Macrostructural Cerebellar Neuroplasticity Correlates With Motor Recovery After Stroke.

BACKGROUND: Motor recovery varies across post-stroke individuals, some of whom require a better rehabilitation strategy. We hypothesized that macrostructural neuroplasticity of the motor control network including the cerebellum might underlie individual differences in motor recovery. Objectives. To gain insight into the macrostructural neuroplasticity after stroke, we examined 52 post-stroke individuals using both the Fugl-Meyer assessment and structural magnetic resonance imaging. METHODS: We performed voxel-based lesion symptom mapping and cross-sectional voxel-based morphometry to correlate the motor scores with the lesion location and the gray matter volume (GMV), respectively. Longitudinal data were available at ~8 and/or 15 weeks after admission from 43 individuals with supratentorial lesions. We performed a longitudinal VBM analysis followed by a multiple regression analysis to correlate between the changes of the motor assessment scores and those of GMV overtime. RESULTS: We found a cross-sectional correlation of residual motor functioning with GMV in the ipsilesional cerebellum and contralesional parietal cortex. Longitudinally, we found increases in GMV in the ipsilesional supplementary motor area, and the ipsilesional superior and inferior cerebellar zones, along with a GMV decrease in the ipsilesional thalamus. The motor recovery was correlated with the GMV changes in the superior and inferior cerebellar zones. The regaining of upper-limb motor functioning was correlated with the GMV changes of both superior and inferior cerebellum while that of lower-limb motor functioning with the GMV increase of the inferior cerebellum only. CONCLUSIONS: The present findings support the hypothesis that macrostructural cerebellar neuroplasticity is correlated with individual differences in motor recovery after stroke.

Interactions across emotional, cognitive and subcortical motor networks underlying freezing of gait.

Freezing of gait (FOG) is a gait disorder affecting patients with Parkinson's disease (PD) and related disorders. The pathophysiology of FOG is unclear because of its phenomenological complexity involving motor, cognitive, and emotional aspects of behavior. Here we used resting-state functional MRI to retrieve functional connectivity (FC) correlated with the New FOG questionnaire (NFOGQ) reflecting severity of FOG in 67 patients with PD. NFOGQ scores were correlated with FCs in the extended basal ganglia network (BGN) involving the striatum and amygdala, and in the extra-cerebellum network (CBLN) involving the frontoparietal network (FPN). These FCs represented interactions across the emotional (amygdala), subcortical motor (BGN and CBLN), and cognitive networks (FPN). Using these FCs as features, we constructed statistical models that explained 40% of the inter-individual variances of FOG severity and that discriminated between PD patients with and without FOG. The amygdala, which connects to the subcortical motor (BGN and CBLN) and cognitive (FPN) networks, may have a pivotal role in interactions across the emotional, cognitive, and subcortical motor networks. Future refinement of the machine learning-based classifier using FCs may clarify the complex pathophysiology of FOG further and help diagnose and evaluate FOG in clinical settings.

Decoding of Ankle Flexion and Extension from Cortical Current Sources Estimated from Non-invasive Brain Activity Recording Methods.

The classification of ankle movements from non-invasive brain recordings can be applied to a brain-computer interface (BCI) to control exoskeletons, prosthesis, and functional electrical stimulators for the benefit of patients with walking impairments. In this research, ankle flexion and extension tasks at two force levels in both legs, were classified from cortical current sources estimated by a hierarchical variational Bayesian method, using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings. The hierarchical prior for the current source estimation from EEG was obtained from activated brain areas and their intensities from an fMRI group (second-level) analysis. The fMRI group analysis was performed on regions of interest defined over the primary motor cortex, the supplementary motor area, and the somatosensory area, which are well-known to contribute to movement control. A sparse logistic regression method was applied for a nine-class classification (eight active tasks and a resting control task) obtaining a mean accuracy of 65.64% for time series of current sources, estimated from the EEG and the fMRI signals using a variational Bayesian method, and a mean accuracy of 22.19% for the classification of the pre-processed of EEG sensor signals, with a chance level of 11.11%. The higher classification accuracy of current sources, when compared to EEG classification accuracy, was attributed to the high number of sources and the different signal patterns obtained in the same vertex for different motor tasks. Since the inverse filter estimation for current sources can be done offline with the present method, the present method is applicable to real-time BCIs. Finally, due to the highly enhanced spatial distribution of current sources over the brain cortex, this method has the potential to identify activation patterns to design BCIs for the control of an affected limb in patients with stroke, or BCIs from motor imagery in patients with spinal cord injury.

Structure design for a Two-DoF myoelectric prosthetic hand to realize basic hand functions in ADLs.

