Cathy Chou
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Cathy Chou has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- David J. Reinkensmeyer
Department of Mechanical and Aerospace Engineering, University of California Irvine, Irvine, CA 92697, USA
2 shared publications - James E. Bobrow
Mechanical and Aerospace Engineering Department, University of California, Irvine, Irvine CA 92697 USA
2 shared publications - James P. Allington
University of California; Irvine, CA, USA
2 shared publications - Julius Klein
University of California; Irvine, CA, USA
2 shared publications - Marie-Hélène Milot
Faculté de Médecine et des Sciences de la Santé, École de Réadaptation, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
2 shared publications - Steven C. Cramer
Department of Neurology, University of California, Los Angeles (S.C.C.).
2 shared publications
Latest Publications
- Open Access
- 18 March 2014
- Published by SAGE Publications in Neurorehabilitation and Neural Repair
- Vol. 28 (9) , 819-827
Corticospinal Excitability as a Predictor of Functional Gains at the Affected Upper Limb Following Robotic Training in Chronic Stroke Survivors
Background . Robotic training can help improve function of a paretic limb following a stroke, but individuals respond differently to the training. A predictor of functional gains might improve the ability to select those individuals more likely to benefit from robot-based therapy. Studies evaluating predictors of functional improvement after a robotic training are scarce. One study has found that white matter tract integrity predicts functional gains following a robotic training of the hand and wrist. Objective . To determine the predictive ability of behavioral and brain measures in order to improve selection of individuals for robotic training. Methods : Twenty subjects with chronic stroke participated in an 8-week course of robotic exoskeletal training for the arm. Before training, a clinical evaluation, functional magnetic resonance imaging (fMRI), diffusion tensor imaging, and transcranial magnetic stimulation (TMS) were each measured as predictors. Final functional gain was defined as change in the Box and Block Test (BBT). Measures significant in bivariate analysis were fed into a multivariate linear regression model. Results . Training was associated with an average gain of 6 ± 5 blocks on the BBT ( P < .0001). Bivariate analysis revealed that lower baseline motor-evoked potential (MEP) amplitude on TMS, and lower laterality M1 index on fMRI each significantly correlated with greater BBT change. In the multivariate linear regression analysis, baseline MEP magnitude was the only measure that remained significant. Conclusion . Subjects with lower baseline MEP magnitude benefited the most from robotic training of the affected arm. These subjects might have reserve remaining for the training to boost corticospinal excitability, translating into functional gains.
- 18 March 2014
- Published by SAGE Publications in Neurorehabilitation and Neural Repair
- Vol. 28 (9) , 819-827
Open AccessCorticospinal Excitability as a Predictor of Functional Gains at the Affected Upper Limb Following Robotic Training in Chronic Stroke Survivors
Background . Robotic training can help improve function of a paretic limb following a stroke, but individuals respond differently to the training. A predictor of functional gains might improve the ability to select those individuals more likely to benefit from robot-based therapy. Studies evaluating predictors of functional improvement after a robotic training are scarce. One study has found that white matter tract integrity predicts functional gains following a robotic training of the hand and wrist. Objective . To determine the predictive ability of behavioral and brain measures in order to improve selection of individuals for robotic training. Methods : Twenty subjects with chronic stroke participated in an 8-week course of robotic exoskeletal training for the arm. Before training, a clinical evaluation, functional magnetic resonance imaging (fMRI), diffusion tensor imaging, and transcranial magnetic stimulation (TMS) were each measured as predictors. Final functional gain was defined as change in the Box and Block Test (BBT). Measures significant in bivariate analysis were fed into a multivariate linear regression model. Results . Training was associated with an average gain of 6 ± 5 blocks on the BBT ( P < .0001). Bivariate analysis revealed that lower baseline motor-evoked potential (MEP) amplitude on TMS, and lower laterality M1 index on fMRI each significantly correlated with greater BBT change. In the multivariate linear regression analysis, baseline MEP magnitude was the only measure that remained significant. Conclusion . Subjects with lower baseline MEP magnitude benefited the most from robotic training of the affected arm. These subjects might have reserve remaining for the training to boost corticospinal excitability, translating into functional gains.
- Open Access
- 1 January 2013
- Published by Springer Nature in Journal of NeuroEngineering and Rehabilitation
- Vol. 10 (1) , 112
A crossover pilot study evaluating the functional outcomes of two different types of robotic movement training in chronic stroke survivors using the arm exoskeleton BONES
To date, the limited degrees of freedom (DOF) of most robotic training devices hinders them from providing functional training following stroke. We developed a 6-DOF exoskeleton (“BONES”) that allows movement of the upper limb to assist in rehabilitation. The objectives of this pilot study were to evaluate the impact of training with BONES on function of the affected upper limb, and to assess whether multijoint functional robotic training would translate into greater gains in arm function than single joint robotic training also conducted with BONES.
- 1 January 2013
- Published by Springer Nature in Journal of NeuroEngineering and Rehabilitation
- Vol. 10 (1) , 112
Open AccessA crossover pilot study evaluating the functional outcomes of two different types of robotic movement training in chronic stroke survivors using the arm exoskeleton BONES
To date, the limited degrees of freedom (DOF) of most robotic training devices hinders them from providing functional training following stroke. We developed a 6-DOF exoskeleton (“BONES”) that allows movement of the upper limb to assist in rehabilitation. The objectives of this pilot study were to evaluate the impact of training with BONES on function of the affected upper limb, and to assess whether multijoint functional robotic training would translate into greater gains in arm function than single joint robotic training also conducted with BONES.