Best facilitated cortical activation during different stepping, treadmill, and robot-assisted walking training paradigms and speeds: A functional near-infrared spectroscopy neuroimaging study

Regional activity and effective connectivity within the frontoparietal network during precision walking with visual cueing: an fNIRS study

Precision walking (PW) incorporates precise step adjustments into regular walking patterns to navigate challenging surroundings. However, the brain processes involved in PW control, which encompass cortical regions and interregional interactions, are not fully understood. This study aimed to investigate the changes in regional activity and effective connectivity within the frontoparietal network associated with PW. Functional near-infrared spectroscopy data were recorded from adult subjects during treadmill walking tasks, including normal walking (NOR) and PW with visual cues, wherein the intercue distance was either fixed (FIX) or randomly varied (VAR) across steps. The superior parietal lobule (SPL), dorsal premotor area (PMd), supplementary motor area (SMA), and dorsolateral prefrontal cortex (dlPFC) were specifically targeted. The results revealed higher activities in SMA and left PMd, as well as left-to-right SPL connectivity, in VAR than in FIX. Activities in SMA and right dlPFC, along with dlPFC-to-SPL connectivity, were higher in VAR than in NOR. Overall, these findings provide insights into the roles of different brain regions and connectivity patterns within the frontoparietal network in facilitating gait control during PW, providing a useful baseline for further investigations into brain networks involved in locomotion.

Effects of Training with a Powered Exoskeleton on Cortical Activity Modulation in Hemiparetic Chronic Stroke Patients: A Randomized Controlled Pilot Trial

OBJECTIVES

To investigate the effects of exoskeleton-assisted gait training in stroke patients.

DESIGN

Prospective randomized controlled trial.

SETTING

Rehabilitation department in a single tertiary hospital.

PARTICIPANTS

Thirty (N=30) chronic stroke patients with Functional Ambulatory Category scale (FAC) between 2 and 4.

INTERVENTION

Patients were randomly assigned to 1 of 2 groups: training with Healbot G, a wearable powered exoskeleton (Healbot G group; n=15), or treadmill training (control group; n=15). All participants received 30 minutes of training, 10 times per week, for 4 weeks.

OUTCOME MEASUREMENTS

The primary outcome was oxyhemoglobin level changes, representing cortical activity in both motor cortices using functional near-infrared spectroscopy. The secondary outcomes included FAC, Berg Balance Scale, Motricity Index for the lower extremities (MI-Lower), 10-meter walk test, and gait symmetry ratio (spatial step and temporal symmetry ratio).

RESULTS

Compared to the control group, during the entire training session, the pre-training and post-training mean cortical activity, and the amount of increment between pre- and post-training were significantly higher in the Healbot G group (∆mean ± SD; pre-training, 0.245±0.119, post-training, 0.697±0.429, between pre- and post-training, 0.471±0.401μmol, P<.001). There was no significant difference in cortical activity between affected- and unaffected hemispheres after Healbot G training. FAC (∆mean ± SD; 0.35 ± 0.50, P=.012), MI-Lower (∆mean ± SD; 7.01 ± 0.14, P=.001), and spatial step gait symmetry ratio (∆mean ± SD; -0.32 ± 0.25, P=.049) were improved significantly in the Healbot G group.

CONCLUSION

Exoskeleton-assisted gait training induces cortical modulation effect in both motor cortices, a balanced cortical activation pattern with improvements in spatial step symmetry ratio, walking ability, and voluntary strength.

fNIRS is sensitive to leg activity in the primary motor cortex after systemic artifact correction

BACKGROUND

functional near-infrared spectroscopy (fNIRS) is an increasingly popular tool to study cortical activity during movement and gait that requires further validation. This study aimed to assess (1) whether fNIRS can detect the difficult-to-measure leg area of the primary motor cortex (M1) and distinguish it from the hand area; and (2) whether fNIRS can differentiate between automatic (i.e., not requiring one's attention) and non-automatic movement processes. Special attention was attributed to systemic artifacts (i.e., changes in blood pressure, heart rate, breathing) which were assessed and corrected by short channels, i.e., fNIRS channels which are mainly sensitive to superficial scalp hemodynamics.

METHODS

Twenty-three seated, healthy participants tapped four fingers on a keyboard or tapped the right foot on four squares on the floor in a specific order given by a 12-digit sequence (e.g., 434141243212). Two different sequences were executed: a beforehand learned (i.e., automatic) version and a newly learned (i.e., non-automatic) version. A 36-channel fNIRS device including 12 short channels covered multiple motor-related cortical areas including M1. The fNIRS data were analyzed with a general linear model (GLM). Correlation between the expected functional hemodynamic responses (i.e. task regressor) and the short channels (i.e. nuisance regressors), necessitated performing a separate short channel regression instead of integrating them in the GLM.

RESULTS

Consistent with the M1 somatotopy, we found significant HbO increases of very large effect size in the lateral M1 channels during finger tapping (Cohen's d = 1.35, p<0.001) and significant HbO increases of moderate effect size in the medial M1 channels during foot tapping (Cohen's d = 0.8, p<0.05). The cortical activity differences between automatic and non-automatic tasks were not significantly different. Importantly, leg movements produced large systemic fluctuations, which were adequately removed by the use of all available short channels.

