Date of Award


Degree Type


Degree Name

Master of Science




Dr. Jody C. Culham

Second Advisor

Dr. Melvyn A. Goodale


The analysis of neural functional connectivity from resting-state MRI data using tech­ niques derived form graph theoretical foundations has recently attracted a significant amount of research interest. The bulk of such work done to date focuses on relatively small graphs, derived by partitioning the brain into regions of interest.

In this thesis we develop tools leveraging high-performance computing and meth­ ods for analyzing “whole brain” graphs in which we consider each grey-matter voxel in the brain to be an individual graph vertex. Based on 26 resting-state fMRI datasets we then empirically determine optimal sets of graph metrics for large graphs under varying assumptions followed by an investigation of the robustness of these metrics as assumptions are varied.

We then demonstrate the application of our methods to the question of hierarchical organization in prefrontal cortex.

We conclude by describing a technique for significantly reducing the size of our graphs, while losing as little useful information as possible.



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