The neural and behavioral characteristics of dynamic adaptation of the wrist
Date
2022
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
University of Delaware
Abstract
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 rehabilitation 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 the 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. This thesis focuses on establishing the fundamental behavioral characteristics and neural correlates of neuromotor control of the wrist in healthy, young adults. ☐ In Part I, we present research focused on characterizing motor control of the wrist in a variety of dynamic environments during wrist pointing performed in an ex- oskeleton. In this work, we combine computational modeling with behavioral measures of wrist kinematics, kinetics and EMG to characterize feed-forward motor control pro- cesses utilized during task execution. Results from our experiments validate the use of a previously proposed two-state model for describing adaptation-based control of the wrist, and determined that deviations from model-predicted behavior were in part attributable to muscle co-contraction by healthy individuals during these tasks. ☐ In Part II, we present research focused on identifying brain regions associated with active motor control of the wrist during dynamic perturbations. In this work, we focus first on identifying brain regions engaged during task execution, for comparison with previous fMRI motor control experiments from the literature. After validating measurements taken with our device, we focused on methods for identifying adaptation specific processes during task execution. Our results identified a diffuse network of brain regions engaged in active adaptation, that included the contralateral primary motor cortex, the posterior parietal cortex, and the ipsilateral cerebellum of the right wrist used for task execution. ☐ In Part III, we present research focused on identying brain regions associated with consolidation of motor learning following task execution via changes in resting state functional connectivity (rsFC). In this work, we quantified changes in rsFC within task-localized brain regions within the cortico-thalamic-cerebellar (CTC) network and cortical sensorimotor network immediately following dynamic adaptation. Our results showed rsFC changed in two networks: rsFC increased within a CTC network and de- creased interhemispherically within the cortical sensorimotor network. Changes in both networks were associated to day one behavior, while only changes in the CTC network were associated with retention, indicative of memory formation. Behavioral variance decomposition analysis indicated that increases within the CTC network were specific to adaptation, while decreases in the cortical sensorimotor network were associated with alternate error reduction processes.
Description
Keywords
Adaptation, fMRI, Motor control, Resting state functional connectivity, Robotics