Action Intention Modulates the Activity Pattern in Early Visual Areas

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dc.contributor.advisorCrawford, John Douglas
dc.creatorVelji- Ibrahim, Jena
dc.date.accessioned2018-08-27T16:39:02Z
dc.date.available2018-08-27T16:39:02Z
dc.date.copyright2018-04-12
dc.date.issued2018-08-27
dc.date.updated2018-08-27T16:39:02Z
dc.degree.disciplineKinesiology & Health Science
dc.degree.levelMaster's
dc.degree.nameMSc - Master of Science
dc.description.abstractThe activity pattern in the early visual cortex (EVC) can be used to predict upcoming actions as it is functionally connected to higher-order motor areas. However, the mechanism by which the EVC enhances action-relevant features is unclear. We explored this using fMRI. Participants performed Align or Open Hand movements to two oriented objects. We localized the calcarine sulcus, corresponding to the periphery, and the occipital pole, corresponding to the fovea. During planning, univariate analysis did not reveal significant results so we used multi-voxel pattern analysis (MVPA) to decode action type and object orientation. Though objects were located in the periphery, we found a significant decoding accuracy for orientation in an action-dependent manner in the occipital pole and action network areas. We established the functional connectivity between the EVC and somatomotor areas during planning using psychophysiological interaction (PPI) analysis. Taken together, our results show object orientation is modulated by action preparation.
dc.identifier.urihttp://hdl.handle.net/10315/35000
dc.language.isoen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectNeurosciences
dc.subject.keywordsAction planning
dc.subject.keywordsEarly visual cortex
dc.subject.keywordsFeedback connections
dc.subject.keywordsfMRI
dc.subject.keywordsMulti-voxel pattern analysis (MVPA)
dc.subject.keywordsPsychophysiological interaction (PPI) analysis
dc.titleAction Intention Modulates the Activity Pattern in Early Visual Areas
dc.typeElectronic Thesis or Dissertation

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