Year: 2020
Type: dissertation
Abstract: Object grasping is fundamental to our interaction with the environment. To plan a grasp, the brain relies on lateral and medial pathways within a parieto-frontal network that convert grasp-relevant object features into motor plans to guide hand movements. While much is known about their cortical loci, little is known about when representations of visual and visuomotor features emerge and how they unfold over time. Characterizing the temporal dynamics of these representations is crucial in order to provide a complete functional description of the grasp network and to elucidate the computational steps during grasp planning. The objective of this dissertation was to examine the temporal structure of neural processes underlying grasp planning and execution. To this end three studies were conducted in which electroencephalography (EEG) was recorded while participants planned and executed precision grasps upon viewing 3D objects. Time-resolved multivariate pattern analyses were employed to infer visual and visuomotor representations from the data. I demonstrate that grasp-relevant visual and visuomotor computations follow similar time courses but display different properties and dynamics. Further, grasp planning follows a monotonic progression from visual to motor representations but a partially non-monotonic progression from higher to lower level processes. Finally, visuomotor representations of grip size (implied by object size) and orientation progress differently over time but are similarly integrated into the grasp program. The latter is driven by overlapping neural processes that control left-handed and bimanual grasps and occur at an intermediate level of grasp control (i.e., upstream from the control of effectors but downstream from effector-independent goal representations). These results provide novel insights into the visual-to-motor transformations and hierarchical structure underlying the control of human precision grasps. Further, they highlight the need for temporally highly resolved methods like EEG or MEG to attain a comprehensive understanding of human grasp control.
Source: TSpace (University of Toronto)
Author: Lin Guo
Language: English
Topic: Motor Control and Adaptation
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W3176658530