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Predicting the performance of motor imagery in stroke patients : multivariate pattern analysis of functional MRI data

PARK CH; CHANG WH; LEE M; KWON GH; KIM L; KIM ST; KIM YH
NEUROREHABIL NEURAL REPAIR , 2015, vol. 29, n° 3, p. 247-254
Doc n°: 174601
Localisation : Documentation IRR

D.O.I. : http://dx.doi.org/DOI:10.1177/1545968314543308
Descripteurs : AF21 - ACCIDENTS VASCULAIRES CEREBRAUX, AK15 - IRM

In a brain-computer interface for stroke rehabilitation, motor
imagery is a preferred means for providing a gateway to an effector action or
behavior. However, stroke patients often exhibit failure to comply with motor
imagery, and therefore their motor imagery performance is highly variable.
OBJECTIVE: We sought to identify motor cortical areas responsible for motor
imagery performance in stroke patients, specifically by using a multivariate
pattern analysis of functional magnetic resonance imaging data. METHODS: We
adopted an imaginary finger tapping task in which motor imagery performance could
be monitored for 12 chronic stroke patients with subcortical infarcts and 12 age-
and sex-matched healthy controls. We identified the typical activation pattern
elicited for motor imagery in healthy controls, as computed over the voxels
within each searchlight in the motor cortex. Then we measured the similarity of
each individual's activation pattern to the typical activation pattern. RESULTS:
In terms of activation levels, the stroke patients showed no activation in the
ipsilesional primary motor cortex (M1); in terms of activation patterns, they
showed lower similarity to the typical activation pattern in the area than the
healthy controls. Furthermore, the stroke patients were better able to perform
motor imagery if their activation patterns in the bilateral supplementary motor
areas and ipsilesional M1 were close to the typical activation pattern.
CONCLUSIONS: These findings suggest functional roles of the motor cortical areas
for compliance with motor imagery in stroke, which can be applied to the
implementation of motor imagery-based brain-computer interface for stroke rehabilitation.
CI - (c) The Author(s) 2014.

Langue : ANGLAIS

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