DISCUSSION

Our results indicate that fNIRS is sensitive to leg activity in M1, though the sensitivity is lower than for finger activity and requires rigorous correction for systemic fluctuations. We furthermore highlight that systemic artifacts may result in an unreliable GLM analysis when short channels show signals that are similar to the expected hemodynamic responses.

Asymmetric cortical activation in healthy and hemiplegic individuals during walking: A functional near-infrared spectroscopy neuroimaging study

Background

This study investigated the cortical activation mechanism underlying locomotor control during healthy and hemiplegic walking.

Methods

A total of eight healthy individuals with right leg dominance (male patients, 75%; mean age, 40.06 ± 4.53 years) and six post-stroke patients with right hemiplegia (male patients, 86%; mean age, 44.41 ± 7.23 years; disease course, 5.21 ± 2.63 months) completed a walking task at a treadmill speed of 2 km/h and a functional electrical stimulation (FES)-assisted walking task, respectively. Functional near-infrared spectroscopy (fNIRS) was used to detect hemodynamic changes in neuronal activity in the bilateral sensorimotor cortex (SMC), supplementary motor area (SMA), and premotor cortex (PMC).

Results

fNIRS cortical mapping showed more SMC-PMC-SMA locomotor network activation during hemiplegic walking than during healthy gait. Furthermore, more SMA and PMC activation in the affected hemisphere was observed during the FES-assisted hemiplegic walking task than during the non-FES-assisted task. The laterality index indicated asymmetric cortical activation during hemiplegic gait, with relatively greater activation in the unaffected (right) hemisphere during hemiplegic gait than during healthy walking. During hemiplegic walking, the SMC and SMA were predominantly activated in the unaffected hemisphere, whereas the PMC was predominantly activated in the affected hemisphere. No significant differences in the laterality index were noted between the other groups and regions (p > 0.05).

Conclusion

An important feature of asymmetric cortical activation was found in patients with post-stroke during the walking process, which was the recruitment of more SMC-SMA-PMC activation than in healthy individuals. Interestingly, there was no significant lateralized activation during hemiplegic walking with FES assistance, which would seem to indicate that FES may help hemiplegic walking recover the balance in cortical activation. These results, which are worth verifying through additional research, suggest that FES used as a potential therapeutic strategy may play an important role in motor recovery after stroke.

Relation between Cortical Activation and Effort during Robot-Mediated Walking in Healthy People: A Functional Near-Infrared Spectroscopy Neuroimaging Study (fNIRS)

Force and effort are important components of a motor task that can impact rehabilitation effectiveness. However, few studies have evaluated the impact of these factors on cortical activation during gait. The purpose of the study was to investigate the relation between cortical activation and effort required during exoskeleton-mediated gait at different levels of physical assistance in healthy individuals. Twenty-four healthy participants walked 10 m with an exoskeleton that provided four levels of assistance: 100%, 50%, 0%, and 25% resistance. Functional near-infrared spectroscopy (fNIRS) was used to measure cerebral flow dynamics with a 20-channel (plus two reference channels) device that covered most cortical motor regions bilaterally. We measured changes in oxyhemoglobin (HbO₂) and deoxyhemoglobin (HbR). According to HbO₂ levels, cortical activation only differed slightly between the assisted conditions and rest. In contrast, bilateral and widespread cortical activation occurred during the two unassisted conditions (somatosensory, somatosensory association, primary motor, premotor, and supplementary motor cortices). A similar pattern was seen for HbR levels, with a smaller number of significant channels than for HbO₂. These results confirmed the hypothesis that there is a relation between cortical activation and level of effort during gait. This finding should help to optimize neurological rehabilitation strategies to drive neuroplasticity.

Involvement of the Rostromedial Prefrontal Cortex in Human-Robot Interaction: fNIRS Evidence From a Robot-Assisted Motor Task

Assistive exoskeleton robots are being widely applied in neurorehabilitation to improve upper-limb motor and somatosensory functions. During robot-assisted exercises, the central nervous system appears to highly attend to external information-processing (IP) to efficiently interact with robotic assistance. However, the neural mechanisms underlying this process remain unclear. The rostromedial prefrontal cortex (rmPFC) may be the core of the executive resource allocation that generates biases in the allocation of processing resources toward an external IP according to current behavioral demands. Here, we used functional near-infrared spectroscopy to investigate the cortical activation associated with executive resource allocation during a robot-assisted motor task. During data acquisition, participants performed a right-arm motor task using elbow flexion-extension movements in three different loading conditions: robotic assistive loading (ROB), resistive loading (RES), and non-loading (NON). Participants were asked to strive for kinematic consistency in their movements. A one-way repeated measures analysis of variance and general linear model-based methods were employed to examine task-related activity. We demonstrated that hemodynamic responses in the ventral and dorsal rmPFC were higher during ROB than during NON. Moreover, greater hemodynamic responses in the ventral rmPFC were observed during ROB than during RES. Increased activation in ventral and dorsal rmPFC subregions may be involved in the executive resource allocation that prioritizes external IP during human-robot interactions. In conclusion, these findings provide novel insights regarding the involvement of executive control during a robot-assisted motor task.

Comparative effects of passive and active mode robot-assisted gait training on brain and muscular activities in sub-acute and chronic stroke

BACKGROUND

Robot-assisted gait training (RAGT) was initially developed based on the passive controlled (PC) mode, where the target or ideal locomotor kinematic trajectory is predefined and a patient basically 'rides' the robot instead of actively participating in the actual locomotor relearning process. A new insightful contemporary neuroscience and mechatronic evidence suggest that robotic-based locomotor relearning can be best achieved through active interactive (AI) mode rather than PC mode.

