Corticospinal Excitability as a Predictor of Functional Gains at the Affected Upper Limb Following Robotic Training in Chronic Stroke Survivors

I wish they would just take all these damned studies on robotic training and just make a stroke protocol out of it. Stop studying it and deliver some results! That is what a great stroke association would do instead of just putting out press releases.
http://nnr.sagepub.com/content/28/9/819?etoc

1. Marie-Hélène Milot, PhD1,2
2. Steven J. Spencer, PhD2
3. Vicky Chan2
4. James P. Allington, PhD2
5. Julius Klein, PhD2
6. Cathy Chou2
7. Kristin Pearson-Fuhrhop, PhD2
8. James E. Bobrow, PhD2
9. David J. Reinkensmeyer, PhD2
10. Steven C. Cramer, MD2

1. ¹Université de Sherbrooke, Sherbrooke, Quebec, Canada
2. ²University of California; Irvine, CA, USA

1. Marie-Hélène Milot, Université de Sherbrooke, Faculté de médecine et des sciences de la santé, École de réadaptation, Centre de recherche sur le vieillissement, 1036 Belvédère sud, Sherbrooke, Québec, J1H 4C4, Canada. Email: marie-helene.milot@usherbrooke.ca

Abstract

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.