[Abstract +References] Reorganization of Bioelectrical Activity in the Neocortex after Stroke by Rehabilitation Using a Brain–Computer Interface Controlling a Wrist Exoskeleton

The process of the functional rearrangement of the motor cortex of the brain after stroke is due to neuroplasticity, and this underlies motor recovery. Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) are currently recognized as the most informative methods for studying these processes. The course of the neuroplastic process can be evaluated from the power levels of EEG rhythms during imagination of movements in the paralyzed arm in right-handed patients after stroke in the left hemisphere monitored at different times – before and after courses of neurorehabilitation using a brain–computer interface controlling a wrist exoskeleton. Powerful excitatory interactions in the primary motor cortex and frontoparietal areas in the lesioned and “intact” hemispheres are initially seen, and these probably reflect reorganization of neural networks. Rehabilitation courses were followed by restoration of bioelectrical activity in the primary motor cortex due to recovery of efficient connections with the premotor and superior parietal zones and decreases in the pathological influences of the contralateral hemisphere.

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[ARTICLE] fNIRS-based Neurorobotic Interface for gait rehabilitation – Full Text

Abstract

Background

In this paper, a novel functional near-infrared spectroscopy (fNIRS)-based brain-computer interface (BCI) framework for control of prosthetic legs and rehabilitation of patients suffering from locomotive disorders is presented.

Methods

fNIRS signals are used to initiate and stop the gait cycle, while a nonlinear proportional derivative computed torque controller (PD-CTC) with gravity compensation is used to control the torques of hip and knee joints for minimization of position error. In the present study, the brain signals of walking intention and rest tasks were acquired from the left hemisphere’s primary motor cortex for nine subjects. Thereafter, for removal of motion artifacts and physiological noises, the performances of six different filters (i.e. Kalman, Wiener, Gaussian, hemodynamic response filter (hrf), Band-pass, finite impulse response) were evaluated. Then, six different features were extracted from oxygenated hemoglobin signals, and their different combinations were used for classification. Also, the classification performances of five different classifiers (i.e. k-Nearest Neighbour, quadratic discriminant analysis, linear discriminant analysis (LDA), Naïve Bayes, support vector machine (SVM)) were tested.

Results

The classification accuracies obtained from SVM using the hrf were significantly higher (p < 0.01) than those of the other classifier/ filter combinations. Those accuracies were 77.5, 72.5, 68.3, 74.2, 73.3, 80.8, 65, 76.7, and 86.7% for the nine subjects, respectively.

Conclusion

The control commands generated using the classifiers initiated and stopped the gait cycle of the prosthetic leg, the knee and hip torques of which were controlled using the PD-CTC to minimize the position error. The proposed scheme can be effectively used for neurofeedback training and rehabilitation of lower-limb amputees and paralyzed patients.

Background

Neurological disability due specifically to stroke or spinal cord injury can profoundly affect the social life of paralyzed patients [1, 2, 3]. The resultant gait impairment is a large contributor to ambulatory dysfunction [4]. In order to regain complete functional independence, physical rehabilitation remains the mainstay option, owing to the significant expense of health care and the redundancy of therapy sessions. Such devices are developed as alternatives to traditional, expensive and time-consuming exercises in busy daily life. In the past, similar training sessions on treadmills performed using robotic mechanisms have shown better functional outcomes [1, 2, 5, 6, 7]. However, these devices have limitations particular to given research and clinical settings. Therefore, wearable upper- and lower-limb robotic devices have been developed [7, 8], which are used to assist users by actuating joints to partial or complete movement using brain intentions, according to individual-patient needs.

To date, various noninvasive modalities including functional magnetic resonance imaging (fMRI), electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have been used to acquire brain signals. fNIRS is a relatively new modality that detects brain intention with reference to changes in hemodynamic response. Its fewer artifacts, better spatial resolution and acceptable temporal resolution make it the choice for comprehensive and promising results in, for example, rehabilitation and mental task applications [9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]. The main brain-computer interface (BCI) challenge in this regard is to extract useful information from raw brain signals for control-command generation [21, 22, 23]. Acquired signals are processed in the following four stages: preprocessing, feature extraction, classification, and command generation. In preprocessing, physiological and instrumental artifacts and noises are removed [24, 25]. After this filtration stage, feature extraction proceeds in order to gather useful information. Then, the extracted features are classified using different classifiers. Finally, the trained classifier is used to generate control commands based on a trained model [23]. Figure 1 shows a schematic of a BCI.