OBJECTIVE

The purpose of this study was to compare the pattern of gait-related cortical activity, specifically gait event-related spectral perturbations (ERSPs), and muscle activity from the tibialis anterior (TA) and clinical functional tests in subacute and chronic stroke patients during robot-assisted gait training (RAGT) in passive controlled (PC) and active interactive (AI) modes.

METHODS

The present study involves a two-group pretest-posttest design in which two groups (i.e., PC-RAGT group and AI-RAGT group) of 14 stroke subjects were measured to assess changes in ERSPs, the muscle activation of TA, and the clinical functional tests, following 15- 18 sessions of intervention according to the protocol of each group.

RESULTS

Our preliminary results demonstrated that the power in the μ band (8- 12 Hz) was increased in the leg area of sensorimotor cortex (SMC) and supplementary motor area (SMA) at post-intervention as compared to pre-intervention in both groups. Such cortical neuroplasticity change was associated with TA muscle activity during gait and functional independence in functional ambulation category (FAC) and motor coordination in Fugl- Meyer Assessment for lower extremity (FMA-LE) test as well as spasticity in the modified Ashworth scale (MAS) measures.

CONCLUSIONS

We have first developed a novel neuroimaging experimental paradigm which distinguished gait event related cortical involvement between pre- and post-intervention with PC-RAGT and AI-RAGT in individuals with subacute and chronic hemiparetic stroke.

Frequency-Dependent Effects on Coordination and Prefrontal Hemodynamics During Finger Force Production Tasks

Behavioral stability partially depends on the variability of net outcomes by means of the co-varied adjustment of individual elements such as multi-finger forces. The properties of cyclic actions affect stability and variability of the performance as well as the activation of the prefrontal cortex that is an origin of subcortical structure for the coordinative actions. Little research has been done on the issue of the relationship between stability and neuronal response. The purpose of the study was to investigate the changes in the neural response, particularly at the prefrontal cortex, to the frequencies of isometric cyclic finger force production. The main experimental task was to produce finger forces while matching the produced force to sine-wave templates as accurately as possible. Also, the hemodynamics responses of the prefrontal cortex, including oxy-hemoglobin concentration (ΔHbO) and the functional connectivity, were measured using functional near-infrared spectroscopy. The frequency conditions comprised 0.1, 1, and 2 Hz. The uncontrolled manifold (UCM) approach was applied to compute synergy indices in time-series. The relative phase (RP), the coefficient of variation (CV) of the peak and trough force values were computed as the indices of performance accuracy. The statistical parametric mapping (SPM) was implemented to compare the synergy indices of three frequency conditions in time-series. A less accurate performance in the high-frequency condition was caused not by the RP, but mainly by the inconsistent peak force values (CV; p < 0.01, η p 2 = 0.90). The SPM analysis revealed that the synergy indices were larger in the low-frequency than in high-frequency conditions. Further, the ΔHbO remained unchanged under all frequency conditions, while the functional connectivity decreased with an increase in the frequency of cyclic force production. The current results suggested that the concurrent activation of the prefrontal region mainly depends on the frequency of cyclic force production, which was associated with the strength of stability indices and performance errors. The current study is the first work to uncover the effect of frequency on the multi-finger synergies as to the hemodynamic response in the prefrontal cortex, which possibly provides a clue of the neural mechanism of synergy formation and its changes.

Classification accuracy and functional difference prediction in different brain regions of drug abuser prefrontal lobe basing on machine-learning

Taking different types of addictive drugs such as methamphetamine, heroin, and mixed drugs causes brain functional Changes. Based on the prefrontal functional near-infrared spectroscopy, this study was designed with an experimental paradigm that included the induction of resting and drug addiction cravings. Hemoglobin concentrations of 30 drug users (10 on methamphetamine, 10 on heroin, and 10 on mixed type) were collected. For these three types of individuals, the convolutional neural networks (CNN) was designed to classify eight Brodmann areas and the entire prefrontal area, and the average accuracy of the three classifications on each functional area was obtained. As a result, the classification accuracy was lower on the left side than on the right in the dorsolateral prefrontal cortex (DLPFC) of the drug users, while it was higher on the left than on the right in the ventrolateral prefrontal cortex (VLPFC), frontopolar prefrontal cortex (FPC), and orbitofrontal cortex (OFC). Then the differences in eight functional areas between the three types of individuals were statistically analyzed, and results showed significant differences in the right VLPFC and right OFC.

