Studying motor control using fMRI-compatible robotics

Neurorehabilitation is centered on the idea that retraining motor function can be advanced by incorporating concepts of neuromotor control into therapy. Robot-mediated neurorehabilitation (RMN) uses robots as tools to execute neurorehabilitation protocols to retrain motor control following neural injury. Although many everyday manipulation tasks are performed using the hand and wrist, relatively few studies have focused on neuromotor control of the wrist, especially during human-robot interaction. To achieve the full potential of RMN for the wrist, we need to better understand the fundamental mechanisms involved in motor control of the wrist and the neural basis of these control networks in the brain.

One component of motor control that can be readily studied in the laboratory setting is motor adaptation. Motor adaptation refers to the process of adjusting our motor actions through error feedback, in response to learnable (stable) changes in task dynamics. Following adaptation, adapted motor plans are consolidated into motor memories that contribute to faster relearning on re-exposure to the same dynamic conditions. Behavioral studies show that this adaptation process is driven by at least two learning processes, one fast and one slow, that differentially contribute to motor memory formation. Currently, the neural representation of these learning processes is largely unknown. Our work focuses on using functional magnetic resonance imaging (fMRI) acquired during adaptation task performance to measure neural activity associated with these adaptation processes, to localize fast and slow learning networks.  We also utilize resting state fMRI (rs-fMRI), acquired pre- and post- adaptation task performance when subjects are at rest, to identify neural networks involved in motor memory formation via changes in functional connectivity.  

Additionally, impedance control, which refers to the control of our joint stiffness, can be used to reject perturbations in response to both stable and unstable changes in task dynamics. Our lab is working on using our fMRI-compatible robots to expose participants to different force fields (stable and unstable) to determine brain regions associated with impedance control and/or motor adaptation. In this work, we use task-based fMRI, EMG and computational models of motor learning to study how the brain learns to control movement of the wrist in stable and unstable dynamic environments.

Publications on this topic

A. J. Farrens, F. Sergi, ”Characterizing adaptive behavior of the wrist during lateral force perturbations”, IEEE/RAS-EMBS International Conference on Biomedical Robotics, New York City, November 2020, pre-print, available online. Nominated for Best Student Paper Award.

A. J. Farrens, F. Sergi, ”Neural correlates of dynamic adaptation and motor memory formation in two-degree of freedom wrist pointing”, IEEE/RAS-EMBS International Conference on Biomedical Robotics, New York City, November 2020, pre-print.

A. J. Farrens, F. Sergi“Identifying the neural representation of fast and slow states of neuromotor adaptation to force fields using fMRI”, 16th International Conference on Rehabilitation Robotics,pre-printavailable online.

A. J. Farrens, A. Zonnino, F. Sergi,“Effects of force-field adaptation on neural activation and resting-state functional connectivity”, International Conference on Neurorehabilitation, available onlinepre-print.

Print Friendly, PDF & Email