Brain–robot interface driven plasticity: Distributed modulation of corticospinal excitability

Resolving equivocal gain modulation of corticospinal excitability

The ratio between the input and output of neuronal populations, usually referred to as gain modulation, is rhythmically modulated along the oscillatory cycle. Previous research on spinal neurons, however, revealed contradictory findings: both uni- and bimodal patterns of increased responsiveness for synaptic input have been proposed for the oscillatory beta rhythm. In this study, we compared previous approaches of phase estimation directly on simulated data and empirically tested the corresponding predictions in healthy males and females. We applied single-pulse transcranial magnetic stimulation over the primary motor cortex at rest, and assessed the spinal output generated by this input. Specifically, the peak-to-peak amplitude of the motor evoked potential in the contralateral forearm was estimated as a function of the EMG phase at which the stimulus was applied. The findings indicated that human spinal neurons adhere to a unimodal pattern of increased responsiveness, and suggest that the rising phase of the upper beta band maximizes gain modulation. Importantly, a bimodal pattern of increased responsiveness was shown to result in an artifact during data analysis and filtering. This observation of invalid preprocessing could be generalized to other frequency bands (i.e., delta, theta, alpha, and gamma), different task conditions (i.e., voluntary muscle contraction), and EEG-based phase estimations. Appropriate analysis algorithms, such as broad-band filtering, enable us to accurately determine gain modulation of neuronal populations and to avoid erroneous phase estimations. This may facilitate novel phase-specific interventions for targeted neuromodulation.

An overview of methods of left and right foot motor imagery based on Tikhonov regularisation common spatial pattern

The motor imagery brain-computer interface (MI-BCI) provides an interactive control channel for spinal cord injury patients. However, the limitations of feature extraction algorithms may lead to low accuracy and instability in decoding electroencephalogram (EEG) signals. In this study, we examined the classification performance of an MI-BCI system by focusing on the distinction of the left and right foot kinaesthetic motor imagery tasks in five subjects. Feature extraction was performed using the common space pattern (CSP) and the Tikhonov regularisation CSP (TRCSP) spatial filters. TRCSP overcomes the CSP problems of noise sensitivity and overfitting. Moreover, support vector machine (SVM) and linear discriminant analysis (LDA) were used for classification and recognition. We constructed four combined classification methods (TRCSP-SVM, TRCSP-LDA, CSP-SVM, and CSP-LDA) and evaluated them by comparing their accuracies, kappa coefficients, and receiver operating characteristic (ROC) curves. The results showed that the TRCSP-SVM method performed significantly better than others (average accuracy 97%, average kappa coefficient 0.91, and average area under ROC curve (AUC) 0.98). Using TRCSP instead of standard CSP improved accuracy by up to 10%. This study provides insights into the classification of EEG signals. The results of this study can aid lower limb MI-BCI systems in rehabilitation training.

Oscillatory neurofeedback networks and poststroke rehabilitative potential in severely impaired stroke patients

Motor restoration after severe stroke is often limited. However, some of the severely impaired stroke patients may still have a rehabilitative potential. Biomarkers that identify these patients are sparse. Eighteen severely impaired chronic stroke patients with a lack of volitional finger extension participated in an EEG study. During sixty-six trials of kinesthetic motor imagery, a brain-machine interface turned event-related beta-band desynchronization of the ipsilesional sensorimotor cortex into opening of the paralyzed hand by a robotic orthosis. A subgroup of eight patients participated in a subsequent four-week rehabilitation training. Changes of the movement extent were captured with sensors which objectively quantified even discrete improvements of wrist movement. Albeit with the same motor impairment level, patients could be differentiated into two groups, i.e., with and without task-related increase of bilateral cortico-cortical phase synchronization between frontal/premotor and parietal areas. This fronto-parietal integration (FPI) was associated with a significantly higher volitional beta modulation range in the ipsilesional sensorimotor cortex. Following the four-week training, patients with FPI showed significantly higher improvement in wrist movement than those without FPI. Moreover, only the former group improved significantly in the upper extremity Fugl-Meyer-Assessment score. Neurofeedback-related long-range oscillatory coherence may differentiate severely impaired stroke patients with regard to their rehabilitative potential, a finding that needs to be confirmed in larger patient cohorts.

Spatially bivariate EEG-neurofeedback can manipulate interhemispheric inhibition

Human behavior requires inter-regional crosstalk to employ the sensorimotor processes in the brain. Although external neuromodulation techniques have been used to manipulate interhemispheric sensorimotor activity, a central controversy concerns whether this activity can be volitionally controlled. Experimental tools lack the power to up- or down-regulate the state of the targeted hemisphere over a large dynamic range and, therefore, cannot evaluate the possible volitional control of the activity. We addressed this difficulty by using the recently developed method of spatially bivariate electroencephalography (EEG)-neurofeedback to systematically enable the participants to modulate their bilateral sensorimotor activities. Here, we report that participants learn to up- and down-regulate the ipsilateral excitability to the imagined hand while maintaining constant contralateral excitability; this modulates the magnitude of interhemispheric inhibition (IHI) assessed by the paired-pulse transcranial magnetic stimulation (TMS) paradigm. Further physiological analyses revealed that the manipulation capability of IHI magnitude reflected interhemispheric connectivity in EEG and TMS, which was accompanied by intrinsic bilateral cortical oscillatory activities. Our results show an interesting approach for neuromodulation, which might identify new treatment opportunities, e.g., in patients suffering from a stroke.

Decoding personalized motor cortical excitability states from human electroencephalography

Brain state-dependent transcranial magnetic stimulation (TMS) requires real-time identification of cortical excitability states. Current approaches deliver TMS during brain states that correlate with motor cortex (M1) excitability at the group level. Here, we hypothesized that machine learning classifiers could successfully discriminate between high and low M1 excitability states in individual participants using information obtained from low-density electroencephalography (EEG) signals. To test this, we analyzed a publicly available dataset that delivered 600 single TMS pulses to the right M1 during EEG and electromyography (EMG) recordings in 20 healthy adults. Multivariate pattern classification was used to discriminate between brain states during which TMS evoked small and large motor-evoked potentials (MEPs). Results show that personalized classifiers successfully discriminated between low and high M1 excitability states in 80% of tested participants. MEPs elicited during classifier-predicted high excitability states were significantly larger than those elicited during classifier-predicted low excitability states in 90% of tested participants. Personalized classifiers did not generalize across participants. Overall, results show that individual participants exhibit unique brain activity patterns which predict low and high M1 excitability states and that these patterns can be efficiently captured using low-density EEG signals. Our findings suggest that deploying individualized classifiers during brain state-dependent TMS may enable fully personalized neuromodulation in the future.

Impaired phase synchronization of motor-evoked potentials reflects the degree of motor dysfunction in the lesioned human brain

The functional corticospinal integrity (CSI) can be indexed by motor-evoked potentials (MEP) following transcranial magnetic stimulation of the motor cortex. Glial brain tumors in motor-eloquent areas are frequently disturbing CSI resulting in different degrees of motor dysfunction. However, this is unreliably mirrored by MEP characteristics. In 59 consecutive patients with diffuse glial tumors and 21 healthy controls (CTRL), we investigated the conventional MEP features, that is, resting motor threshold (RMT), amplitudes and latencies. In addition, frequency-domain MEP features were analyzed to estimate the event-related spectral perturbation (ERSP), and the induced phase synchronization by intertrial coherence (ITC). The clinical motor status was captured including the Medical Research Council Scale (MRCS), the Grooved Pegboard Test (GPT), and the intake of antiepileptic drugs (AED). Motor function was classified according to MRCS and GPT as no motor deficit (NMD), fine motor deficits (FMD) and gross motor deficits (GMD). CSI was assessed by diffusion-tensor imaging (DTI). Motor competent subjects (CTRL and NMD) had similar ERSP and ITC values. The presence of a motor deficit (FMD and GMD) was associated with an impairment of high-frequency ITC (150-300 Hz). GMD and damage to the CSI demonstrated an additional reduction of high-frequency ERSP (150-300 Hz). GABAergic AED increased ERSP but not ITC. Notably, groups were indistinguishable based on conventional MEP features. Estimating MEP phase synchronization provides information about the corticospinal transmission after transcranial magnetic stimulation and reflects the degree of motor impairment that is not captured by conventional measures.

