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Abstract #2687

Open Source Random Matrix Theory Software for the Analysis of Functional Magnetic-Resonance Imaging Examinations

Derek Berger1, Gurpreet S Matharoo1,2,3, and Jacob Levman1
1Computer Science, St. Francis Xavier University, Antigonish, NS, Canada, 2Physics, St. Francis Xavier University, Clydesdale, NS, Canada, 3ACENET, St. Francis Xavier University, Antigonish, NS, Canada

We assess the potential application of RMT-based features for the analysis of functional MRI (fMRI) across diverse datasets. As novel contributions, we (1) assess the potential for RMT-inspired, whole-brain features extracted from voxel-wise functional connectivity, (2) assess these features’ predictive—rather than explanatory—value, (3) investigate the effect of varying RMT analysis methods on the robustness of study findings, and (4) make general-purpose code publicly available for users to extract these features from a wide variety of data. We find preliminary evidence suggesting that RMT-inspired features may have unique potential in analyses of fMRI functional connectivity.

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