Decoding of Motor Imagery Involving Whole-body Coordination. (1st October 2022)

• Record Type: Journal Article
• Authors: Yang, Huixiang; Ogawa, Kenji
• Is Part Of: Neuroscience. Volume 501(2022)
• Page Start: 131
• Page End: 142
• Publication Date: 2022-10-01
• Subjects: Motor imagery -- Functional magnetic resonance imaging -- Multi-voxel pattern analysis -- Motor Imagery Questionnaire-Revised; IPL inferior parietal lobules -- KI kinesthetic motor imagery -- M1 primary motor cortex -- MIQ motor imagery questionnaire -- MVPA multivariate pattern analysis -- PMd dorsal premotor cortex -- PMv ventral premotor cortex -- S1 primary somatosensory cortex -- SMA supplementary motor area -- SMC sensorimotor cortices -- SPL superior parietal lobules -- SVM support vector machine -- V1 primary visual cortex -- VI visual motor imagery; Neurochemistry -- Periodicals; Neurophysiology -- Periodicals; Neurology -- Periodicals; Neurochimie -- Périodiques; Neurophysiologie -- Périodiques; Neurochemistry; Neurophysiology; Electronic journals; Periodicals; Electronic journals; 612.8
• DOI: 10.1016/j.neuroscience.2022.07.029
• Languages: English
• ISSNs: 0306-4522

Abstract

Highlights: Motor imagery of general movements could be classified based on the peak location within the sensorimotor cortex. Motor imagery can be decoded from the primary visual cortex, even when negatively activated. Participants who perceived kinesthetic motor imagery as easier than visual motor imagery had a significantly higher classification accuracy within the left primary somatosensory cortex. Abstract: The present study investigated whether different types of motor imageries can be classified based on the location of the activation peaks or the multivariate pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) and compared the difference between visual motor imagery (VI) and kinesthetic motor imagery (KI). During fMRI scanning sessions, 25 participants imagined four movements included in the Motor Imagery Questionnaire-Revised (MIQ-R): knee lift, jump, arm movement, and waist bend. These four imagined movements were then classified based on the peak location or the patterns of fMRI signal values. We divided the participants into two groups based on whether they found it easier to generate VI (VI group, n = 10) or KI (KI group, n = 15). Our results show that the imagined movements can be classified using both the location of the activation peak and the spatial activation patterns within the sensorimotor cortex, and MVPA performs better than the activation peak classification. Furthermore, our result reveals that the KI group achieved a higher MVPA decoding accuracy within the left primary somatosensory cortex than the VI group, suggesting that the modality of motor imagery differently affects the classification performance in distinct brain regions.