Article

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.
CI - (c) The Author(s) 2014.

Langue : ANGLAIS