage or diseases at different levels of the hierarchical motor control system and side-loops. Altered cortico-peripheral interactions might be essential characteristics indicating motor dysfunctions. By integrating cortical and peripheral responses, top-down and bottom-up cortico-peripheral coupling measures could provide new insights into the motor control and recovery process. This review first discusses the neural bases of cortico-peripheral interactions, and corticomuscular coupling and corticokinematic coupling measures are addressed. Subsequently, methodological efforts are summarized to enhance the modeling reliability of neural coupling measures, both linear and nonlinear approaches are introduced. The latest progress, limitations, and future directions are discussed. Finally, we emphasize clinical applications of cortico-peripheral interactions in different motor disorders, including stroke, neurodegenerative diseases, tremor, and other motor-related disorders. The modified interaction patterns and potential changes following rehabilitation interventions are illustrated. Altered coupling strength, modified coupling directionality, and reorganized cortico-peripheral activation patterns are pivotal attributes after motor dysfunction. More robust coupling estimation methodologies and combination with other neurophysiological modalities might more efficiently shed light on motor control and recovery mechanisms. Future studies with large sample sizes might be necessary to determine the reliabilities of cortico-peripheral interaction measures in clinical practice.

Temporal Dynamics of Corticomuscular Coherence Reflects Alteration of the Central Mechanisms of Neural Motor Control in Post-Stroke Patients

The neural control of muscular activity during a voluntary movement implies a continuous updating of a mix of afferent and efferent information. Corticomuscular coherence (CMC) is a powerful tool to explore the interactions between the motor cortex and the muscles involved in movement realization. The comparison of the temporal dynamics of CMC between healthy subjects and post-stroke patients could provide new insights into the question of how agonist and antagonist muscles are controlled related to motor performance during active voluntary movements. We recorded scalp electroencephalography activity, electromyography signals from agonist and antagonist muscles, and upper limb kinematics in eight healthy subjects and seventeen chronic post-stroke patients during twenty repeated voluntary elbow extensions and explored whether the modulation of the temporal dynamics of CMC could contribute to motor function impairment. Concomitantly with the alteration of elbow extension kinematics in post-stroke patients, dynamic CMC analysis showed a continuous CMC in both agonist and antagonist muscles during movement and highlighted that instantaneous CMC in antagonist muscles was higher for post-stroke patients compared to controls during the acceleration phase of elbow extension movement. In relation to motor control theories, our findings suggest that CMC could be involved in the online control of voluntary movement through the continuous integration of sensorimotor information. Moreover, specific alterations of CMC in antagonist muscles could reflect central command alterations of the selectivity in post-stroke patients.

Corticokinematic coherence is stronger to regular than irregular proprioceptive stimulation of the hand

Proprioceptive afference can be investigated using corticokinematic coherence (CKC), which indicates coupling between limb kinematics and cortical activity. CKC has been quantified using proprioceptive stimulation (movement actuators) with fixed interstimulus interval (ISI). However, it is unclear how regularity of the stimulus sequence (jitter) affects CKC strength. Eighteen healthy volunteers (16 right-handed, 27.8 ± 5.0 yr, 7 females) participated in magnetoencephalography (MEG) session in which their right index finger was continuously moved at ∼3 Hz with Constant 333 ms ISI or with 20% Jitter (ISI 333 ± 66 ms) using a pneumatic-movement actuator. Three minutes of data per condition were collected. Finger kinematics were recorded with a three-axis accelerometer. CKC strength was defined as the peak coherence value in the Rolandic MEG gradiometer pair contralateral to the movement at 3 Hz. Both conditions resulted in significant coherence peaking in the gradiometers over the primary sensorimotor cortex. Constant stimulation yielded stronger CKC at 3 Hz (0.78 ± 0.11 vs. 0.66 ± 0.13, P < 0.001) and its first harmonic (0.60 ± 0.19 vs. 0.27 ± 0.11, P < 0.001) than irregular stimulation. Similarly, the respective sustained-movement evoked field was also stronger for constant stimulation. The results emphasize the importance of temporal stability of the proprioceptive stimulation sequence when quantifying CKC strength. The weaker CKC during irregular stimulation can be explained with temporal and thus spectral scattering of the paired peripheral and cortical events beyond the mean stimulation frequency. This impairs the signal-to-noise ratio of respective MEG signal and thus CKC strength. When accurately estimating and following changes in CKC strength, we suggest using precise movement actuators with constant stimulation sequence.NEW & NOTEWORTHY Cortical proprioceptive processing can be investigated using corticokinematic coherence (CKC). The findings show that CKC method is sensitive to temporal stability in the stimulation sequence. Although both regular and irregular sequences resulted in robust coherence, the regular stimulation sequence with pneumatic movement actuator is recommended to maximize coherence strength and reproducibility to allow better comparability between groups or populations.