Fig. 1 Schematic of BCI

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[Abstract] Transcranial direct current stimulation over multiple days enhances motor performance of a grip task

Abstract

Background

Recovery of handgrip is critical after stroke since it is positively related to upper limb function. To boost motor recovery, transcranial direct current stimulation (tDCS) is a promising, non-invasive brain stimulation technique for the rehabilitation of persons with stroke. When applied over the primary motor cortex (M1), tDCS has been shown to modulate neural processes involved in motor learning. However, no studies have looked at the impact of tDCS on the learning of a grip task in both stroke and healthy individuals.

Objective

To assess the use of tDCS over multiple days to promote motor learning of a grip task using a learning paradigm involving a speed-accuracy tradeoff in healthy individuals.

Methods

In a double-blinded experiment, 30 right-handed subjects (mean age: 22.1 ± 3.3 years) participated in the study and were randomly assigned to an anodal (n = 15) or sham (n = 15) stimulation group. First, subjects performed the grip task with their dominant hand while following the pace of a metronome. Afterwards, subjects trained on the task, at their own pace, over 5 consecutive days while receiving sham or anodal tDCS over M1. After training, subjects performed de novo the metronome-assisted task. The change in performance between the pre and post metronome-assisted task was used to assess the impact of the grip task and tDCS on learning.

Results

Anodal tDCS over M1 had a significant effect on the speed-accuracy tradeoff function. The anodal tDCS group showed significantly greater improvement in performance (39.28 ± 15.92%) than the sham tDCS group (24.06 ± 16.35%) on the metronome-assisted task, t(28) = 2.583, P = 0.015 (effect size d = 0.94).

Conclusions

Anodal tDCS is effective in promoting grip motor learning in healthy individuals. Further studies are warranted to test its potential use for the rehabilitation of fine motor skills in stroke patients.

Source: Transcranial direct current stimulation over multiple days enhances motor performance of a grip task – ScienceDirect

[Abstract] Changes in motor cortex excitability for the trained and non-trained hand after long-term unilateral motor training

Highlights

We investigated intracortical facilitation (ICF) in M1 after unilateral long term hand training.

Motor performance improved for both hands but ICF was only altered for the untrained hand.

The ICF-decrease is associated with a transfer of training-induced improvement of performance.

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Abstract

Repetitive unilateral upper limb motor training does not only affect behavior but also increases excitability of the contralateral primary motor cortex (M1). The behavioral gain is partially transferred to the non-trained side. Changes in M1 intracortical facilitation (ICF) might as well be observed for both hand sides. We measured ICF of both left and right abductor pollicis brevis muscles (APB) before and after a two-week period of arm ability training (AAT) of the left hand in 13 strongly right handed healthy volunteers. Performance with AAT-tasks improved for both the left trained and right untrained hand. ICF for the untrained hand decreased over training while it remained unchanged for the left trained hand. Decrease of ICF for the right hand was moderately associated with an increase of AAT-performance for the untrained right hand. We conclude that ICF-imbalance between dominant and non-dominant hand is sensitive to long-term motor training: training of the non-dominant hand results in a decrease of ICF of the dominant hand. The ICF-decrease is associated with a transfer of training-induced improvement of performance from the non-dominant to the dominant hand.

Source: Changes in motor cortex excitability for the trained and non-trained hand after long-term unilateral motor training

[Review] Motor Imagery-Based Rehabilitation: Potential Neural Correlates and Clinical Application for Functional Recovery of Motor Deficits after Stroke – Full Text PDF

ABSTRACT:

Motor imagery (MI), defined as the mental implementation of an action in the absence of movement or muscle activation, is a rehabilitation technique that offers a means to replace or restore lost motor function in stroke patients when used in conjunction with conventional physiotherapy procedures. This article briefly reviews the concepts and neural correlates of MI in order to promote improved understanding, as well as to enhance the clinical utility of MI-based rehabilitation regimens. We specifically highlight the role of the cerebellum and basal ganglia, premotor, supplementary motor, and prefrontal areas, primary motor cortex, and parietal cortex. Additionally, we examine the recent literature related to MI and its potential as a therapeutic technique in both upper and lower limb stroke rehabilitation.

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[ARTICLE] Direct and crossed effects of somatosensory electrical stimulation on motor learning and neuronal plasticity in humans – Full Text PDF

Abstract

Purpose: Sensory input can modify voluntary motor function. We examined whether somatosensory electrical stimulation (SES) added to motor practice (MP) could augment motor learning, interlimb transfer, and whether physiological changes in neuronal excitability underlie these changes.