Recent Advances and Current Trends in Brain-Computer Interface (BCI) Research and Their Applications

Brain-Computer Interface (BCI) provides direct communication between the brain and an external device. BCI systems have become a trendy field of research in recent years. These systems can be used in a variety of applications to help both disabled and healthy people. Concerning significant BCI progress, we may assume that these systems are not very far from real-world applications. This review has taken into account current trends in BCI research. In this survey, one hundred most cited articles from the WOS database were selected over the last four years. This survey is divided into several sectors. These sectors are Medicine, Communication and Control, Entertainment, and Other BCI applications. The application area, recording method, signal acquisition types, and countries of origin have been identified in each article. This survey provides an overview of the BCI articles published from 2016 to 2020 and their current trends and advances in different application areas.

Interhemispheric differences in time-frequency representation of motor evoked potentials in brain tumor patients

OBJECTIVE

Conventional time-series parameters are unreliable descriptors of motor-evoked potentials (MEPs) in brain tumor patients. Frequency domain analysis is suggested to provide additional information about the status of the cortico-spinal motor system. Aim of the present study was to describe the time-frequency representation of MEPs and its relation to the motor performance.

METHODS

This study enrolled 17 consecutive brain tumor patients with impaired dexterity. After brain mapping of the affected (AH) and non-affected (NAH) hemisphere, TMS was applied to the hotspots of the abductor pollicis brevis muscles (APB). Using a Morlet wavelet approach, event-related spectral perturbation (ERSP) and inter-trial coherence (ITC) of the MEPs were calculated and compared to the Grooved Pegboard Test (GPT). Additionally, inter- and intra-subject reliability was assessed by the intraclass correlation coefficient (ICC).

RESULTS

MEPs were projecting to a frequency band between 30 and 400 Hz with a local maximum between 100 and 150 Hz. There was a significant ERSP and ITC reduction of the AH in comparison to the NAH. In contrast, no interhemispheric differences were depicted in the conventional time-series analysis. ERSP and ITC values correlated significantly with GPT results (r = 0.35 and r = 0.50). Time-frequency MEP description had good inter-and intra-subject reliability (ICC = 0.63).

CONCLUSIONS

Brain tumors affect corticospinal transmission resulting in a reduction of temporal and spectral MEP synchronization correlating with the dexterity performance.

SIGNIFICANCE

Time-frequency representation of MEPs provide additional information beyond conventional time-domain features.

Repetitive Transcranial Magnetic Stimulation for Tinnitus Treatment in Vestibular Schwannoma: A Pilot Study

Background: Vestibular schwannomas (VS) are brain tumors affecting the vestibulocochlear nerve. Thus, VS patients suffer from tinnitus (TN). While the pathophysiology is mainly unclear, there is an increasing interest in repetitive transcranial magnetic stimulation (rTMS) for TN treatment. However, the results have been divergent. In addition to the methodological aspects, the heterogeneity of the patients might affect the outcome. Yet, there is no study evaluating rTMS exclusively in VS-associated tinnitus. Thus, the present pilot study evaluates low-frequency rTMS to the right dorsolateral pre-frontal cortex (DLPFC) in a VS-associated tinnitus.

Methods: This prospective pilot study enrolled nine patients with a monoaural VS-associated tinnitus ipsilateral to the tumor. Patients were treated with a 10-day rTMS regime (1 Hz, 100% RMT, 1,200 pulses, right DLPFC). The primary endpoint of the study was the reduction of TN distress (according to the Tinnitus Handicap Inventory, THI). The secondary endpoint was a reduction of TN intensity (according to the Tinnitus Matching Test, TMT) and the evaluation of factors predicting tinnitus outcome (i.e., hearing impairment, TN duration, type of tinnitus).

Results: No complications or side effects occurred. There was one drop-out due to a non-responsiveness of the complaint. There was a significant acute effect of rTMS on the THI and TMT. However, there was no significant long-term effect after 4 weeks. While the THI failed to detect any clinically relevant acute effect of rTMS in 56% of the patients, TMT revealed a reduction of TN intensity for more than 20 in 89% and for more than 50 in 56% of the patients. Notably, the acute effect of rTMS was influenced by the TN type and duration. In general, patients with a tonal TN and shorter TN duration showed a better response to the rTMS therapy.

Conclusion: The present pilot study is the first one to exclusively evaluate the effect of low-frequency rTMS to the right DLPFC in a VS-associated tinnitus. Our results prove the feasibility and the efficacy of rTMS in this patient cohort. There is a significant acute but a limited long-term effect. In addition, there is evidence that patients with a tonal tinnitus and shorter tinnitus duration might have the strongest benefit. A larger, randomized controlled study is necessary to prove these initial findings.

Peripheral Electrical Stimulation Modulates Cortical Beta-Band Activity

Low-frequency peripheral electrical stimulation using a matrix electrode (PEMS) modulates spinal nociceptive pathways. However, the effects of this intervention on cortical oscillatory activity have not been assessed yet. The aim of this study was to investigate the effects of low-frequency PEMS (4 Hz) on cortical oscillatory activity in different brain states in healthy pain-free participants. In experiment 1, PEMS was compared to sham stimulation. In experiment 2, motor imagery (MI) was used to modulate the sensorimotor brain state. PEMS was applied either during MI-induced oscillatory desynchronization (concurrent PEMS) or after MI (delayed PEMS) in a cross-over design. For both experiments, PEMS was applied on the left forearm and resting-state electroencephalography (EEG) was recording before and after each stimulation condition. Experiment 1 showed a significant decrease of global resting-state beta power after PEMS compared to sham (p = 0.016), with a median change from baseline of −16% for PEMS and −0.54% for sham. A cluster-based permutation test showed a significant difference in resting-state beta power comparing pre- and post-PEMS (p = 0.018) that was most pronounced over bilateral central and left frontal sensors. Experiment 2 did not identify a significant difference in the change from baseline of global EEG power for concurrent PEMS compared to delayed PEMS. Two cluster-based permutation tests suggested that frontal beta power may be increased following both concurrent and delayed PEMS. This study provides novel evidence for supraspinal effects of low-frequency PEMS and an initial indication that the presence of a cognitive task such as MI may influence the effects of PEMS on beta activity. Chronic pain has been associated with changes in beta activity, in particular an increase of beta power in frontal regions. Thus, brain state-dependent PEMS may offer a novel approach to the treatment of chronic pain. However, further studies are warranted to investigate optimal stimulation conditions to achieve a reduction of pain.

Brain State-dependent Gain Modulation of Corticospinal Output in the Active Motor System

The communication through coherence hypothesis suggests that only coherently oscillating neuronal groups can interact effectively and predicts an intrinsic response modulation along the oscillatory rhythm. For the motor cortex (MC) at rest, the oscillatory cycle has been shown to determine the brain's responsiveness to external stimuli. For the active MC, however, the demonstration of such a phase-specific modulation of corticospinal excitability (CSE) along the rhythm cycle is still missing. Motor evoked potentials in response to transcranial magnetic stimulation (TMS) over the MC were used to probe the effect of cortical oscillations on CSE during several motor conditions. A brain-machine interface (BMI) with a robotic hand orthosis allowed investigating effects of cortical activity on CSE without the confounding effects of voluntary muscle activation. Only this BMI approach (and not active or passive hand opening alone) revealed a frequency- and phase-specific cortical modulation of CSE by sensorimotor beta-band activity that peaked once per oscillatory cycle and was independent of muscle activity. The active MC follows an intrinsic response modulation in accordance with the communication through coherence hypothesis. Furthermore, the BMI approach may facilitate and strengthen effective corticospinal communication in a therapeutic context, for example, when voluntary hand opening is no longer possible after stroke.