Motor Cortical Activation Assessment in Progressive Multiple Sclerosis Patients Enrolled in Gait Rehabilitation: A Secondary Analysis of the RAGTIME Trial Assisted by Functional Near-Infrared Spectroscopy

This study aimed to determine cortical activation responses to two different rehabilitative programs, as measured through functional near-infrared spectroscopy (fNIRS). As a secondary analysis of the RAGTIME trial, we studied 24 patients with progressive multiple sclerosis (MS) and severe disability who were randomized to a regimen of robot-assisted gait training (RAGT) or overground walking (OW). Cortical activation during a treadmill walking task, assessed through fNIRS recordings from the motor and premotor cortexes (M1/PM), was calculated as the area under the curve (AUC) of oxyhemoglobin for each hemisphere and the total area (Tot-Oxy_AUC). Gait speed, endurance, and balance were also measured, along with five healthy control subjects. At baseline, Tot-Oxy_AUC during walking was significantly increased in MS patients compared to healthy people and was significantly higher for those with more severe disabilities; it was also inversely correlated with physical performance. After rehabilitation, significant opposite variations in Tot-Oxy_AUC were observed, with activity levels being increased after OW and decreased after RAGT (+242,080 ± 361,902 and −157,031 ± 172,496 arbitrary units, respectively; p = 0.002), particularly in patients who were trained at a lower speed. Greater reductions in the cortical activation of the more affected hemisphere were significantly related to improvements in gait speed (r = −0.42) and endurance (r = −0.44). Cortical activation, assessed through fNIRS, highlighted the brain activity in response to the type and intensity of rehabilitation.

Classifying Motion Intention of Step Length and Synchronous Walking Speed by Functional Near-Infrared Spectroscopy

In some patients who have suffered an amputation or spinal cord injury, walking ability may be degraded or deteriorated. Helping these patients walk independently on their own initiative is of great significance. This paper proposes a method to identify subjects’ motion intention under different levels of step length and synchronous walking speed by using functional near-infrared spectroscopy technology. Thirty-one healthy subjects were recruited to walk under six given sets of gait parameters (small step with low/midspeed, midstep with low/mid/high speed, and large step with midspeed). The channels were subdivided into more regions. More frequency bands (6 subbands on average in the range of 0-0.18 Hz) were decomposed by applying the wavelet packet method. Further, a genetic algorithm and a library for support vector machine algorithm were applied for selecting typical feature vectors, which were represented by important regions with partial important channels mentioned above. The walking speed recognition rate was 71.21% in different step length states, and the step length recognition rate was 71.21% in different walking speed states. This study explores the method of identifying motion intention in two-dimensional multivariate states. It lays the foundation for controlling walking-assistance equipment adaptively based on cerebral hemoglobin information.

Effects of passive and active training modes of upper-limb rehabilitation robot on cortical activation: a functional near-infrared spectroscopy study

OBJECTIVE

The purpose of this study is to investigate the cortical activation during passive and active training modes under different speeds of upper extremity rehabilitation robots.

METHODS

Twelve healthy subjects completed the active and passive training modes at various speeds (0.12, 0.18, and 0.24 m/s) for the right upper limb. The functional near-infrared spectroscopy (fNIRS) was used to measure the neural activities of the sensorimotor cortex (SMC), premotor cortex (PMC), supplementary motor area (SMA), and prefrontal cortex (PFC).

RESULTS

Both the active and passive training modes can activate SMC, PMC, SMA, and PFC. The activation level of active training is higher than that of passive training. At the speed of 0.12 m/s, there is no significant difference in the intensity of the two modes. However, at the speed of 0.24 m/s, there are significant differences between the two modes in activation levels of each region of interest (ROI) (P < 0.05) (SMC: F = 8.90, P = 0.003; PMC: F = 8.26, P = 0.005; SMA: F = 5.53, P = 0.023; PFC: F = 9.160, P = 0.003).

CONCLUSION

This study mainly studied on the neural mechanisms of active and passive training modes at different speeds based on the end-effector upper-limb rehabilitation robot. Slow, active training better facilitated the cortical activation associated with cognition and motor control.See Video Abstract, http://links.lww.com/WNR/A621.

Five-day rehabilitation of patients undergoing total knee arthroplasty using an end-effector gait robot as a neuromodulation blending tool for deafferentation, weight offloading and stereotyped movement: Interim analysis

Deafferentation and weight offloading can increase brain and spinal motor neuron excitability, respectively. End-effector gait robots (EEGRs) can blend these effects with stereotyped movement-induced neuroplasticity. The authors aimed to evaluate the usefulness of EEGRs as a postoperative neuro-muscular rehabilitation tool. This prospective randomized controlled trial included patients who had undergone unilateral total knee arthroplasty (TKA). Patients were randomly allocated into two groups: one using a 200-step rehabilitation program in an EEGR or the other using a walker on a floor (WF) three times a day for five weekdays. The two groups were compared by electrophysiological and biomechanical methods. Since there were no more enrollments due to funding issues, interim analysis was performed. Twelve patients were assigned to the EEGR group and eight patients were assigned to the WF group. Although the muscle volume of the quadriceps and hamstring did not differ between the two groups, the normalized peak torque of the operated knee flexors (11.28 ± 16.04 Nm/kg) was improved in the EEGR group compared to that of the operated knee flexors in the WF group (4.25 ± 14.26 Nm/kg) (p = 0.04). The normalized compound motor action potentials of the vastus medialis (VM) and biceps femoris (BF) were improved in the EEGR group (p < 0.05). However, the normalized real-time peak amplitude and total, mean area under the curve of VM were decreased during rehabilitation in the EEGR group (p < 0.05). No significant differences were found between operated and non-operated knees in the EEGR group. Five-day EEGR-assisted rehabilitation induced strengthening in the knee flexors and the muscular reactivation of the BF and VM after TKA, while reducing the real-time use of the VM. This observation may suggest the feasibility of this technique: EEGR modulated the neuronal system of the patients rather than training their muscles. However, because the study was underpowered, all of the findings should be interpreted with the utmost caution.

