Models, analyses, tools, and experiments to characterize human motor control and coordination
Abstract/Contents
- Abstract
- In this thesis, we explore motor control and coordination in the brain using a novel set of models, analysis methods, experimental tools, and neuroscience experiments. We begin by presenting a family of anatomically accurate human upper-body musculoskeletal models, which provide a basis to identify relationships between muscle forces and tasks performed with the hands. Next, we develop a control theory formulation that describes muscle control strategies given motor task specifications at the hands. Our theory, when applied to musculoskeletal models can help identify whether specific motor tasks are rich enough to span the space of neuro-musculo-skeletal control and coordination. In other words, whether experimentally observing the brain when humans perform such tasks has the potential to provide information about how the motor system works. To observe neural activation in the brain as live human subjects performed rich and informative motor tasks, we developed a family of functional magnetic resonance imaging compatible 'Haptic fMRI Interfaces' (HFIs). We demonstrate that HFIs work reliably and enable motor neuroimaging experiments with sufficient resolution to study visually guided feedback control as well as force control tasks. Finally, we discuss a series of haptic neuroimaging experiments we conducted to identify regions in the brain that are likely to represent motor control and coordination functions. To be specific, we identify brain regions whose activation is associated with visually guided hand-trajectory control yet is invariant to limb configuration (and thus muscle coordination), and independently identify brain regions whose activation is associated with muscle coordination yet is invariant to visually guided hand-force control. Our results constitute a foundation for model- and theory-driven human motor neuroscience, and set the stage for systematically investigating the brain's motor regions.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2017 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Menon, Samir |
|---|---|
| Associated with | Stanford University, Computer Science Department. |
| Primary advisor | Boahen, Kwabena (Kwabena Adu) |
| Primary advisor | Khatib, Oussama |
| Thesis advisor | Boahen, Kwabena (Kwabena Adu) |
| Thesis advisor | Khatib, Oussama |
| Thesis advisor | Li, Fei Fei, 1976- |
| Thesis advisor | Wandell, Brian A |
| Advisor | Li, Fei Fei, 1976- |
| Advisor | Wandell, Brian A |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Samir Menon. |
|---|---|
| Note | Submitted to the Department of Computer Science. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2017. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2017 by Samir Menon
- License
- This work is licensed under a Creative Commons Attribution Share Alike 3.0 Unported license (CC BY-SA).
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