Time-Frequency Representation of Motor Evoked Potentials in Brain Tumor Patients

Background: The integrity of the motor system can be examined by applying navigated transcranial magnetic stimulation (nTMS) to the cortex. The corresponding motor-evoked potentials (MEPs) in the target muscles are mirroring the status of the human motor system, far beyond corticospinal integrity. Commonly used time domain features of MEPs (e.g., peak-to-peak amplitudes and onset latencies) exert a high inter-subject and intra-subject variability. Frequency domain analysis might help to resolve or quantify disease-related MEP changes, e.g., in brain tumor patients. The aim of the present study was to describe the time-frequency representation of MEPs in brain tumor patients, its relation to clinical and imaging findings, and the differences to healthy subject.

Methods: This prospective study compared 12 healthy subjects with 12 consecutive brain tumor patients (with and without a paresis) applying nTMS mapping. Resulting MEPs were evaluated in the time series domain (i.e., amplitudes and latencies). After transformation into the frequency domain using a Morlet wavelet approach, event-related spectral perturbation (ERSP), and inter-trial coherence (ITC) were calculated and compared to diffusion tensor imaging (DTI) results.

Results: There were no significant differences in the time series characteristics between groups. MEPs were projecting to a frequency band between 30 and 300 Hz with a local maximum around 100 Hz for both healthy subjects and patients. However, there was ERSP reduction for higher frequencies (>100 Hz) in patients in contrast to healthy subjects. This deceleration was mirrored in an increase of the inter-peak MEP latencies. Patients with a paresis showed an additional disturbance in ITC in these frequencies. There was no correlation between the CST integrity (as measured by DTI) and the MEP parameters.

Conclusion: Time-frequency analysis may provide additional information above and beyond classical MEP time domain features and the status of the corticospinal system in brain tumor patients. This first evaluation indicates that brain tumors might affect cortical physiology and the responsiveness of the cortex to TMS resulting in a temporal dispersion of the corticospinal transmission.

Real-Time Implementation of EEG Oscillatory Phase-Informed Visual Stimulation Using a Least Mean Square-Based AR Model

It is a technically challenging problem to assess the instantaneous brain state using electroencephalography (EEG) in a real-time closed-loop setup because the prediction of future signals is required to define the current state, such as the instantaneous phase and amplitude. To accomplish this in real-time, a conventional Yule–Walker (YW)-based autoregressive (AR) model has been used. However, the brain state-dependent real-time implementation of a closed-loop system employing an adaptive method has not yet been explored. Our primary purpose was to investigate whether time-series forward prediction using an adaptive least mean square (LMS)-based AR model would be implementable in a real-time closed-loop system or not. EEG state-dependent triggers synchronized with the EEG peaks and troughs of alpha oscillations in both an open-eyes resting state and a visual task. For the resting and visual conditions, statistical results showed that the proposed method succeeded in giving triggers at a specific phase of EEG oscillations for all participants. These individual results showed that the LMS-based AR model was successfully implemented in a real-time closed-loop system targeting specific phases of alpha oscillations and can be used as an adaptive alternative to the conventional and machine-learning approaches with a low computational load.

State-Dependent Gain Modulation of Spinal Motor Output

Afferent somatosensory information plays a crucial role in modulating efferent motor output. A better understanding of this sensorimotor interplay may inform the design of neurorehabilitation interfaces. Current neurotechnological approaches that address motor restoration after trauma or stroke combine motor imagery (MI) and contingent somatosensory feedback, e.g., via peripheral stimulation, to induce corticospinal reorganization. These interventions may, however, change the motor output already at the spinal level dependent on alterations of the afferent input. Neuromuscular electrical stimulation (NMES) was combined with measurements of wrist deflection using a kinematic glove during either MI or rest. We investigated 360 NMES bursts to the right forearm of 12 healthy subjects at two frequencies (30 and 100 Hz) in random order. For each frequency, stimulation was assessed at nine intensities. Measuring the induced wrist deflection across different intensities allowed us to estimate the input-output curve (IOC) of the spinal motor output. MI decreased the slope of the IOC independent of the stimulation frequency. NMES with 100 Hz vs. 30 Hz decreased the threshold of the IOC. Human-machine interfaces for neurorehabilitation that combine MI and NMES need to consider bidirectional communication and may utilize the gain modulation of spinal circuitries by applying low-intensity, high-frequency stimulation.

Brain-Machine Neurofeedback: Robotics or Electrical Stimulation?

Neurotechnology such as brain-machine interfaces (BMI) are currently being investigated as training devices for neurorehabilitation, when active movements are no longer possible. When the hand is paralyzed following a stroke for example, a robotic orthosis, functional electrical stimulation (FES) or their combination may provide movement assistance; i.e., the corresponding sensory and proprioceptive neurofeedback is given contingent to the movement intention or imagination, thereby closing the sensorimotor loop. Controlling these devices may be challenging or even frustrating. Direct comparisons between these two feedback modalities (robotics vs. FES) with regard to the workload they pose for the user are, however, missing. Twenty healthy subjects controlled a BMI by kinesthetic motor imagery of finger extension. Motor imagery-related sensorimotor desynchronization in the EEG beta frequency-band (17–21 Hz) was turned into passive opening of the contralateral hand by a robotic orthosis or FES in a randomized, cross-over block design. Mental demand, physical demand, temporal demand, performance, effort, and frustration level were captured with the NASA Task Load Index (NASA-TLX) questionnaire by comparing these workload components to each other (weights), evaluating them individually (ratings), and estimating the respective combinations (adjusted workload ratings). The findings were compared to the task-related aspects of active hand movement with EMG feedback. Furthermore, both feedback modalities were compared with regard to their BMI performance. Robotic and FES feedback had similar workloads when weighting and rating the different components. For both robotics and FES, mental demand was the most relevant component, and higher than during active movement with EMG feedback. The FES task led to significantly more physical (p = 0.0368) and less temporal demand (p = 0.0403) than the robotic task in the adjusted workload ratings. Notably, the FES task showed a physical demand 2.67 times closer to the EMG task, but a mental demand 6.79 times closer to the robotic task. On average, significantly more onsets were reached during the robotic as compared to the FES task (17.22 onsets, SD = 3.02 vs. 16.46, SD = 2.94 out of 20 opportunities; p = 0.016), even though there were no significant differences between the BMI classification accuracies of the conditions (p = 0.806; CI = −0.027 to −0.034). These findings may inform the design of neurorehabilitation interfaces toward human-centered hardware for a more natural bidirectional interaction and acceptance by the user.

Induction of plasticity in the human motor system by motor imagery and transcranial magnetic stimulation

KEY POINTS

Delivering transcranial magnetic brain stimulation over the motor cortex during motor imagination leads to enhanced motor output, which is selective for the muscles primarily involved in the imagined movement. This novel protocol may be useful to enhance function after damage to the motor system, such as after stroke.

ABSTRACT

Several paired stimulation paradigms are known to induce plasticity in the motor cortex, reflected by changes in the motor evoked potential (MEP) following the paired stimulation. Motor imagery (MI) is capable of activating the motor system and affecting cortical excitability. We hypothesized that it might be possible to use MI in conjunction with transcranial magnetic stimulation (TMS) to induce plasticity in the human motor system. TMS was delivered to the motor cortex of healthy human subjects, and baseline MEPs recorded from forearm flexor, forearm extensor and intrinsic hand muscles. Subjects were then asked to imagine either wrist flexion or extension movements during TMS delivery (n = 90 trials). Immediately after this intervention, MEP measurement was repeated. Control protocols tested the impact of imagination or TMS alone. Flexion imagination with TMS increased MEPs in flexors and an intrinsic hand muscle. Extensor imagination with TMS increased MEPs in extensor muscles only. The control paradigms did not produce significant changes. We conclude that delivering TMS during MI is capable of inducing plastic changes in the motor system. This new protocol may find utility to enhance functional rehabilitation after brain injury.

