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

Abstract

BackgroundGait 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.MethodsTwelve 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).ResultsA 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).ConclusionsOn 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.

Passive, yet not inactive: robotic exoskeleton walking increases cortical activation dependent on task

“…While this is a possibility, the level of variability is similar to that reported in Koenraadt et al (2014) (n = 11, 10 repetitions/condition) [15]. Further, methodologically the signal-to-noise for this study is likely increased with greater number of trials per condition and larger sample size than some gait studies using fNIRS [16,27,36]. So, it is conceivable that the variability reflects actual individual variation in brain activation associated with robotic exoskeleton assisted gait.…”

“…In Koenraadt et al 2014, decreased oxyHb is found with normal walking and precision stepping in the SMA [15]. Berger et al 2019 showed decreased oxyHB for treadmill walking over SMA/PMC compared with the sensorimotor cortex [27]. In fMRI research, two studies found increased activation for passive compared with active stepping in prefrontal and posterior parietal area of the precuneus [10], and cingulate and medial frontal areas; motor response inhibition was proposed as a potential mechanism to explain the increased activity for passive stepping [11].…”

“…As variations in walking patterns can influence level of brain activation [27], and that the participant was given some active kinematic control during the Active condition, the Xsens MVN Awinda™ (Xsens Technologies B.V., Culver City, California, USA) motion tracking system was used to examine whether knee kinematics were similar across conditions. It served as a control for whether brain activation could be attributed to potential kinematic differences between conditions.…”

Neural Correlates of Age-Related Changes in Precise Grip Force Regulation: A Combined EEG-fNIRS Study

“…Thus, uncovering the link between age- and disease-related impairments of motor control and changes in brain activity to identify objective biomarkers for neuroplastic changes and motor recovery is a future goal in neurorehabilitation (Bönstrup et al, 2018 ; Coscia et al, 2019 ). Based on this, individualized training protocols and interventions with new technologies such as BMI and NIBS could be developed, which enable sustainable rehabilitation and optimization of the outcome and effectiveness of motor recovery (Semprini et al, 2018 ; Steinberg et al, 2019 ; Berger et al, 2019a , b ).…”

Evidence of Neuroplasticity Due to Acupuncture: An fNIRS Study

“…To investigate the brain conditions, several brain signal acquisition techniques are used. For acquiring the brain signal noninvasively, commonly used methods include electroencephalography [10,11], functional near-infrared spectroscopy (fNIRS) [12][13][14], functional magnetic resonance imaging (fMRI) [15], and magnetoencephalography [16][17]. fNIRS is a brain imaging technique that utilizes near-infrared light to detect the cerebral blood ow in the brain.…”