High level motion: neural correlates and functional connectivity
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This thesis uses functional magnetic resonance imaging (fMRI) data to investigate: 1. The neural substrate of high level visual motion 2. The functional connectivity between a behavioral task and resting state. In chapter 1, we find the neural substrate of a set of psychophysical high level motion tasks. Specifically, we used tasks of visually guided navigation, such as heading from optic flow, landmarks, motion parallax, and collision detection. We also used tasks underlying the ability to perform object recognition from motion cues alone such as 3D Structure From Motion (SFM) and Biological Motion (BM). fMRI data was analyzed with Brain Voyager and activated anatomical areas were delineated using Matlab scripts developed in the laboratory. Several regions within the dorsal visual system elicited significant BOLD activity: the dorsal-occipital (BA19) and parietal lobes (BA 37, 40, 7). The ventral areas (BA 20, 21, 22, 38) showed significant BOLD activity only in BM and SFM and in heading tests using landmarks or motion parallax. We generated a schematic map with the overlapping areas among high level motion tasks, which can aid in diagnosis and rehabilitation of motion deficits in neurological patients. In chapter 2, we computed the functional brain connectivity between the brain areas in a resting state (subject performs no task), and during task (subject performs a visual motion task). In the resting state, we found connectivity using correlations between the posterior cingulate cortex (PCC), precuneus, medial prefrontal cortex (MPFC), lateral parietal cortex, and the hippocampal formation, which have been reported as the default mode network (DMN) since it represents correlated neural activity during a state of rest. We used bivariate correlations to compute functional connectivity using the CONN fMRI toolbox and in-house Matlab scripts. We computed a whole-brain analysis and compared network statistics in both, resting state and during task to investigate measures of integration such as path length and global efficiency, regional measures such as degree (number of connections) and betweenness centrality (number of shortest paths), and global measures such as small-worldness. The DMN and graph theoretical measures connectivity during task was stronger as compared with the resting state. We also computed these measures in task using a similar frequency spectrum as rest (0.009 Hz < f < 0.08 Hz), and in the full frequency spectrum. We find that on the whole, the connectivity measures in the DMN and the graph theoretical measure are stronger in the fullband signal processing analysis as compared to the bandpass version of the analysis.
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