Topics and trends in artificial intelligence assisted human brain research

Artificial intelligence (AI) assisted human brain research is a dynamic interdisciplinary field with great interest, rich literature, and huge diversity. The diversity in research topics and technologies keeps increasing along with the tremendous growth in application scope of AI-assisted human brain research. A comprehensive understanding of this field is necessary to assess research efficacy, (re)allocate research resources, and conduct collaborations. This paper combines the structural topic modeling (STM) with the bibliometric analysis to automatically identify prominent research topics from the large-scale, unstructured text of AI-assisted human brain research publications in the past decade. Analyses on topical trends, correlations, and clusters reveal distinct developmental trends of these topics, promising research orientations, and diverse topical distributions in influential countries/regions and research institutes. These findings help better understand scientific and technological AI-assisted human brain research, provide insightful guidance for resource (re)allocation, and promote effective international collaborations.

Haptic Adaptive Feedback to Promote Motor Learning With a Robotic Ankle Exoskeleton Integrated With a Video Game

Background: Robotic devices have been used to rehabilitate walking function after stroke. Although results suggest that post-stroke patients benefit from this non-conventional therapy, there is no agreement on the optimal robot-assisted approaches to promote neurorecovery. Here we present a new robotic therapy protocol using a grounded exoskeleton perturbing the ankle joint based on tacit learning control. Method: Ten healthy individuals and a post-stroke patient participated in the study and were enrolled in a pilot intervention protocol that involved performance of ankle movements following different trajectories via video game visual feedback. The system autonomously modulated task difficulty according to the performance to increase the challenge. We hypothesized that motor learning throughout training sessions would lead to increased corticospinal excitability of dorsi-plantarflexor muscles. Transcranial Magnetic Stimulation was used to assess the effects on corticospinal excitability. Results: Improvements have been observed on task performance and motor outcomes in both healthy individuals and post-stroke patient case study. Tibialis Anterior corticospinal excitability increased significantly after the training; however no significant changes were observed on Soleus corticospinal excitability. Clinical scales showed functional improvements in the stroke patient. Discussion and Significance: Our findings both in neurophysiological and performance assessment suggest improved motor learning. Some limitations of the study include treatment duration and intensity, as well as the non-significant changes in corticospinal excitability obtained for Soleus. Nonetheless, results suggest that this robotic training framework is a potentially interesting approach that can be explored for gait rehabilitation in post-stroke patients.

Brain-computer interfaces in neurologic rehabilitation practice

The brain-computer interfaces (BCIs) for neurologic rehabilitation are based on the assumption that by retraining the brain to specific activities, an ultimate improvement of function can be expected. In this chapter, we review the present status, key determinants, and future directions of the clinical use of BCI in neurorehabilitation. The recent advancements in noninvasive BCIs as a therapeutic tool to promote functional motor recovery by inducing neuroplasticity are described, focusing on stroke as it represents the major cause of long-term motor disability. The relevance of recent findings on BCI use in spinal cord injury beyond the control of neuroprosthetic devices to restore motor function is briefly discussed. In a dedicated section, we examine the potential role of BCI technology in the domain of cognitive function recovery by instantiating BCIs in the long history of neurofeedback and some emerging BCI paradigms to address cognitive rehabilitation are highlighted. Despite the knowledge acquired over the last decade and the growing number of studies providing evidence for clinical efficacy of BCI in motor rehabilitation, an exhaustive deployment of this technology in clinical practice is still on its way. The pipeline to translate BCI to clinical practice in neurorehabilitation is the subject of this chapter.

Corticospinal excitability modulation by pairing peripheral nerve stimulation with cortical states of movement initiation

KEY POINTS

We compare the effects on corticospinal excitability of repeatedly delivering peripheral nerve stimulation at three time points (-30 ms, 0 ms, +50 ms) relative to muscle onset in a cue-guided task. Plastic changes in excitability are only observed when stimuli are delivered immediately before the time when muscles activate, while stimuli delivered at muscle onset or shortly later (0, +50 ms) have no effect. Plastic effects are abolished if there is ongoing volitional electromyogram activity in the muscles prior to the onset of the phasic contraction. The plastic effects induced by timing peripheral stimulation relative to electromyographic markers of muscle activation are as effective as those that occur if stimulation is timed relative to electroencephalographic markers of motor cortical activation. We provide a simple alternative protocol to induce plasticity in people in whom electroencephalogram recording is difficult.

ABSTRACT

Plastic changes in corticospinal excitability (CSE) and motor function can be induced in a targeted and long-term manner if afferent volleys evoked by peripheral nerve stimulation are repeatedly associated with the peak of premovement brain activity assessed with an electroencephalogram (EEG). The present study investigated whether other factors might also characterize this optimal brain state for plasticity induction. In healthy human volunteers (n = 24), we found that the same reliable changes in CSE can be induced by timing peripheral afferent stimulation relative to the onset of electromyogram (EMG) activity rather than using the EEG peak. Specifically, we observed an increase in CSE when peripheral stimulation activated the cortex just before movement initiation. By contrast, there was no effect on CSE if the afferent input reached the cortex at the same time or after EMG onset, consistent with the idea that the temporal order of synaptic activation from afferent input and voluntary movement is important for production of plasticity. Finally, in 14 volunteers, we found that background voluntary muscle activity prior to movement also abolished the effect on CSE. One possible explanation is that the intervention strengthens synapses that are inactive at rest but change their activity in anticipation of movement, and that the intervention fails when the synapses are tonically active during background EMG activity. Overall, we demonstrate that, in individuals with voluntary control of muscles targeted by our intervention, EMG signals are a suitable alternative to an EEG for inducing plasticity by coupling movement-related brain states with peripheral afferent input.

Different oscillatory entrainment of cortical networks during motor imagery and neurofeedback in right and left handers

Volitional modulation and neurofeedback of sensorimotor oscillatory activity is currently being evaluated as a strategy to facilitate motor restoration following stroke. Knowledge on the interplay between this regional brain self-regulation, distributed network entrainment and handedness is, however, limited. In a randomized cross-over design, twenty-one healthy subjects (twelve right-handers [RH], nine left-handers [LH]) performed kinesthetic motor imagery of left (48 trials) and right finger extension (48 trials). A brain-machine interface turned event-related desynchronization in the beta frequency-band (16-22 Hz) during motor imagery into passive hand opening by a robotic orthosis. Thereby, every participant subsequently activated either the dominant (DH) or non-dominant hemisphere (NDH) to control contralateral hand opening. The task-related cortical networks were studied with electroencephalography. The magnitude of the induced oscillatory modulation range in the sensorimotor cortex was independent of both handedness (RH, LH) and hemispheric specialization (DH, NDH). However, the regional beta-band modulation was associated with different alpha-band networks in RH and LH: RH presented a stronger inter-hemispheric connectivity, while LH revealed a stronger intra-hemispheric interaction. Notably, these distinct network entrainments were independent of hemispheric specialization. In healthy subjects, sensorimotor beta-band activity can be robustly modulated by motor imagery and proprioceptive feedback in both hemispheres independent of handedness. However, right and left handers show different oscillatory entrainment of cortical alpha-band networks during neurofeedback. This finding may inform neurofeedback interventions in future to align them more precisely with the underlying physiology.