Neuroplastic effects of end-effector robotic gait training for hemiparetic stroke: a randomised controlled trial

Detecting neuroplastic changes during locomotor neurorehabilitation is crucial for independent primal motor behaviours. However, long-term locomotor training-related neuroplasticity remains unexplored. We compared the effects of end-effector robot-assisted gait training (E-RAGT) and bodyweight-supported treadmill training (BWST) on cortical activation in individuals with hemiparetic stroke. Twenty-three men and five women aged 53.2 ± 11.2 years were recruited and randomly assigned to participate in E-RAGT (n = 14) or BWST (n = 14) for 30 min/day, 5 days/week, for 4 weeks. Cortical activity, lower limb motor function, and gait speed were evaluated before and after training. Activation of the primary sensorimotor cortex, supplementary motor area, and premotor cortex in the affected hemisphere significantly increased only in the E-RAGT group, although there were no significant between-group differences. Clinical outcomes, including the Fugl-Meyer assessment (FMA), timed up and go test, and 10-m walk test scores, improved after training in both groups, with significantly better FMA scores in the E-RAGT group than in the BWST group. These findings suggest that E-RAGT effectively improves neuroplastic outcomes in hemiparetic stroke, although its superiority over conventional training remains unclear. This may have clinical implications and provides insight for clinicians interested in locomotor neurorehabilitation after hemiparetic stroke.Trial Registration: ClinicalTrials.gov Identifier NCT04054739 (12/08/2019).

Cortical Tasks-Based Optimal Filter Selection: An fNIRS Study

Functional near-infrared spectroscopy (fNIRS) is one of the latest noninvasive brain function measuring technique that has been used for the purpose of brain-computer interfacing (BCI). In this paper, we compare and analyze the effect of six most commonly used filtering techniques (i.e., Gaussian, Butterworth, Kalman, hemodynamic response filter (hrf), Wiener, and finite impulse response) on classification accuracies of fNIRS-BCI. To conclude with the best optimal filter for a specific cortical task owing to a specific cortical region, we divided our experimental tasks according to the three main cortical regions: prefrontal, motor, and visual cortex. Three different experiments were performed for prefrontal and motor execution tasks while one for visual stimuli. The tasks performed for prefrontal include rest (R) vs mental arithmetic (MA), R vs object rotation (OB), and OB vs MA. Similarly, for motor execution, R vs left finger tapping (LFT), R vs right finger tapping (RFT), and LFT vs RFT. Likewise, for the visual cortex, R vs visual stimuli (VS) task. These experiments were performed for ten trials with five subjects. For consistency among extracted data, six statistical features were evaluated using oxygenated hemoglobin, namely, slope, mean, peak, kurtosis, skewness, and variance. Combination of these six features was used to classify data by the nonlinear support vector machine (SVM). The classification accuracies obtained from SVM by using hrf and Gaussian were significantly higher for R vs MA, R vs OB, R vs RFT, and R vs VS and Wiener filter for OB vs MA. Similarly, for R vs LFT and LFT vs RFT, hrf was found to be significant p<0.05. These results show the feasibility of using hrf for effective removal of noises from fNIRS data.

Electroencephalographic Functional Connectivity With the Tacit Learning System Prosthetic Hand: A Case Series Using Motor Imagery

We previously created a prosthetic hand with a tacit learning system (TLS) that automatically supports the control of forearm pronosupination. This myoelectric prosthetic hand enables sensory feedback and flexible motor output, which allows users to move efficiently with minimal burden. In this study, we investigated whether electroencephalography can be used to analyze the influence of the auxiliary function of the TLS on brain function. Three male participants who had sustained below-elbow amputations and were myoelectric prosthesis users performed a series of physical movement trials with the TLS inactivated and activated. Trials were video recorded and a sequence of videos was prepared to represent each individual's own use while the system was inactivated and activated. In a subsequent motor imagery phase during which electroencephalography (EEG) signals were collected, each participant was asked to watch both videos of themself while actively imagining the physical movement depicted. Differences in mean cortical current and amplitude envelope correlation (AEC) values between supplementary motor areas (SMA) and each vertex were calculated. For all participants, there were differences in the mean cortical current generated by the motor imagery tasks when the TLS inactivated and activated conditions were compared. The AEC values were higher during the movement imagery task with TLS activation, although their distribution on the cortex varied between the three individuals. In both S1 and other brain areas, AEC values increased in conditions with the TLS activated. Evidence from this case series indicates that, in addition to motor control, TLS may change sensory stimulus recognition.

Detecting self-paced walking intention based on fNIRS technology for the development of BCI