Methods: Participants (18–30 years, n = 31) received MP, SES, MP + SES, or a control intervention. Visuomotor practice included 300 trials for 25 min with the rightdominant wrist and SES consisted of weak electrical stimulation of the radial and median nerves above the elbow. Single- and double-pulse transcranial magnetic stimulation (TMS) metrics were measured in the intervention and nonintervention extensor carpi radialis.

Results: There was 27 % motor learning and 9 % (both p < 0.001) interlimb transfer in all groups but SES added to MP did not augment learning and transfer. Corticospinal excitability increased after MP and SES when measured at rest but it increased after MP and decreased after SES when measured during contraction. No changes occurred in intracortical inhibition and facilitation. MP did not affect the TMS metrics in the transfer hand. In contrast, corticospinal excitability strongly increased after SES with MP + SES showing sharply opposite of these effects.

Conclusion: Motor practice and SES each can produce motor learning and interlimb transfer and are likely to be mediated by different mechanisms. The results provide insight into the physiological mechanisms underlying the effects of MP and SES on motor learning and cortical plasticity and show that these mechanisms are likely to be different for the trained and stimulated motor cortex and the non-trained and non-stimulated motor cortex.

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[ARTICLE] Interactions between primary and secondary motor areas for recovered hand functions after stroke

Abstract

Objectives

Goal oriented hand movements are the final product of complex interplay between multiple cortical regions of the frontoparietal cortex. Following stroke, recovery of function might be related to structural and functional modifications in the surviving brain networks. Strikingly, the ventral premotor cortex (PMv) plays a crucial role in the sensorimotor processing engaged in shaping finger movements and shares extensive reciprocal projections to the primary motor cortex (M1), making the PMv a promising structure involved in hand motor recovery after stroke. By instance, mapping studies in stroke animal models consistently showed that improvement in forelimb motor performance was associated with reorganization within the PMv. However, precise functional interactions between the PMv and the M1 in the process of hand motor recovery have not been investigated in stroke patients. Paired-pulse transcranial magnetic stimulation (pp-TMS) was used in order to evaluate PMv-M1 interactions in stroke patients and healthy controls. To further disentangle whether recovery-related changes are specific to PMv-M1 or apparent in all interactions between primary and secondary motor areas after stroke, connectivity between posterior parietal cortex (PPC) and M1 have been also assessed.

Methods

22 patients (aged 62.3 ± 9.8 SD) with mild to severe unilateral hand motor deficit and 20 healthy controls (aged 59.7 ± 20.9 SD) participated in the present study. Within experiment-1, PMv-M1 interactions were investigated by using neuronavigated-ppTMS in both the affected and unaffected hemisphere, considering 6 interstimulus intervals (ISI; 2, 4, 6, 8, 10 and 15 ms) between conditioning and test pulse. In experiment-2, PPC-M1 interactions were investigated targeting two PPC regions: the anterior intraparietal sulcus (aIPS) and the caudal intraparietal sulcus (cIPS).

Results

Experiment-1: A consistent difference between patients and controls, with a group by ISI interaction, has been observed for both the affected (F = 14.9, p < 0.001) as well as the unaffected (F = 5.3, p < 0.001) hemisphere. Further posthoc analysis confirmed a significant difference at 6 and 8 ms between both groups (ISI6 ms = T = 3.4, p = 0.002; ISI8 ms = T = 7.1, p < 0.001), with consistent facilitation in stroke and rather inhibition in healthy controls. Interestingly, the amount of facilitation observed at 8 ms in stroke patients was positively correlated with the level of force production of the paretic hand (R2 = 0.33, p = 0.010) assessed by the laterality-index of grip-force. Experiment-2: No group differences have been observed between aiPS-M1 and cIPS-M1 in both hemispheres.

Conclusions

The present pp-TMS approach enabled us to assess in vivo the specific contributions of individual brain areas to recovery of function after stroke. These findings demonstrate changes in interareal connectivity between the PMv and the M1 associated with recovered hand motor function. These data support a paramount role of the PMv within the process of functional reorganization and successful hand motor recovery, as suggested in previous animal work, contributing significantly to the understanding of the mechanisms underlying cortical reorganization after focal brain lesions.

via V37. Interactions between primary and secondary motor areas for recovered hand functions after stroke – Clinical Neurophysiology.

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