Does motor imagery share neural networks with executed movement: a multivariate fMRI analysis - Sharma, Nikhil; Baron, Jean-Claude (Frontiers in Human Neuroscience, 2013)
Introduction Motor imagery (MI) is the mental rehearsal of a motor first person action-representation. There is interest in using MI to access the motor network after stroke. Conventional fMRI modelling has shown that MI and executed movement (EM) activate similar cortical areas but it remains unknown whether they share cortical networks. Proving this is central to using MI to access the motor network and as a form of motor training. Here we use multivariate analysis (tensor independent component analysis-TICA) to map the array of neural networks involved during MI and EM. Methods Fifteen right-handed healthy volunteers (mean-age 28.4 yrs) were recruited and screened for their ability to carry out MI (Chaotic MI Assessment). fMRI consisted of an auditory-paced (1Hz) right hand finger-thumb opposition sequence (2,3,4,5; 2…) with two separate runs acquired (MI & rest and EM & rest: block design). No distinction was made between MI and EM until the final stage of processing. This allowed TICA to identify independent-components (IC) that are common or distinct to both tasks with no prior assumptions. Results TICA defined 52 ICs. Non-significant ICs and those representing artefact were excluded. Components in which the subject scores were significantly different to zero (for either EM or MI) were included. 7 independent components remained. There were IC’s shared between EM and MI involving the contralateral BA4, PMd, parietal areas and SMA. IC’s exclusive to EM involved the contralateral BA4, S1 and ipsilateral cerebellum whereas the IC related exclusively to MI involved ipsilateral BA4 and PMd. Conclusion In addition to networks specific to each task indicating a degree of independence, we formally demonstrate here for the first time that MI and EM share cortical networks. This significantly strengthens the rationale for using MI to access the motor networks, but the results also highlight important differences.