Since more and more elderly people suffer from lower extremity movement problems, it is of great social significance to assist persons with motor dysfunction to walk independently again and reduce the burden on caregivers. The self-paced walking intention, which could increase the ability of self-control on the start and stop of motion, was studied by applying brain-computer interface (BCI) technology, a novel research field. The cerebral hemoglobin signal, which was obtained from 30 subjects by applying functional near-infrared spectroscopy (fNIRS) technology, was processed to detect self-paced walking intention in this paper. Teager-Kaiser energy was extracted at each sampling point for five sub-bands (0.0095~0.021 Hz, 0.021~0.052 Hz, 0.052~0.145 Hz, 0.145~0.6 Hz, and 0.6~2.0 Hz). Gradient boosting decision tree (GBDT) was then utilized to establish the detecting model in real-time. The proposed method had a good performance to detect the walking intention and passed the pseudo-online test with a true positive rate of 100% (80/80), a false positive rate of 2.91% (4822/165171), and a detection latency of 0.39 ± 1.06 s. GBDT method had an area under the curve value of 0.944 and was 0.125 (p < 0.001) higher than linear discriminant analysis (LDA). The results reflected that it is feasible to decode self-paced walking intention by applying fNIRS technology. This study lays a foundation for applying fNIRS-based BCI technology to control walking assistive devices practically. Graphical abstract Graphical representation of the detecting process for pseudo-online test. The lower figure is a partial enlargement of the upper figure. In the lower figure, the blue line represents the probability of walking predicted by GBDT without smoothing and the orange-red line represents the smoothed probability. The dark-red ellipse shows the effect of the smoothing-threshold method.

Increased gait variability during robot-assisted walking is accompanied by increased sensorimotor brain activity in healthy people

BACKGROUND

Gait disorders are major symptoms of neurological diseases affecting the quality of life. Interventions that restore walking and allow patients to maintain safe and independent mobility are essential. Robot-assisted gait training (RAGT) proved to be a promising treatment for restoring and improving the ability to walk. Due to heterogenuous study designs and fragmentary knowlegde about the neural correlates associated with RAGT and the relation to motor recovery, guidelines for an individually optimized therapy can hardly be derived. To optimize robotic rehabilitation, it is crucial to understand how robotic assistance affect locomotor control and its underlying brain activity. Thus, this study aimed to investigate the effects of robotic assistance (RA) during treadmill walking (TW) on cortical activity and the relationship between RA-related changes of cortical activity and biomechanical gait characteristics.

METHODS

Twelve healthy, right-handed volunteers (9 females; M = 25 ± 4 years) performed unassisted walking (UAW) and robot-assisted walking (RAW) trials on a treadmill, at 2.8 km/h, in a randomized, within-subject design. Ground reaction forces (GRFs) provided information regarding the individual gait patterns, while brain activity was examined by measuring cerebral hemodynamic changes in brain regions associated with the cortical locomotor network, including the sensorimotor cortex (SMC), premotor cortex (PMC) and supplementary motor area (SMA), using functional near-infrared spectroscopy (fNIRS).

RESULTS

A statistically significant increase in brain activity was observed in the SMC compared with the PMC and SMA (p < 0.05), and a classical double bump in the vertical GRF was observed during both UAW and RAW throughout the stance phase. However, intraindividual gait variability increased significantly with RA and was correlated with increased brain activity in the SMC (p = 0.05; r = 0.57).

CONCLUSIONS

On the one hand, robotic guidance could generate sensory feedback that promotes active participation, leading to increased gait variability and somatosensory brain activity. On the other hand, changes in brain activity and biomechanical gait characteristics may also be due to the sensory feedback of the robot, which disrupts the cortical network of automated walking in healthy individuals. More comprehensive neurophysiological studies both in laboratory and in clinical settings are necessary to investigate the entire brain network associated with RAW.

Immediate muscle strengthening by an end-effector type gait robot with reduced real-time use of leg muscles: A case series and review of literature

BACKGROUND

De-afferentation or non-weight bearing induces rapid cortical and spinal α-motor neuron excitability. Author supposed that an end-effector type gait robot (EEGR) could provide patients with a training condition that was specific enough to activate rapid cortical/spinal neuroplasticity, leading to immediate muscle strengthening. The electromyographic and biomechanical comparisons were conducted.

AIM

To compare the electromyographic activities of the thigh and shank muscles and isometric peak torque (PT) before and after walking training on a floor or in the end-effector gait robot.

METHODS

Twelve outpatients without ambulatory dysfunction were recruited. Order of two interventions (5-min training on a floor at a comfortable pace or training in an EEGR with non-weight bearing on their feet and 100% guidance force at 2.1 km/h) were randomly chosen. Isometric PT, maximal ratio of torque development, amplitude of compound motor action potential (CMAP), and area under the curve (AUC) were evaluated before and 10 min after both interventions.

RESULTS

The degree of PT improvement of the dominant knee flexors was larger in the EEGR than on the floor (9.6 ± 22.4 Nm/BW, P < 0.01). The EEGR-trained patients had greater PT improvement of the dominant knee extensors than those who trained on the floor (4.5 ± 28.1 Nm/BW, P < 0.01). However, all electromyographic activities of the thigh and shank muscles (peak CMAP, mean and peak AUC) were significantly lower for the use of the EEGR than walking on the floor.

CONCLUSION

Immediate strengthening of the knee flexors and extensors was induced after the 5-min EEGR training, despite reduced muscular use.

