Article

Robot-assisted training may improve motor function in some
hemiparetic patients after stroke, but no physiological predictor of
rehabilitation progress is reliable. Resting state functional magnetic resonance
imaging (RS-fMRI) may serve as a method to assess and predict changes in the
motor network. OBJECTIVE: The authors examined the effects of upper-extremity
robot-assisted rehabilitation (MANUS) versus an electroencephalography-based
brain computer interface setup with motor imagery (MI EEG-BCI) and compared
pretreatment and posttreatment RS-fMRI. METHODS: In all, 9 adults with
upper-extremity paresis were trained for 4 weeks with a MANUS shoulder-elbow
robotic rehabilitation paradigm. In 3 participants, robot-assisted movement began
if no voluntary movement was initiated within 2 s. In 6 participants,
MI-BCI-based movement was initiated if motor imagery was detected. RS-fMRI and
Fugl-Meyer (FM) upper-extremity motor score were assessed before and after
training. RESULTS: . The individual gain in FM scores over 12 weeks could be
predicted from functional connectivity changes (FCCs) based on the pre-post
differences in RS-fMRI measurements. Both the FM gain and FCC were numerically
higher in the MI-BCI group. Increases in FC of the supplementary motor area, the
contralesional and ipsilesional motor cortex, and parts of the visuospatial
system with mostly association cortex regions and the cerebellum correlated with
individual upper-extremity function improvement. CONCLUSION: FCC may predict the
steepness of individual motor gains. Future training could therefore focus on
directly inducing these beneficial increases in FC. Evaluation of the treatment
groups suggests that MI is a potential facilitator of such neuroplasticity.

Langue : ANGLAIS