Pneumatic artificial muscle-based stimulator for passive functional magnetic resonance imaging sensorimotor mapping in patients with brain tumours

BACKGROUND

Two concerns with respect to pre-operative task-based motor functional magnetic resonance imaging (fMRI) in patients with brain tumours are inadequate performance due to patients' impaired motor function and head motion artefacts.

NEW METHOD

In the present study we validate the use of a stimulator based on a pneumatic artificial muscle (PAM) for fMRI mapping of the primary sensorimotor (SM1) cortex in twenty patients with rolandic or perirolandic brain tumours. All patients underwent both active and passive motor block-design fMRI paradigms, performing comparable active and passive PAM-induced flexion-extensions of the icontralesional index finger.

RESULTS

PAM-induced movements resulted in a significant BOLD signal increase in contralateral primary motor (M1) and somatosensory (S1) cortices in 18/20 and 19/20 (p<.05 FWE corrected in 16/18 and 18/19) patients, versus 18/20 and 16/20 (p<.05 FWE corrected) during active movements. The two patients in whom the PAM-based stimulator failed to induce any significant BOLD signal change in the contralateral M1 cortex differed from the two in whom active motion was conversely ineffective. At the group level, no significant difference in contrast magnitude was observed within the contralateral SM1 cortex when comparing active with passive movements. During passive movements, head motion was significantly reduced. Comparison with existing method(s) As compared to the several robotic devices for passive motion that were introduced in the past decades, our PAM-based stimulator appears smaller, handier, and easier to use.

CONCLUSION

The use of PAM-based stimulators should be included in routine pre-operative fMRI protocols along with active paradigms in such patients' population.

Dynamic modulation of cortico-muscular coupling during real and imagined sensorimotor synchronisation

People have a natural and intrinsic ability to coordinate body movements with rhythms surrounding them, known as sensorimotor synchronisation. This can be observed in daily environments, when dancing or singing along with music, or spontaneously walking, talking or applauding in synchrony with one another. However, the neurophysiological mechanisms underlying accurately synchronised movement with selected rhythms in the environment remain unclear. Here we studied real and imagined sensorimotor synchronisation with interleaved auditory and visual rhythms using cortico-muscular coherence (CMC) to better understand the processes underlying the preparation and execution of synchronised movement. Electroencephalography (EEG), electromyography (EMG) from the finger flexors, and continuous force signals were recorded in 20 participants during tapping and imagined tapping with discrete stimulus sequences consisting of alternating auditory beeps and visual flashes. The results show that the synchronisation between cortical and muscular activity in the beta (14-38 Hz) frequency band becomes time-locked to the taps executed in synchrony with the visual and auditory stimuli. Dynamic modulation in CMC also occurred when participants imagined tapping with the visual stimuli, but with lower amplitude and a different temporal profile compared to real tapping. These results suggest that CMC does not only reflect changes related to the production of the synchronised movement, but also to its preparation, which appears heightened under higher attentional demands imposed when synchronising with the visual stimuli. These findings highlight a critical role of beta band neural oscillations in the cortical-muscular coupling underlying sensorimotor synchronisation.

Analysis of corticomuscular connectivity during walking using vine copula

Corticomuscular connectivity plays an important role in the neural control of human motion. This study recorded electroencephalography (EEG) and surface electromyography (sEMG) signals from subjects performing specific tasks (walking on level ground and on stairs) based on metronome instructions. This study presents a novel method based on vine copula to jointly model EEG and sEMG signals. The advantage of vine copula is its applicability in the construction of dependency structures to describe the connectivity between the cortex and muscles during different movements. A corticomuscular function network was also constructed by analyzing the dependence of each channel sample. The successfully constructed network shows information transmission between different divisions of the cortex, between muscles, and between the cortex and muscles when the body performs lower limb movements. Additionally, it highlights the potential of the vine copula concept used in this study, indicating that significant changes in the corticomuscular network under lower limb movements can be quantified by effective connectivity values.