Current State and Future Prospects of EEG and fNIRS in Robot-Assisted Gait Rehabilitation: A Brief Review

Gait and balance impairments are frequently considered as the most significant concerns among individuals suffering from neurological diseases. Robot-assisted gait training (RAGT) has shown to be a promising neurorehabilitation intervention to improve gait recovery in patients following stroke or brain injury by potentially initiating neuroplastic changes. However, the neurophysiological processes underlying gait recovery through RAGT remain poorly understood. As non-invasive, portable neuroimaging techniques, electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) provide new insights regarding the neurophysiological processes occurring during RAGT by measuring different perspectives of brain activity. Due to spatial information about changes in cortical activation patterns and the rapid temporal resolution of bioelectrical changes, more features correlated with brain activation and connectivity can be identified when using fused EEG-fNIRS, thus leading to a detailed understanding of neurophysiological mechanisms underlying motor behavior and impairments due to neurological diseases. Therefore, multi-modal integrations of EEG-fNIRS appear promising for the characterization of neurovascular coupling in brain network dynamics induced by RAGT. In this brief review, we surveyed neuroimaging studies focusing specifically on robotic gait rehabilitation. While previous studies have examined either EEG or fNIRS with respect to RAGT, a multi-modal integration of both approaches is lacking. Based on comparable studies using fused EEG-fNIRS integrations either for guiding non-invasive brain stimulation or as part of brain-machine interface paradigms, the potential of this methodologically combined approach in RAGT is discussed. Future research directions and perspectives for targeted, individualized gait recovery that optimize the outcome and efficiency of RAGT in neurorehabilitation were further derived.

Validating attentive locomotion training using interactive treadmill: an fNIRS study

BACKGROUND

Existing treadmill-based locomotion training, which has been used for gait function recovery, still has limitations, such as less attentive training. Interactive treadmills (ITMs) were developed to overcome these limitations, but it has not yet been verified that ITMs can make the user pay closer attention to walk training.

METHODS

An experimental comparison between ITMs and conventional treadmills was conducted by measuring the level of the user's attention using functional near-infrared spectroscopy (fNIRS). To consider the effect of task complexity on the subject's attention, we provided two (slow and fast) speed conditions for walking on both treadmills.

RESULTS

Both the cortical activity images and oxygenated hemoglobin (oxyHb) changes showed that the level of attention to walking induced by the ITM was significantly higher than that induced by the conventional treadmill. We found that the walking speed on the ITM also affected the level of attention.

CONCLUSION

ITM-based locomotion training would be a promising solution to the limitations of existing treadmill-based locomotion training currently used to improve gait function recovery.

TRIAL REGISTRATION

DGIST-HR-150309-03-02 . Registered 01 March 2015.

Supra-Spinal Modulation Of Walking In Healthy Individuals And Persons With Multiple Sclerosis: A fNIRS Mobile Imaging Study

Multiple sclerosis (MS) is one of the neurodegenerative diseases that damage the nervous system and inflicts cognitive and motor deficits. In motor domain, MS mainly causes slower gait resulting in challenges in activities of daily living. Premotor cortices are affected by MS, where several imaging studies have reported re-organization in the activity and connectivity of these regions. Recent advancements in mobile imaging technologies and signal processing techniques have made it possible to study supraspinal modulation of walking in able-bodied individuals and persons with injuries or neurological disorders. Functional near-infrared spectroscopy (fNIRS), for example, was used in studying dual-tasking in MS population. In the current study, we used fNIRS to record activities of premotor and supplementary motor areas in MS and healthy populations during standing and walking. Fourteen healthy controls and 14 persons with MS were tested during overground walking. Results show higher right premotor cortex activities compared with left premotor and bilateral supplementary motor areas in the MS group. In the healthy control group, activity was higher during walking in all the four studied brain regions. These results confirm the role of the premotor cortices in movement planning and in modulating walking activities; they also confirm that individuals with mild MS have a similar premotor control strategy as healthy controls while performing the same physical task.

Pilot Study on Gait Classification Using fNIRS Signals

Rehabilitation training is essential for motor dysfunction patients, and the training through their subjective motion intention, comparing to passive training, is more conducive to rehabilitation. This study proposes a method to identify motion intention of different walking states under the normal environment, by using the functional near-infrared spectroscopy (fNIRS) technology. Twenty-two healthy subjects were recruited to walk with three different gaits (including small-step with low-speed, small-step with midspeed, midstep with low-speed). The wavelet packet decomposition was used to find out the main characteristic channels in different motion states, and these channels with links in frequency and space were combined to define as feature vectors. According to different permutations and combinations of all feature vectors, a library for support vector machines (libSVM) was used to achieve the best recognition model. Finally, the accuracy rate of these three walking states was 78.79%. This study implemented the classification of different states' motion intention by using the fNIRS technology. It laid a foundation to apply the classified motion intention of different states timely, to help severe motor dysfunction patients control a walking-assistive device for rehabilitation training, so as to help them restore independent walking abilities and reduce the economic burdens on society.

Effects of Walkbot gait training on kinematics, kinetics, and clinical gait function in paraplegia and quadriplegia

BACKGROUND

The robotic-assisted gait training (RAGT) system has gained recognition as an innovative, effective paradigm to improve functional ambulation and activities of daily living in spinal cord injury and stroke. However, the effects of the Walkbot robotic-assisted gait training system with a specialized hip-knee-ankle actuator have never been examined in the paraplegia and quadriplegia population.

OBJECTIVE

The aim of this study was to determine the long-term effects of Walkbot training on clinical for hips and knee stiffness in individuals with paraplegia or quadriplegia.

METHODS

Nine adults with subacute or chronic paraplegia resulting from spinal cord injury or quadriplegia resulting from cerebral vascular accident (CVA) and/or hypoxia underwent progressive conventional gait retraining combined with the Walkbot RAGT for 5 days/week over an average of 43 sessions for 8 weeks. Clinical outcomes were measured with the Functional Ambulation Category (FAC), Modified Rankin Scale (MRS), Korean version of the Modified Barthel Index (K-MBI), Modified Ashworth Scale (MAS). Kinetic and kinematic data were collected via a built-in Walkbot program.