Distinct Beta-band Oscillatory Circuits Underlie Corticospinal Gain Modulation

Rhythmic synchronization of neurons is known to affect neuronal interactions. In the motor system, oscillatory power fluctuations modulate corticospinal excitability. However, previous research addressing phase-specific gain modulation in the motor system has resulted in contradictory findings. It remains unclear how many time windows of increased responsiveness each oscillatory cycle provides. Moreover, we still lack conclusive evidence as to whether the motor cortex entails an intrinsic response modulation along the rhythm cycle, as shown for spinal neurons. We investigated this question with single-pulse transcranial magnetic stimulation over the primary motor cortex at rest. Application of near-motor threshold stimuli revealed a frequency- and phase-specific gain modulation at both cortical and spinal level, independent of the spontaneous oscillatory power fluctuations at each level. We detected bilateral sensorimotor circuits in the lower beta-band (14–17 Hz) and unilateral corticospinal circuits in the upper beta-band (20–24 Hz). These findings provide novel evidence that intrinsic activity in the human motor cortex modulates input gain along the beta oscillatory cycle within distinct circuits. In accordance with periodic alternations of synchronous hyper- and depolarization, increased neuronal responsiveness occurred once per oscillatory beta cycle. This information may lead to new brain state-dependent and circuit-specific interventions for targeted neuromodulation.

Combined endogenous and exogenous disinhibition of intracortical circuits augments plasticity induction in the human motor cortex

BACKGROUND

Motor imagery (MI) engages cortical areas in the human brain similar to motor practice. Corticospinal excitability (CSE) is facilitated during but not after MI practice. We hypothesized that lasting CSE changes could be achieved by associatively pairing this endogenous modulation with exogenous stimulation of the same intracortical circuits.

METHODS

We combined MI with a disinhibition protocol (DIS) targeting intracortical circuits by paired-pulse repetitive transcranial magnetic stimulation in one main and three subsequent experiments. The follow-up experiments were applied to increase effects, e.g., by individualizing inter-stimulus intervals, adding neuromuscular stimulation and expanding the intervention period. CSE was captured during (online) and after (offline) the interventions via input-output changes and cortical maps of motor evoked potentials. A total of 35 healthy subjects (mean age 26.1 ± 2.6 years, 20 females) participated in this study.

RESULTS

A short intervention (48 stimuli within ∼90s) increased CSE. This plasticity developed rapidly, was associative (with MI_on, but not MI_off or REST) and persisted beyond the intervention period. Follow-up experiments revealed the relevance of individualizing inter-stimulus intervals and of consistent inter-burst periods for online and offline effects, respectively. Expanding this combined MI/DIS intervention to 480 stimuli amplified the sustainability of CSE changes. When concurrent neuromuscular electrical stimulation was applied, the plasticity induction was cancelled.

CONCLUSIONS

This novel associative stimulation protocol augmented plasticity induction in the human motor cortex within a remarkably short period of time and in the absence of active movements. The combination of endogenous and exogenous disinhibition of intracortical circuits may provide a therapeutic backdoor when active movements are no longer possible, e.g., for hand paralysis after stroke.

Reaction Time Predicts Brain-Computer Interface Aptitude

There is evidence that 15-30% of the general population cannot effectively operate brain-computer interfaces (BCIs). Thus the BCI performance predictors are critically required to pre-screen participants. Current neurophysiological and psychological tests either require complicated equipment or suffer from subjectivity. Thus, a simple and objective BCI performance predictor is desirable. Neurofeedback (NFB) training involves performing a cognitive task (motor imagery) instructed via sensory stimuli and re-adjusted through ongoing real-time feedback. A simple reaction time (SRT) test reflects the time required for a subject to respond to a defined stimulus. Thus, we postulated that individuals with shorter reaction times operate a BCI with rapidly updated feedback better than individuals with longer reaction times. Furthermore, we investigated how changing the feedback update interval (FUI), i.e., modification of the feedback provision frequency, affects the correlation between the SRT and BCI performance. Ten participants attended four NFB sessions with FUIs of 16, 24, 48, and 96 ms in a randomized order. We found that: 1) SRT is correlated with the BCI performance with FUIs of 16 and 96 ms; 2) good and poor performers elicit stronger ERDs and control BCIs more effectively (i.e., produced larger information transfer rates) with 16 and 96 ms FUIs, respectively. Our findings suggest that SRT may be used as a simple and objective surrogate for BCI aptitude with FUIs of 16 and 96 ms. It also implies that the FUI customization according to participants SRT measure may enhance the BCI performance.

Precise estimation of human corticospinal excitability associated with the levels of motor imagery-related EEG desynchronization extracted by a locked-in amplifier algorithm

BACKGROUND

Physical motor exercise aided by an electroencephalogram (EEG)-based brain-computer interface (BCI) is known to improve motor recovery in patients with stroke. In such a BCI paradigm, event-related desynchronization (ERD) in the alpha and beta bands extracted from EEG recorded over the primary sensorimotor area (SM1) is often used, since ERD has been suggested to be associated with an increase of corticospinal excitability. Recently, we demonstrated a novel online lock-in amplifier (LIA) algorithm to estimate the amplitude modulation of motor-related SM1 ERD. With this algorithm, the delay time, accuracy, and stability to estimate motor-related SM1 ERD were significantly improved compared with the conventional fast Fourier transformation (FFT) algorithm. These technical improvements to extract an ERD trace imply a potential advantage for a better trace of the excitatory status of the SM1 in a BCI context. Therefore, the aim of this study was to assess the precision of LIA-based ERD tracking for estimation of corticospinal excitability using a transcranial magnetic stimulation (TMS) paradigm.

METHODS

The motor evoked potentials (MEPs) induced by single-pulse TMS over the primary motor cortex depending on the magnitudes of SM1 ERD (i.e., 35% and 70%) extracted by the online LIA or FFT algorithm were monitored during a motor imagery task of wrist extension in 17 healthy participants. Then, the peak-to-peak amplitudes of MEPs and their variabilities were assessed to investigate the precision of the algorithms.

RESULTS

We found greater MEP amplitude evoked by single-pulse TMS triggered by motor imagery-related alpha SM1 ERD than at rest. This enhancement was associated with the magnitude of ERD in both FFT and LIA algorithms. Moreover, we found that the variabilities of peak-to-peak MEP amplitudes at 35% and 70% ERDs calculated by the novel online LIA algorithm were smaller than those extracted using the conventional FFT algorithm.

CONCLUSIONS

The present study demonstrated that the calculation of motor imagery-related SM1 ERDs using the novel online LIA algorithm led to a more precise estimation of corticospinal excitability than when the ordinary FFT-based algorithm was used.

Muscle-selective disinhibition of corticomotor representations using a motor imagery-based brain-computer interface

Bridging between brain activity and machine control, brain-computer interface (BCI) can be employed to activate distributed neural circuits implicated in a specific aspect of motor control. Using a motor imagery-based BCI paradigm, we previously found a disinhibition within the primary motor cortex contralateral to the imagined movement, as evidenced by event-related desynchronization (ERD) of oscillatory cortical activity. Yet it is unclear whether this BCI approach does selectively facilitate corticomotor representations targeted by the imagery. To address this question, we used brain state-dependent transcranial magnetic stimulation while participants performed kinesthetic motor imagery of wrist movements with their right hand and received online visual feedback of the ERD. Single and paired-pulse magnetic stimulation were given to the left primary motor cortex at a low or high level of ERD to assess intracortical excitability. While intracortical facilitation showed no modulation by ERD, short-latency intracortical inhibition was reduced the higher the ERD. Intracortical disinhibition was only found in the agonist muscle targeted by motor imagery at high ERD level, but not in the antagonist muscle. Single pulse motor-evoked potential was also increased the higher the ERD. However, at high ERD level, this facilitatory effect on overall corticospinal excitability was not selective to the agonist muscle. Analogous results were found in two independent experiments, in which participants either performed kinesthetic motor imagery of wrist extension or flexion. Our results showed that motor imagery-based BCI can selectively disinhibit the corticomotor output to the agonist muscle, enabling effector-specific training in patients with motor paralysis.