Toward improving functional recovery in spinal cord injury using robotics: a pilot study focusing on ankle rehabilitation

Background: Conventional physical therapy interventions are strongly recommended to improve ambulation potential and upright mobility in persons with incomplete spinal cord injury (iSCI). Ankle rehabilitation plays a significant role, as it aims to stem drop foot consequences.Research question: This pilot study aimed to assess the neurophysiological underpinnings of robot-aided ankle rehabilitation (using a platform robot) compared to conventional physiotherapy and its efficacy in improving gait performance and balance in persons with iSCI.Methods: Ten individuals with subacute/chronic iSCI (six males and four females, 39 ± 13 years, time since injury 8 ± 4 months, ASIA impairment scale grade C-D) were provided with one-month intensive training for robot-aided ankle rehabilitation (24 sessions, 1 h daily, six times a week). Clinical (10-Meter Walk Test (10MWT), 6-Minute Walk Test (6MWT), and Timed Up and Go test (TUG)), and electrophysiological aftereffects (surface-EMG from tibialis anterior and medial gastrocnemius muscles to estimate muscle activation patterns; and corticomuscular coherence-CMC-to assess functional synchronization between sensorimotor cortex and muscles, i.e. the functional integrity of corticospinal output) were assessed at baseline (PRE) and after the trial completion (POST). The experimental group (EG) data were compared with those coming from a retrospective control group (CG; n = 10) matched for clinical-demographic characteristics, who previously underwent conventional ankle rehabilitation.Results: the EG achieved a greater improvement in balance and gait as compared to the CG (TUG EG from 70 ± 18 to 45 ± 15 s, p = 0.002; CG from 68 ± 21 to 48 ± 18 s, p = 0.01; group-comparison p = 0.001; 10MWT EG from 0.43 ± 0.11 to 0.51 ± 0.09 m/s, p = 0.006; CG from 0.4 ± 0.13 to 0.45 ± 0.12, p = 0.01; group-comparison p = 0.006; 6 MWT EG from 231 ± 13 to 274 ± 15 m, p < 0.001; CG from 236 ± 13 to 262 ± 15 m, p = 0.003; group-comparison p = 0.01). Furthermore, the EG showed a retraining of muscle activation (an increase within proper movements, with a reduction of co-contractions) and CMC (beta frequency increase within proper movements, i.e. in a framework of preserved motor coordination). The improvements in CMC, gait, balance, and muscle activation were not correlated with each other.Conclusions: Robot-aided ankle rehabilitation improved gait performance by selectively ameliorating CMC, muscle activation patterns, and, lastly, gait balance and speed. Despite CMC, gait, balance, and muscle activation were not correlated, this pilot study suggests that robot-aided ankle rehabilitation may favor a better communication between above-SCI and below-SCI structures. This communication improvement may depend on a more synchronized corticospinal output (as per CMC increase) and a better responsiveness of below-SCI motorneurons to corticospinal output (as per specific and ankle movement focused muscle activation increases at the surface EMG), thus favoring greater recruitment of spinal motor units and, ultimately, improving muscle activation pattern and strength.Significance: Adopting robot-aided ankle rehabilitation protocols for persons with iSCI in the subacute/chronic phase may allow achieving a clinically significant improvement in gait performance.

Depth and phase of respiration modulate cortico-muscular communication

Recent studies in animals have convincingly demonstrated that respiration cyclically modulates oscillatory neural activity across diverse brain areas. To what extent this generalises to humans in a way that is relevant for behaviour is yet unclear. We used magnetoencephalography (MEG) to assess the potential influence of respiration depth and respiration phase on the human motor system. We obtained simultaneous recordings of brain activity, muscle activity, and respiration while participants performed a steady contraction task. We used corticomuscular coherence as a measure of efficient long-range cortico-peripheral communication. We found coherence within the beta range over sensorimotor cortex to be reduced during voluntary deep compared to involuntary normal breathing. Moreover, beta coherence was found to be cyclically modulated by respiration phase in both conditions. Overall, these results demonstrate how respiratory rhythms influence the synchrony of brain oscillations, conceivably regulating computational efficiency through neural excitability. Intriguing questions remain with regard to the shape of these modulatory processes and how they influence perception, cognition, and behaviour.

