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Title: A Nonparametric Statistical Approach to Inter-Subject Functional Connectivity Analyses of the Brain
Authors: Hssaine, Chamsi
Advisors: Liu, Han
Department: Operations Research and Financial Engineering
Class Year: 2016
Abstract: Until recently, all of the analyses that have been done on the brain have relied exclusively on functional connectivity, or the use of correlations, to describe the relationship between different regions of an individual's brain during exposure to an external stimulus. Simony et al. propose an alternative method, inter-subject functional connectivity, which successfully reveals patterns induced by the stimulus and filters out intrinsic neural effects from brain activity [12]. We build upon the work done in [12] and suggest the use of distance correlation, a more generalized notion of dependence, to characterize the relationship between different regions of the brain. We find that distance correlation produces close to identical results to Pearson correlation, confirming that the relationship between voxels during stimulus exposure is approximately linear. Additionally, we analyze the dynamics of inter-subject functional connectivity throughout the duration of the stimulus exposure. We find that different regions of the brain are activated at different times during stimulus exposure, and that the clustering of the brain largely remains the same as time progresses.
Extent: 93 pages
Type of Material: Princeton University Senior Theses
Language: en_US
Appears in Collections:Operations Research and Financial Engineering, 2000-2017

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