Modulating functional connectivity after stroke with neurofeedback: Effect on motor deficits in a controlled cross-over study

Synchronization of neural activity as measured with functional connectivity (FC) is increasingly used to study the neural basis of brain disease and to develop new treatment targets. However, solid evidence for a causal role of FC in disease and therapy is lacking. Here, we manipulated FC of the ipsilesional primary motor cortex in ten chronic human stroke patients through brain-computer interface technology with visual neurofeedback. We conducted a double-blind controlled crossover study to test whether manipulation of FC through neurofeedback had a behavioral effect on motor performance. Patients succeeded in increasing FC in the motor cortex. This led to improvement in motor function that was significantly greater than during neurofeedback training of a control brain area and proportional to the degree of FC enhancement. This result provides evidence that FC has a causal role in neurological function and that it can be effectively targeted with therapy.

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Stroke patients participated in clinical trial on neurofeedback of functional connectivity.

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Patients learned to enhance synchrony of neural activity in their motor cortex.

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This led to reduced motor impairment.

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Evidence for a causal role of neural synchrony in neurological deficits and recovery.

Recruitment of Additional Corticospinal Pathways in the Human Brain with State-Dependent Paired Associative Stimulation

Standard brain stimulation protocols modify human motor cortex excitability by modulating the gain of the activated corticospinal pathways. However, the restoration of motor function following lesions of the corticospinal tract requires also the recruitment of additional neurons to increase the net corticospinal output. For this purpose, we investigated a novel protocol based on brain state-dependent paired associative stimulation.Motor imagery (MI)-related electroencephalography was recorded in healthy males and females for brain state-dependent control of both cortical and peripheral stimulation in a brain-machine interface environment. State-dependency was investigated with concurrent, delayed, and independent stimulation relative to the MI task. Specifically, sensorimotor event-related desynchronization (ERD) in the β-band (16-22 Hz) triggered peripheral stimulation through passive hand opening by a robotic orthosis and transcranial magnetic stimulation to the respective cortical motor representation, either synchronously or subsequently. These MI-related paradigms were compared with paired cortical and peripheral input applied independent of the brain state. Cortical stimulation resulted in a significant increase in corticospinal excitability only when applied brain state-dependently and synchronously to peripheral input. These gains were resistant to a depotentiation task, revealed a nonlinear evolution of plasticity, and were mediated via the recruitment of additional corticospinal neurons rather than via synchronization of neuronal firing. Recruitment of additional corticospinal pathways may be achieved when cortical and peripheral inputs are applied concurrently, and during β-ERD. These findings resemble a gating mechanism and are potentially important for developing closed-loop brain stimulation for the treatment of hand paralysis following lesions of the corticospinal tract.SIGNIFICANCE STATEMENT The activity state of the motor system influences the excitability of corticospinal pathways to external input. State-dependent interventions harness this property to increase the connectivity between motor cortex and muscles. These stimulation protocols modulate the gain of the activated pathways, but not the overall corticospinal recruitment. In this study, a brain-machine interface paired peripheral stimulation through passive hand opening with transcranial magnetic stimulation to the respective cortical motor representation during volitional β-band desynchronization. Cortical stimulation resulted in the recruitment of additional corticospinal pathways, but only when applied brain state-dependently and synchronously to peripheral input. These effects resemble a gating mechanism and may be important for the restoration of motor function following lesions of the corticospinal tract.

Sensorimotor Integration During Motor Learning: Transcranial Magnetic Stimulation Studies

The effect of sensory signals coming from skin and muscle afferents on the sensorimotor cortical networks is entitled as sensory-motor integration (SMI). SMI can be studied electrophysiologically by the motor cortex excitability changes in response to peripheral sensory stimulation. These changes include the periods of short afferent inhibition (SAI), afferent facilitation (AF), and late afferent inhibition (LAI). During the early period of motor skill acquisition, motor cortex excitability increases and changes occur in the area covered by the relevant zone of the motor cortex. In the late period, these give place to the morphological changes, such as synaptogenesis. SAI decreases during learning the motor skills, while LAI increases during motor activity. In this review, the role of SMI in the process of motor learning and transcranial magnetic stimulation techniques performed for studying SMI is summarized.

Restoring Motor Functions After Stroke: Multiple Approaches and Opportunities

More than 1.5 million people suffer a stroke in Europe per year and more than 70% of stroke survivors experience limited functional recovery of their upper limb, resulting in diminished quality of life. Therefore, interventions to address upper-limb impairment are a priority for stroke survivors and clinicians. While a significant body of evidence supports the use of conventional treatments, such as intensive motor training or constraint-induced movement therapy, the limited and heterogeneous improvements they allow are, for most patients, usually not sufficient to return to full autonomy. Various innovative neurorehabilitation strategies are emerging in order to enhance beneficial plasticity and improve motor recovery. Among them, robotic technologies, brain-computer interfaces, or noninvasive brain stimulation (NIBS) are showing encouraging results. These innovative interventions, such as NIBS, will only provide maximized effects, if the field moves away from the “one-fits all” approach toward a “patient-tailored” approach. After summarizing the most commonly used rehabilitation approaches, we will focus on NIBS and highlight the factors that limit its widespread use in clinical settings. Subsequently, we will propose potential biomarkers that might help to stratify stroke patients in order to identify the individualized optimal therapy. We will discuss future methodological developments, which could open new avenues for poststroke rehabilitation, toward more patient-tailored precision medicine approaches and pathophysiologically motivated strategies.

[Neurofeedback-based motor imagery training for rehabilitation after stroke]

Mental training, including motor observation and motor imagery, has awakened much academic interest. The presumed functional equivalence of motor imagery and motor execution has given hope that mental training could be used for motor rehabilitation after a stroke. Results obtained from randomized controlled trials have shown mixed results. Approximately half of the studies demonstrate positive effects of motor imagery training but the rest do not show an additional benefit. Possible reasons why motor imagery training has so far not become established as a robust therapeutic approach are discussed in detail. Moreover, more recent approaches, such as neurofeedback-based motor imagery or closed-loop systems are presented and the potential importance for motor learning and rehabilitation after a stroke is discussed.

Low Latency Estimation of Motor Intentions to Assist Reaching Movements along Multiple Sessions in Chronic Stroke Patients: A Feasibility Study

Background: The association between motor-related cortical activity and peripheral stimulation with temporal precision has been proposed as a possible intervention to facilitate cortico-muscular pathways and thereby improve motor rehabilitation after stroke. Previous studies with patients have provided evidence of the possibility to implement brain-machine interface platforms able to decode motor intentions and use this information to trigger afferent stimulation and movement assistance. This study tests the use a low-latency movement intention detector to drive functional electrical stimulation assisting upper-limb reaching movements of patients with stroke.

Methods: An eight-sessions intervention on the paretic arm was tested on four chronic stroke patients along 1 month. Patients' intentions to initiate reaching movements were decoded from electroencephalographic signals and used to trigger functional electrical stimulation that in turn assisted patients to do the task. The analysis of the patients' ability to interact with the intervention platform, the assessment of changes in patients' clinical scales and of the system usability and the kinematic analysis of the reaching movements before and after the intervention period were carried to study the potential impact of the intervention.

Results: On average 66.3 ± 15.7% of trials (resting intervals followed by self-initiated movements) were correctly classified with the decoder of motor intentions. The average detection latency (with respect to the movement onsets estimated with gyroscopes) was 112 ± 278 ms. The Fügl-Meyer index upper extremity increased 11.5 ± 5.5 points with the intervention. The stroke impact scale also increased. In line with changes in clinical scales, kinematics of reaching movements showed a trend toward lower compensatory mechanisms. Patients' assessment of the therapy reflected their acceptance of the proposed intervention protocol.

Conclusions: According to results obtained here with a small sample of patients, Brain-Machine Interfaces providing low-latency support to upper-limb reaching movements in patients with stroke are a reliable and usable solution for motor rehabilitation interventions with potential functional benefits.

