MIGRAINE: MRI Graph Reliability Analysis and Inference for Connectomics
Abstract
Currently, connectomes (e.g., functional or structural brain graphs) can be estimated in humans at 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.
Cite
@article{arxiv.1312.4875,
title = {MIGRAINE: MRI Graph Reliability Analysis and Inference for Connectomics},
author = {William Gray Roncal and Zachary H. Koterba and Disa Mhembere and Dean M. Kleissas and Joshua T. Vogelstein and Randal Burns and Anita R. Bowles and Dimitrios K. Donavos and Sephira Ryman and Rex E. Jung and Lei Wu and Vince Calhoun and R. Jacob Vogelstein},
journal= {arXiv preprint arXiv:1312.4875},
year = {2016}
}
Comments
Published as part of 2013 IEEE GlobalSIP conference