bioRxivNeurosciencePreprint
Encoding of bimanual movement directions across the human sensorimotor system
Ali GhavampouriD, Atsushi YokoiiD, Diogo F DuarteiD, Jean‐Jacques Orban de XivryiD, J. Andrew PruszynskiiD, Jörn DiedrichseniD
Abstract
Bimanual coordination requires the control of each hand to account for movements of the other hand. To enable such coordination, it has been hypothesized that the brain has a representation of bimanual movements that supersedes a linear combination of the unimanual movements. To test for such a representation, we mapped the encoding of movement direction during unimanual and bimanual wrist movements in the human brain using functional magnetic resonance imaging (fMRI). For unimanual movements, we found that both the contralateral and ipsilateral movement directions were represented across most cortical motor regions. The representations were similar for mirror-symmetric movements across the hands, revealing an intrinsic (body-centric) code in premotor and parietal regions. For bimanual movements, the neural representations of movement directions could be explained by three components. First, the contralateral movement direction was represented as in the unimanual condition, whereas the representation of the ipsilateral movement direction disappeared. Second, in higher-order regions (rostral premotor and posterior parietal cortex) we found relatively elevated activity when the two movements were directed in unrelated directions. This activity was likely caused by the extra effort to deal with incongruent spatial targets across the hands. Third, and independent of this congruency effect, we found a non-linear interaction between contra- and ipsilateral movement that encoded the specific bimanual movement combination across sensorimotor, premotor, and parietal regions. This representation was suited to facilitate bimanual coordination and learning.
- organism: human
- assay: imaging
- research focus: cell type annotation
- research focus: clustering
- research focus: spatial domain detection
- research focus: differential expression
- approach: statistical model
- Citations
- 0
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