Plasticity of premotor cortico-muscular coherence in severely impaired stroke patients with hand paralysis

Motor recovery in severely impaired stroke patients is often very limited. To refine therapeutic interventions for regaining motor control in this patient group, the functionally relevant mechanisms of neuronal plasticity need to be detected. Cortico-muscular coherence (CMC) may provide physiological and topographic insights to achieve this goal. Synchronizing limb movements to motor-related brain activation is hypothesized to reestablish cortico-motor control indexed by CMC. In the present study, right-handed, chronic stroke patients with right-hemispheric lesions and left hand paralysis participated in a four-week training for their left upper extremity. A brain-robot interface turned event-related beta-band desynchronization of the lesioned sensorimotor cortex during kinesthetic motor-imagery into the opening of the paralyzed hand by a robotic orthosis. Simultaneous MEG/EMG recordings and individual models from MRIs were used for CMC detection and source reconstruction of cortico-muscular connectivity to the affected finger extensors before and after the training program. The upper extremity-FMA of the patients improved significantly from 16.23 ± 6.79 to 19.52 ± 7.91 (p = 0.0015). All patients showed significantly increased CMC in the beta frequency-band, with a distributed, bi-hemispheric pattern and considerable inter-individual variability. The location of CMC changes was not correlated to the severity of the motor impairment, the motor improvement or the lesion volume. Group analysis of the cortical overlap revealed a common feature in all patients following the intervention: a significantly increased level of ipsilesional premotor CMC that extended from the superior to the middle and inferior frontal gyrus, along with a confined area of increased CMC in the contralesional premotor cortex. In conclusion, functionally relevant modulations of CMC can be detected in patients with long-term, severe motor deficits after a brain-robot assisted rehabilitation training. Premotor beta-band CMC may serve as a biomarker and therapeutic target for novel treatment approaches in this patient group.

Constraints and Adaptation of Closed-Loop Neuroprosthetics for Functional Restoration

Closed-loop neuroprosthetics aim to compensate for lost function, e.g., by controlling external devices such as prostheses or wheelchairs. Such assistive approaches seek to maximize speed and classification accuracy for high-dimensional control. More recent approaches use similar technology, but aim to restore lost motor function in the long term. To achieve this goal, restorative neuroprosthetics attempt to facilitate motor re-learning and to strengthen damaged and/or alternative neural connections on the basis of neurofeedback training within rehabilitative environments. Such a restorative approach requires reinforcement learning of self-modulated brain activity which is considered to be beneficial for functional rehabilitation, e.g., improvement of β-power modulation over sensorimotor areas for post-stroke movement restoration. Patients with motor impairments, however, may also have a compromised ability for motor task-related regulation of the targeted brain activity. This would affect the estimation of feature weights and hence the classification accuracy of the feedback device. This, in turn, can frustrate the patients and compromise their motor learning. Furthermore, the feedback training may even become erroneous when unconstrained classifier adaptation-which is often used in assistive approaches-is also applied in this rehabilitation context. In conclusion, the conceptual switch from assistance toward restoration necessitates a methodological paradigm shift from classification accuracy toward instructional efficiency. Furthermore, a constrained feature space, a priori regularized feature weights, and difficulty adaptation present key elements of restorative brain interfaces. These factors need, therefore, to be addressed within a therapeutic framework to facilitate reinforcement learning of brain self-regulation for restorative purposes.

Proprioceptive Feedback Facilitates Motor Imagery-Related Operant Learning of Sensorimotor β-Band Modulation

Motor imagery (MI) activates the sensorimotor system independent of actual movements and might be facilitated by neurofeedback. Knowledge on the interaction between feedback modality and the involved frequency bands during MI-related brain self-regulation is still scarce. Previous studies compared the cortical activity during the MI task with concurrent feedback (MI with feedback condition) to cortical activity during the relaxation task where no feedback was provided (relaxation without feedback condition). The observed differences might, therefore, be related to either the task or the feedback. A proper comparison would necessitate studying a relaxation condition with feedback and a MI task condition without feedback as well. Right-handed healthy subjects performed two tasks, i.e., MI and relaxation, in alternating order. Each of the tasks (MI vs. relaxation) was studied with and without feedback. The respective event-driven oscillatory activity, i.e., sensorimotor desynchronization (during MI) or synchronization (during relaxation), was rewarded with contingent feedback. Importantly, feedback onset was delayed to study the task-related cortical activity in the absence of feedback provision during the delay period. The reward modality was alternated every 15 trials between proprioceptive and visual feedback. Proprioceptive input was superior to visual input to increase the range of task-related spectral perturbations in the α- and β-band, and was necessary to consistently achieve MI-related sensorimotor desynchronization (ERD) significantly below baseline. These effects occurred in task periods without feedback as well. The increased accuracy and duration of learned brain self-regulation achieved in the proprioceptive condition was specific to the β-band. MI-related operant learning of brain self-regulation is facilitated by proprioceptive feedback and mediated in the sensorimotor β-band.

Closed-Loop Task Difficulty Adaptation during Virtual Reality Reach-to-Grasp Training Assisted with an Exoskeleton for Stroke Rehabilitation

Stroke patients with severe motor deficits of the upper extremity may practice rehabilitation exercises with the assistance of a multi-joint exoskeleton. Although this technology enables intensive task-oriented training, it may also lead to slacking when the assistance is too supportive. Preserving the engagement of the patients while providing “assistance-as-needed” during the exercises, therefore remains an ongoing challenge. We applied a commercially available seven degree-of-freedom arm exoskeleton to provide passive gravity compensation during task-oriented training in a virtual environment. During this 4-week pilot study, five severely affected chronic stroke patients performed reach-to-grasp exercises resembling activities of daily living. The subjects received virtual reality feedback from their three-dimensional movements. The level of difficulty for the exercise was adjusted by a performance-dependent real-time adaptation algorithm. The goal of this algorithm was the automated improvement of the range of motion. In the course of 20 training and feedback sessions, this unsupervised adaptive training concept led to a progressive increase of the virtual training space (p < 0.001) in accordance with the subjects' abilities. This learning curve was paralleled by a concurrent improvement of real world kinematic parameters, i.e., range of motion (p = 0.008), accuracy of movement (p = 0.01), and movement velocity (p < 0.001). Notably, these kinematic gains were paralleled by motor improvements such as increased elbow movement (p = 0.001), grip force (p < 0.001), and upper extremity Fugl-Meyer-Assessment score from 14.3 ± 5 to 16.9 ± 6.1 (p = 0.026). Combining gravity-compensating assistance with adaptive closed-loop feedback in virtual reality provides customized rehabilitation environments for severely affected stroke patients. This approach may facilitate motor learning by progressively challenging the subject in accordance with the individual capacity for functional restoration. It might be necessary to apply concurrent restorative interventions to translate these improvements into relevant functional gains of severely motor impaired patients in activities of daily living.

What Turns Assistive into Restorative Brain-Machine Interfaces?

Brain-machine interfaces (BMI) may support motor impaired patients during activities of daily living by controlling external devices such as prostheses (assistive BMI). Moreover, BMIs are applied in conjunction with robotic orthoses for rehabilitation of lost motor function via neurofeedback training (restorative BMI). Using assistive BMI in a rehabilitation context does not automatically turn them into restorative devices. This perspective article suggests key features of restorative BMI and provides the supporting evidence: In summary, BMI may be referred to as restorative tools when demonstrating subsequently (i) operant learning and progressive evolution of specific brain states/dynamics, (ii) correlated modulations of functional networks related to the therapeutic goal, (iii) subsequent improvement in a specific task, and (iv) an explicit correlation between the modulated brain dynamics and the achieved behavioral gains. Such findings would provide the rationale for translating BMI-based interventions into clinical settings for reinforcement learning and motor rehabilitation following stroke.