Time-Frequency Maximal Information Coefficient Method and its Application to Functional Corticomuscular Coupling

An important challenge in the study of functional corticomuscular coupling (FCMC) is an accurate capture of the coupling relationship between the cerebral cortex and the effector muscle. The coherence method is a linear analysis method, which has certain limitations in further revealing the nonlinear coupling between neural signals. Although mutual information (MI) and transfer entropy (TE) based on information theory can capture both linear and nonlinear correlations, the equitability of these algorithms is ignored and the nonlinear components of the correlation cannot be separated. The maximal information coefficient (MIC) is a suitable method to measure the coupling between neurophysiological signals. This study extends the MIC to the time-frequency domain, named time-frequency maximal information coefficient (TFMIC), to explore the FCMC in a specific frequency band. The effectiveness, equitability, and robustness of the algorithm on the simulation data was verified and compared with coherence, TE- and MI- based methods. Simulation results showed that the TFMIC could accurately detect the coupling for different functional relationships at low noise levels. The dorsiflexion experimental results revealed that the beta-band (14-30 Hz) significant coupling was observed at channels Cz, C4, FC4, and FCz. Additionally, the results showed that the coupling was higher in the alpha-band (8-13 Hz) and beta-band (14-30 Hz) than in the gamma-band (31-45 Hz). This might be related to a transition between sensorimotor states. Specifically, the nonlinear component of FCMC was also observed at channels Cz, C4, FC4, and FCz. This study expanded the research on nonlinear coupling components in FCMC.

Visual detection is locked to the internal dynamics of cortico-motor control

Movements overtly sample sensory information, making sensory analysis an active-sensing process. In this study, we show that visual information sampling is not just locked to the (overt) movement dynamics but to the internal (covert) dynamics of cortico-motor control. We asked human participants to perform continuous isometric contraction while detecting unrelated and unpredictable near-threshold visual stimuli. The motor output (force) shows zero-lag coherence with brain activity (recorded via electroencephalography) in the beta-band, as previously reported. In contrast, cortical rhythms in the alpha-band systematically forerun the motor output by 200 milliseconds. Importantly, visual detection is facilitated when cortico-motor alpha (not beta) synchronization is enhanced immediately before stimulus onset, namely, at the optimal phase relationship for sensorimotor communication. These findings demonstrate an ongoing coupling between visual sampling and motor control, suggesting the operation of an internal and alpha-cycling visuomotor loop.

Depth and phase of respiration modulate cortico-muscular communication

Recent studies in animals have convincingly demonstrated that respiration cyclically modulates oscillatory neural activity across diverse brain areas. To what extent this generalises to humans in a way that is relevant for behaviour is yet unclear. We used magnetoencephalography (MEG) to assess the potential influence of respiration depth and respiration phase on the human motor system. We obtained simultaneous recordings of brain activity, muscle activity, and respiration while participants performed a steady contraction task. We used corticomuscular coherence as a measure of efficient long-range cortico-peripheral communication. We found coherence within the beta range over sensorimotor cortex to be reduced during voluntary deep compared to involuntary normal breathing. Moreover, beta coherence was found to be cyclically modulated by respiration phase in both conditions. Overall, these results demonstrate how respiratory rhythms influence the synchrony of brain oscillations, conceivably regulating computational efficiency through neural excitability. Intriguing questions remain with regard to the shape of these modulatory processes and how they influence perception, cognition, and behaviour.

Muscle Synergies and Coherence Networks Reflect Different Modes of Coordination During Walking

When walking speed is increased, the frequency ratio between the arm and leg swing switches spontaneously from 2:1 to 1:1. We examined whether these switches are accompanied by changes in functional connectivity between multiple muscles. Subjects walked on a treadmill with their arms swinging along their body while kinematics and surface electromyography (EMG) of 26 bilateral muscles across the body were recorded. Walking speed was varied from very slow to normal. We decomposed EMG envelopes and intermuscular coherence spectra using non-negative matrix factorization (NMF), and the resulting modes were combined into multiplex networks and analyzed for their community structure. We found five relevant muscle synergies that significantly differed in activation patterns between 1:1 and 2:1 arm-leg coordination and the transition period between them. The corresponding multiplex network contained a single module indicating pronounced muscle co-activation patterns across the whole body during a gait cycle. NMF of the coherence spectra distinguished three EMG frequency bands: 4-8, 8-22, and 22-60 Hz. The community structure of the multiplex network revealed four modules, which clustered functional and anatomical linked muscles across modes of coordination. Intermuscular coherence at 4-22 Hz between upper and lower body and within the legs was particularly pronounced for 1:1 arm-leg coordination and was diminished when switching between modes of coordination. These findings suggest that the stability of arm-leg coordination is associated with modulations in long-distant neuromuscular connectivity.

Can EEG and MEG detect signals from the human cerebellum?