RESULTS

Wilcoxon signed-rank tests showed significant positive intervention effects on K-MBI, maximal hip flexion and extension, maximal knee flexion, active torque in the knee joint, resistive torque, and stiffness in the hip joint (P <  0.05). These findings suggest that the Walkbot RAGT was effective for improving knee and hip kinematics and the active knee joint moment while decreasing hip resistive force. These improvements were associated with functional recovery in gait, balance, mobility and daily activities.

CONCLUSIONS

These findings suggest that the Walkbot RAGT was effective for improving knee and hip kinematics and the active knee joint moment while decreasing hip resistive force. This is the first clinical evidence for intensive, long-term effects of the Walkbot RAGT on active or resistive moments and stiffness associated with spasticity and functional mobility in individuals with subacute or chronic paraplegia or quadriplegia who had reached a plateau in motor recovery after conventional therapy.

Functional near-infrared spectroscopy in movement science: a systematic review on cortical activity in postural and walking tasks

Safe locomotion is a crucial aspect of human daily living that requires well-functioning motor control processes. The human neuromotor control of daily activities such as walking relies on the complex interaction of subcortical and cortical areas. Technical developments in neuroimaging systems allow the quantification of cortical activation during the execution of motor tasks. Functional near-infrared spectroscopy (fNIRS) seems to be a promising tool to monitor motor control processes in cortical areas in freely moving subjects. However, so far, there is no established standardized protocol regarding the application and data processing of fNIRS signals that limits the comparability among studies. Hence, this systematic review aimed to summarize the current knowledge about application and data processing in fNIRS studies dealing with walking or postural tasks. Fifty-six articles of an initial yield of 1420 publications were reviewed and information about methodology, data processing, and findings were extracted. Based on our results, we outline the recommendations with respect to the design and data processing of fNIRS studies. Future perspectives of measuring fNIRS signals in movement science are discussed.

Measuring cortical motor hemodynamics during assisted stepping - An fNIRS feasibility study of using a walker

Walkers are commonly prescribed worldwide to individuals unable to walk independently. Walker usage leads to improved postural control and voluntary movement during step. In the present study, we aimed to provide a concept-proof on the feasibility of an event-related protocol integrating the analyses of biomechanical variables of step initiation and functional near-infrared spectroscopy (fNIRS) to measure activation of the supplementary motor area (SMA) while using a walker. Healthy young participants were tested while stepping with versus without the use of the walker. Behavioral analysis showed that anticipatory postural adjustments (APA) decreased when supporting the body weight on the walker. Delta (without-with) of activation magnitude of the muscle tibialis anterior was positively correlated to the delta of deoxyhemoglobin concentration changes in the SMA. The novelty of this study is the development of a protocol to assess brain function together with biomechanical analysis during the use of a walker. The method sheds light to the potential utility of combining fNIRS and biomechanical assessment during assistive step initiation, which can represent a new opportunity to study populations with mobility deficits.

Neuroimaging of Human Balance Control: A Systematic Review

This review examined 83 articles using neuroimaging modalities to investigate the neural correlates underlying static and dynamic human balance control, with aims to support future mobile neuroimaging research in the balance control domain. Furthermore, this review analyzed the mobility of the neuroimaging hardware and research paradigms as well as the analytical methodology to identify and remove movement artifact in the acquired brain signal. We found that the majority of static balance control tasks utilized mechanical perturbations to invoke feet-in-place responses (27 out of 38 studies), while cognitive dual-task conditions were commonly used to challenge balance in dynamic balance control tasks (20 out of 32 studies). While frequency analysis and event related potential characteristics supported enhanced brain activation during static balance control, that in dynamic balance control studies was supported by spatial and frequency analysis. Twenty-three of the 50 studies utilizing EEG utilized independent component analysis to remove movement artifacts from the acquired brain signals. Lastly, only eight studies used truly mobile neuroimaging hardware systems. This review provides evidence to support an increase in brain activation in balance control tasks, regardless of mechanical, cognitive, or sensory challenges. Furthermore, the current body of literature demonstrates the use of advanced signal processing methodologies to analyze brain activity during movement. However, the static nature of neuroimaging hardware and conventional balance control paradigms prevent full mobility and limit our knowledge of neural mechanisms underlying balance control.

The Relationship between Neurocircuitry Dysfunctions and Attention Deficit Hyperactivity Disorder: A Review

The prefrontal cortex is the superlative structure of brain that needs the longest developmental and maturational duration that highlights the region of attention deficit hyperactivity disorder (ADHD) in neuroimaging studies. Prefrontal cortex functions generate enormously complex and its abundant feedback neurocircuitries with subcortical structures such as striatum and thalamus established through dual neural fibers. These microneurocircuitries are called corticostriatothalamocortical (CSTC) circuits. The CSTC circuits paly an essential role in flexible behaviors. The impaired circuits increase the risk of behavioral and psychological symptoms. ADHD is an especial developmental stage of paediatric disease. It has been reported that the CSTC circuits dysfunctions in ADHD are related to homologous symptoms. This study aimed to review the symptoms of ADHD and discuss the recent advances on the effects of the disease as well as the new progress of treatments with each circuit.