A Reliability Study on Brain Activation During Active and Passive Arm Movements Supported by an MRI-Compatible Robot
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In neurorehabilitation, longitudinal assessment of arm movement related brain function in patients with motor disability is challenging due to variability in task performance. MRI-compatible robots monitor and control task performance, yielding more reliable evaluation of brain function over time. The main goals of the present study were first to define the brain network activated while performing active and passive elbow movements with an MRI-compatible arm robot (MaRIA) in healthy subjects, and second to test the reproducibility of this activation over time. For the fMRI analysis two models were compared. In model 1 movement onset and duration were included, whereas in model 2 force and range of motion were added to the analysis. Reliability of brain activation was tested with several statistical approaches applied on individual and group activation maps and on summary statistics. The activated network included mainly the primary motor cortex, primary and secondary somatosensory cortex, superior and inferior parietal cortex, medial and lateral premotor regions, and subcortical structures. Reliability analyses revealed robust activation for active movements with both fMRI models and all the statistical methods used. Imposed passive movements also elicited mainly robust brain activation for individual and group activation maps, and reliability was improved by including additional force and range of motion using model 2. These findings demonstrate that the use of robotic devices, such as MaRIA, can be useful to reliably assess arm movement related brain activation in longitudinal studies and may contribute in studies evaluating therapies and brain plasticity following injury in the nervous system.
KeywordsfMRI Elbow flexion/extension Neurorehabilitation MRI-Compatible robotic devices Reliability Sensorimotor network
This work was supported by the National Center of Competence in Research (NCCR) on Neural Plasticity and Repair, launched by the Swiss National Science Foundation (SNF), and by the ETH Research Grant TH-34 06-3 MR-robotics. The authors thank Dr. Birgit Keisker, Prof. Roger Gassert, Prof. Martin Meyer and Dr. Christoph Hollnagel for their helpful advice and comments as well as Dr. Roger Lüchinger for his technical support in the MR center. Special thanks go to all the participants of the study.
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