Neuromuscular Plasticity: Disentangling Stable and Variable Motor Maps in the Human Sensorimotor Cortex

Motor maps acquired with transcranial magnetic stimulation (TMS) are evolving as a biomarker for monitoring disease progression or the effects of therapeutic interventions. High test-retest reliability of this technique for long observation periods is therefore required to differentiate daily or weekly fluctuations from stable plastic reorganization of corticospinal connectivity. In this study, a novel projection, interpolation, and coregistration technique, which considers the individual gyral anatomy, was applied in healthy subjects for biweekly acquired TMS motor maps over a period of twelve weeks. The intraclass correlation coefficient revealed long-term reliability of motor maps with relevant interhemispheric differences. The sensorimotor cortex and nonprimary motor areas of the dominant hemisphere showed more extended and more stable corticospinal connectivity. Long-term correlations of the MEP amplitudes at each stimulation site revealed mosaic-like clusters of consistent corticospinal excitability. The resting motor threshold, centre of gravity, and mean MEPs across all TMS sites, as highly reliable cortical map parameters, could be disentangled from more variable parameters such as MEP area and volume. Cortical TMS motor maps provide high test-retest reliability for long-term monitoring when analyzed with refined techniques. They may guide restorative interventions which target dormant corticospinal connectivity for neurorehabilitation.

Hybrid Neuroprosthesis for the Upper Limb: Combining Brain-Controlled Neuromuscular Stimulation with a Multi-Joint Arm Exoskeleton

Brain-machine interface-controlled (BMI) neurofeedback training aims to modulate cortical physiology and is applied during neurorehabilitation to increase the responsiveness of the brain to subsequent physiotherapy. In a parallel line of research, robotic exoskeletons are used in goal-oriented rehabilitation exercises for patients with severe motor impairment to extend their range of motion (ROM) and the intensity of training. Furthermore, neuromuscular electrical stimulation (NMES) is applied in neurologically impaired patients to restore muscle strength by closing the sensorimotor loop. In this proof-of-principle study, we explored an integrated approach for providing assistance as needed to amplify the task-related ROM and the movement-related brain modulation during rehabilitation exercises of severely impaired patients. For this purpose, we combined these three approaches (BMI, NMES, and exoskeleton) in an integrated neuroprosthesis and studied the feasibility of this device in seven severely affected chronic stroke patients who performed wrist flexion and extension exercises while receiving feedback via a virtual environment. They were assisted by a gravity-compensating, seven degree-of-freedom exoskeleton which was attached to the paretic arm. NMES was applied to the wrist extensor and flexor muscles during the exercises and was controlled by a hybrid BMI based on both sensorimotor cortical desynchronization (ERD) and electromyography (EMG) activity. The stimulation intensity was individualized for each targeted muscle and remained subthreshold, i.e., induced no overt support. The hybrid BMI controlled the stimulation significantly better than the offline analyzed ERD (p = 0.028) or EMG (p = 0.021) modality alone. Neuromuscular stimulation could be well integrated into the exoskeleton-based training and amplified both the task-related ROM (p = 0.009) and the movement-related brain modulation (p = 0.019). Combining a hybrid BMI with neuromuscular stimulation and antigravity assistance augments upper limb function and brain activity during rehabilitation exercises and may thus provide a novel restorative framework for severely affected stroke patients.

Probing Corticospinal Recruitment Patterns and Functional Synergies with Transcranial Magnetic Stimulation

Background: On the one hand, stimulating the motor cortex at different spots may activate the same muscle and result in a muscle-specific cortical map. Maps of different muscles, which are functionally coupled, may present with a large overlap but may also show a relevant variability. On the other hand, stimulation of the motor cortex at one spot with different stimulation intensities results in a characteristic input–output (IO) curve for one specific muscle but may simultaneously also activate different, functionally coupled muscles. A comparison of the cortical map overlap of synergistic muscles and their IO curves has not yet been carried out.

Objective: The aim of this study was to probe functional synergies of forearm muscles with transcranial magnetic stimulation by harnessing the convergence and divergence of the corticospinal output.

Methods: We acquired bihemispheric cortical maps and IO curves of the extensor carpi ulnaris, extensor carpi radialis, and extensor digitorum communis muscles by subjecting 11 healthy subjects to both monophasic and biphasic pulse waveforms.

Results: The degree of synergy between pairs of forearm muscles was captured by the overlap of the cortical motor maps and the respective IO curves which were influenced by the pulse waveform. Monophasic and biphasic stimulation were particularly suitable for disentangling synergistic muscles in the right and left hemisphere, respectively.

Conclusion: Combining IO curves and different pulse waveforms may provide complementary information on neural circuit dynamics and corticospinal recruitment patterns of synergistic muscles and their neuroplastic modulation.

Closed-Loop Neuroprosthesis for Reach-to-Grasp Assistance: Combining Adaptive Multi-channel Neuromuscular Stimulation with a Multi-joint Arm Exoskeleton

Stroke patients with severe motor deficits cannot execute task-oriented rehabilitation exercises with their affected upper extremity. Advanced rehabilitation technology may support them in performing such reach-to-grasp movements. The challenge is, however, to provide assistance as needed, while maintaining the participants' commitment during the exercises. In this feasibility study, we introduced a closed-loop neuroprosthesis for reach-to-grasp assistance which combines adaptive multi-channel neuromuscular stimulation with a multi-joint arm exoskeleton. Eighteen severely affected chronic stroke patients were assisted by a gravity-compensating, seven-degree-of-freedom exoskeleton which was attached to the paretic arm for performing reach-to-grasp exercises resembling activities of daily living in a virtual environment. During the exercises, adaptive electrical stimulation was applied to seven different muscles of the upper extremity in a performance-dependent way to enhance the task-oriented movement trajectory. The stimulation intensity was individualized for each targeted muscle and remained subthreshold, i.e., induced no overt support. Closed-loop neuromuscular stimulation could be well integrated into the exoskeleton-based training, and increased the task-related range of motion (p = 0.0004) and movement velocity (p = 0.015), while preserving accuracy. The highest relative stimulation intensity was required to facilitate the grasping function. The facilitated range of motion correlated with the upper extremity Fugl-Meyer Assessment score of the patients (p = 0.028). Combining adaptive multi-channel neuromuscular stimulation with antigravity assistance amplifies the residual motor capabilities of severely affected stroke patients during rehabilitation exercises and may thus provide a customized training environment for patient-tailored support while preserving the participants' engagement.

Compensation or Restoration: Closed-Loop Feedback of Movement Quality for Assisted Reach-to-Grasp Exercises with a Multi-Joint Arm Exoskeleton

Assistive technology allows for intensive practice and kinematic measurements during rehabilitation exercises. More recent approaches attach a gravity-compensating multi-joint exoskeleton to the upper extremity to facilitate task-oriented training in three-dimensional space with virtual reality feedback. The movement quality, however, is mostly captured through end-point measures that lack information on proximal inter-joint coordination. This limits the differentiation between compensation strategies and genuine restoration both during the exercise and in the course of rehabilitation. We extended in this proof-of-concept study a commercially available seven degree-of-freedom arm exoskeleton by using the real-time sensor data to display a three-dimensional multi-joint visualization of the user's arm. Ten healthy subjects and three severely affected chronic stroke patients performed reach-to-grasp exercises resembling activities of daily living assisted by the attached exoskeleton and received closed-loop online feedback of the three-dimensional movement in virtual reality. Patients in this pilot study differed significantly with regard to motor performance (accuracy, temporal efficiency, range of motion) and movement quality (proximal inter-joint coordination) from the healthy control group. In the course of 20 training and feedback sessions over 4 weeks, these pathological measures improved significantly toward the reference parameters of healthy participants. It was moreover feasible to capture the evolution of movement pattern kinematics of the shoulder and elbow and to quantify the individual degree of natural movement restoration for each patient. The virtual reality visualization and closed-loop feedback of joint-specific movement kinematics makes it possible to detect compensation strategies and may provide a tool to achieve the rehabilitation goals in accordance with the individual capacity for genuine functional restoration; a proposal that warrants further investigation in controlled studies with a larger cohort of stroke patients.