Prosthetic hands are desired by those who have lost a hand or both hands not only for decoration but also for the functions to help them with their activities of daily living (ADL). Prosthetic robotic hands that are developed to fully realize the function of a human hand are usually too expensive to be economically available, difficult to operate and maintain, or over heavy for longtime wearing. The aim of this study is therefore to develop a simplified prosthetic hand (sim-PH), which is to be controlled by myoelectric signals from the user, to realize the most important grasp motions in ADL by trading off the cost and performance. This paper reports the structure design of a two-DoF sim-PH with two motors to drive the CM joint of the thumb and the interlocked MP joints of the other four fingers. In order to optimize the structure, the model of the sim-PH was proposed based on which 7 sim-PHs with different structural parameters were manufactured and tested in a pick-and-place experiment. Correspondence analysis of the experimental results clarified the relationship between the hand functions and the shapes of fingers.

Brain-machine interface to control a prosthetic arm with monkey ECoGs during periodic movements.

Brain-machine interfaces (BMIs) are promising technologies for rehabilitation of upper limb functions in patients with severe paralysis. We previously developed a BMI prosthetic arm for a monkey implanted with electrocorticography (ECoG) electrodes, and trained it in a reaching task. The stability of the BMI prevented incorrect movements due to misclassification of ECoG patterns. As a trade-off for the stability, however, the latency (the time gap between the monkey's actual motion and the prosthetic arm movement) was about 200 ms. Therefore, in this study, we aimed to improve the response time of the BMI prosthetic arm. We focused on the generation of a trigger event by decoding muscle activity in order to predict integrated electromyograms (iEMGs) from the ECoGs. We verified the achievability of our method by conducting a performance test of the proposed method with actual achieved iEMGs instead of predicted iEMGs. Our results confirmed that the proposed method with predicted iEMGs eliminated the time delay. In addition, we found that motor intention is better reflected by muscle activity estimated from brain activity rather than actual muscle activity. Therefore, we propose that using predicted iEMGs to guide prosthetic arm movement results in minimal delay and excellent performance.

[Development of a reflex electrical stimulation device to assist walking].

This paper is a summary of the biofeedback technology for the reflex electrical stimulation device to assist walking. The experiments showed that electrical stimulation resulted in prominent stimulation with less habituation. The research elements were an input-type brain machine interface (BMI), functional magnetic resonance imaging (f-MRI) analysis to detect brain activity, multi-channel electrical stimulation, reflex stimulation for muscle contraction, and an adaptive rehabilitation fitting to the walking gate. The results showed that neuro rehabilitation may be attained by the integration of these research elements.

Estimation of handgrip force using frequency-band technique during fatiguing muscle contraction.

In this paper, we propose a force estimation model to compute the handgrip force from SEMG signal during fatiguing muscle contraction tasks. The appropriate frequency range was analyzed using various combinations of a wavelet scale, and the highest accuracy was achieved at a range from 242 to 365 Hz. After that, eight healthy individuals performed a series of static (70%, 50%, 30%, and 20% MVC) and dynamic (0-50% MVC) muscle contraction tasks to evaluate the performance of this technique in comparison with that of former method using the Root Mean Square of the SEMG signal. Both methods had comparable results at the beginning of the experiments, before the onset of muscle fatigue. However, differences were clearly observed as the degree of muscle fatigue began to increase toward the endurance time. Under this condition, the estimated handgrip force using the proposed method improved from 17% to 134% for static contraction tasks and 40% for dynamic contraction tasks. This study overcomes the limitation of the former method during fatiguing muscle contraction tasks and, therefore, unlocks the potential of utilizing the SEMG signal as an indirect force estimation method for various applications.

Quantitative estimation of muscle fatigue using surface electromyography during static muscle contraction.

Muscle fatigue is commonly associated with the musculoskeletal disorder problem. Previously, various techniques were proposed to index the muscle fatigue from electromyography signal. However, quantitative measurement is still difficult to achieve. This study aimed at proposing a method to estimate the degree of muscle fatigue quantitatively. A fatigue model was first constructed using handgrip dynamometer by conducting a series of static contraction tasks. Then the degree muscle fatigue can be estimated from electromyography signal with reasonable accuracy. The error of the estimated muscle fatigue was less than 10% MVC and no significant difference was found between the estimated value and the one measured using force sensor. Although the results were promising, there were still some limitations that need to be overcome in future study.

Control strategy for a myoelectric hand: measuring acceptable time delay in human intention discrimination.

In order to enhance controllability of a myoelectric hand, we focus on a gap between the time when a human intends to move a myoelectric hand and the time when the hand actually moves (i.e., time delay). Normally, the myoelectric hand users dislike the time delay because it makes them feel uncomfortable. However, the users learn the time delay within some time ranges and, eventually, get feel comfortable to operate the hand. Thus, we assume, if we reveal the acceptable delay time (i.e., the time the users accept the gap with their learning ability), we can provide more time in a human intention discrimination process, and enhance its success rate. Therefore, we developed a mobile myoelectric hand system with an embedded linux computer, and conducted a ball catch experiment: we investigate the acceptable delay time by adding the delay time (i.e., 120[ms], 170[ms], 220[ms], 270[ms], 320[ms]) into the human intention discrimination process. As a result, we confirmed that the max accept delay time was approximately 170 [ms] that achieves 61% success rate.