Resting-state functional connectivity predicts the ability to adapt arm reaching in a robot-mediated force field

Abstract

Motor deficits are common outcomes of neurological conditions such as stroke. In order to design personalised motor rehabilitation programmes such as robot-assisted therapy, it would be advantageous to predict how a patient might respond to such treatment. Spontaneous neural activity has been observed to predict differences in the ability to learn a new motor behaviour in both healthy and stroke populations. This study investigated whether spontaneous resting-state functional connectivity could predict the degree of motor adaptation of right (dominant) upper limb reaching in response to a robot-mediated force field. Spontaneous neural activity was measured using resting-state electroencephalography (EEG) in healthy adults before a single session of motor adaptation. The degree of beta frequency (β; 15-25 Hz) resting-state functional connectivity between contralateral electrodes overlying the left primary motor cortex (M1) and the anterior prefrontal cortex (aPFC) could predict the subsequent degree of motor adaptation. This result provides novel evidence for the functional significance of resting-state synchronization dynamics in predicting the degree of motor adaptation in a healthy sample. This study constitutes a promising first step towards the identification of patients who will likely gain most from using robot-mediated upper limb rehabilitation training based on simple measures of spontaneous neural activity.

Perspectives and Challenges in Robotic Neurorehabilitation

“…Furthermore, FC predicts the behavioral outcomes of rehabilitation protocols [209] as well as the recovery of motor function [168,207] in stroke subjects and also correlates with the level of clinical disability in early relapsing-remitting multiple sclerosis subjects [210]. As for the EEG, FC measurements follow spontaneous recovery [204], are related to motor deficits in stroke [211] and multiple sclerosis [206], and predict learning rate [212,213] in healthy subjects. Taken together, all these evidences suggest that an evaluation of the FC pre-and post-rehabilitation is a valuable tool to evaluate the effectiveness of the rehabilitation protocol administered with a newly developed robotic device [168].…”

Effect of multichannel transcranial direct current stimulation to reduce hypertonia in individuals with prolonged disorders of consciousness: A randomized controlled pilot study

“…Beta connectivity in the central (or M1) and frontal regions are widely considered linked to movement and decision making. For instance, degree of betafrequency resting-state functional connectivity between M1 and the anterior prefrontal cortex were found to predict subsequent degree of motor adaptation in healthy volunteers, which suggests that the resting-state synchronization dynamics can predict the degree of motor adaptation in a healthy population [21]. In stroke, beta coherence in the somatosensory areas is increased during movement planning and associated with velocity of movement [22].…”

AI and IoT-Enabled Smart Exoskeleton System for Rehabilitation of Paralyzed People in Connected Communities

“…The motor adaptability of the upper limb is predicted using resting-state functional connectivity. The system could identify effectiveness of robotic upper limb rehabilitation in different patients [18]. The clinical trials to investigate BCI training sessions' effectiveness on stroke patients with upper limb paralysis are being carried out.…”

“…The clinical trials to investigate BCI training sessions' effectiveness on stroke patients with upper limb paralysis are being carried out. The results of the trial indicate that the BCI based assistive devices are effective for post-stroke rehabilitation [18]. Human intentions measured through cortical potentials were used to control the upper-limb exoskeleton movements.…”