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Title: Studying neural selectivity for motion using high-field fMRI
Author: Beckett, Alexander
Awarding Body: University of Nottingham
Current Institution: University of Nottingham
Date of Award: 2013
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Functional magnetic resonance imaging (fMRI) offers a number of opportunities to non-invasively study the properties of the human visual system. Advances in scanner technology, particularly the development of high-field scanners, allow improvements in fMRI such as higher resolution and higher signal to noise ratio (SNR). We aimed to examine what these advances in scanner technology, combined with novel analysis techniques, can tell us about the processing of motion stimuli in the human visual cortex. In Chapter 3 we investigated whether high-resolution fMRI allows us to directly study motion-selective responses in MT+. We used event-related and adaptation methods to examine selectivity for coherent motion and selectivity for direction of motion, and examined the potential limitations of these techniques. One particular analysis technique that has been developed in recent years uses multivariate methods to classify patterns of activity from visual cortex. In Chapter 4 we investigated these methods for classifying direction of motion, particularly whether successful classification responses are based on fine-scale information such as the arrangement of direction-selective columns, or a global signal at a coarser scale. In Chapter 5 we investigated multivariate classification of non-translational motion (e.g. rotation) to see how this compared to the classification of translational motion. The processing of such stimuli have been suggested to be free from the large-scale signals that may be involved in other stimuli, and therefore a more powerful tool for studying the neural architecture of visual cortex. Chapter 6 investigated the processing of plaid motion stimuli, specifically ’pattern’ motion selectivity in MT+ as opposed to ’component’ motion selectivity. These experiments highlight the usefulness of multivariate methods even if the scale of the signal is unknown.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: QP351 Neurophysiology and neuropsychology