The cerebellum plays a key role in the regulation of motor learning, coordination and timing, and has been implicated in sensory and cognitive processes as well. However, our current knowledge of its electrophysiological mechanisms comes primarily from direct recordings in animals, as investigations into cerebellar function in humans have instead predominantly relied on lesion, haemodynamic and metabolic imaging studies. While the latter provide fundamental insights into the contribution of the cerebellum to various cerebellar-cortical pathways mediating behaviour, they remain limited in terms of temporal and spectral resolution. In principle, this shortcoming could be overcome by monitoring the cerebellum's electrophysiological signals. Non-invasive assessment of cerebellar electrophysiology in humans, however, is hampered by the limited spatial resolution of electroencephalography (EEG) and magnetoencephalography (MEG) in subcortical structures, i.e., deep sources. Furthermore, it has been argued that the anatomical configuration of the cerebellum leads to signal cancellation in MEG and EEG. Yet, claims that MEG and EEG are unable to detect cerebellar activity have been challenged by an increasing number of studies over the last decade. Here we address this controversy and survey reports in which electrophysiological signals were successfully recorded from the human cerebellum. We argue that the detection of cerebellum activity non-invasively with MEG and EEG is indeed possible and can be enhanced with appropriate methods, in particular using connectivity analysis in source space. We provide illustrative examples of cerebellar activity detected with MEG and EEG. Furthermore, we propose practical guidelines to optimize the detection of cerebellar activity with MEG and EEG. Finally, we discuss MEG and EEG signal contamination that may lead to localizing spurious sources in the cerebellum and suggest ways of handling such artefacts. This review is to be read as a perspective review that highlights that it is indeed possible to measure cerebellum with MEG and EEG and encourages MEG and EEG researchers to do so. Its added value beyond highlighting and encouraging is that it offers useful advice for researchers aspiring to investigate the cerebellum with MEG and EEG.

Re-emergent Tremor in Parkinson's Disease: The Role of the Motor Cortex

BACKGROUND

Parkinson's disease patients may show a tremor that appears after a variable delay while the arms are kept outstretched (re-emergent tremor). The objectives of this study were to investigate re-emergent tremor pathophysiology by studying the role of the primary motor cortex in this tremor and making a comparison with rest tremor.

METHODS

We enrolled 10 Parkinson's disease patients with both re-emergent and rest tremor. Tremor was assessed by spectral analysis, corticomuscular coherence and tremor-resetting produced by transcranial magnetic stimulation over the primary motor cortex. We also recorded transcranial magnetic stimulation-evoked potentials generated by motor cortex stimulation during rest tremor, tremor suppression during wrist extension, and re-emergent tremor. Spectral analysis, corticomuscular coherence, and tremor resetting were compared between re-emergent tremor and rest tremor.

RESULTS

Re-emergent tremor showed significant corticomuscular coherence, causal relation between motor cortex activity and tremor muscle and tremor resetting. The P60 component of transcranial magnetic stimulation-evoked potentials reduced in amplitude during tremor suppression, recovered before re-emergent tremor, was facilitated at re-emergent tremor onset, and returned to values similar to those of rest tremor during re-emergent tremor. Compared with rest tremor, re-emergent tremor showed similar corticomuscular coherence and tremor resetting, but slightly higher frequency.

CONCLUSIONS

Re-emergent tremor is causally related with the activity of the primary motor cortex, which is likely a convergence node in the network that generates re-emergent tremor. Re-emergent tremor and rest tremor share common pathophysiological mechanisms in which the motor cortex plays a crucial role. © 2020 International Parkinson and Movement Disorder Society.

Ultrasensitive Magnetic Field Sensors for Biomedical Applications

The development of magnetic field sensors for biomedical applications primarily focuses on equivalent magnetic noise reduction or overall design improvement in order to make them smaller and cheaper while keeping the required values of a limit of detection. One of the cutting-edge topics today is the use of magnetic field sensors for applications such as magnetocardiography, magnetotomography, magnetomyography, magnetoneurography, or their application in point-of-care devices. This introductory review focuses on modern magnetic field sensors suitable for biomedicine applications from a physical point of view and provides an overview of recent studies in this field. Types of magnetic field sensors include direct current superconducting quantum interference devices, search coil, fluxgate, magnetoelectric, giant magneto-impedance, anisotropic/giant/tunneling magnetoresistance, optically pumped, cavity optomechanical, Hall effect, magnetoelastic, spin wave interferometry, and those based on the behavior of nitrogen-vacancy centers in the atomic lattice of diamond.