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arXiv · 1312.4875

MIGRAINE: MRI Graph Reliability Analysis and Inference for Connectomics

William Gray Roncal·Zachary H. Koterba·Disa Mhembere·Dean M. Kleissas·Joshua T. Vogelstein·Randal Burns·Anita R. Bowles·Dimitrios K. Donavos·Sephira Ryman·Rex E. Jung·Lei Wu·Vince Calhoun·R. Jacob Vogelstein
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Abstract

Currently, connectomes (e.g., functional or structural brain graphs) can be estimated in humans at $\approx 1~mm^3$ scale using a combination of diffusion weighted magnetic resonance imaging, functional magnetic resonance imaging and structural magnetic resonance imaging scans. This manuscript summarizes a novel, scalable implementation of open-source algorithms to rapidly estimate magnetic resonance connectomes, using both anatomical regions of interest (ROIs) and voxel-size vertices. To assess the reliability of our pipeline, we develop a novel nonparametric non-Euclidean reliability metric. Here we provide an overview of the methods used, demonstrate our implementation, and discuss available user extensions. We conclude with results showing the efficacy and reliability of the pipeline over previous state-of-the-art.

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William Gray Roncal, Zachary H. Koterba, Disa Mhembere, Dean M. Kleissas, Joshua T. Vogelstein, Randal Burns, Anita R. Bowles, Dimitrios K. Donavos, Sephira Ryman, Rex E. Jung, Lei Wu, Vince Calhoun, R. Jacob Vogelstein. 2013-12-17. MIGRAINE: MRI Graph Reliability Analysis and Inference for Connectomics. https://doi.org/10.1109/globalsip.2013.6736878

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