PT  - JOURNAL ARTICLE
AU  - Gregory Kiar
AU  - Eric W. Bridgeford
AU  - Vikram Chandrashekhar
AU  - Disa Mhembere
AU  - Randal Burns
AU  - William R. Gray Roncal
AU  - Joshua T. Vogelstein
TI  - A Comprehensive Cloud Framework for Accurate and Reliable Human Connectome Estimation and Meganalysis
AID  - 10.1101/188706
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 188706
4099  - http://biorxiv.org/content/early/2017/09/16/188706.short
4100  - http://biorxiv.org/content/early/2017/09/16/188706.full
AB  - The connectivity of the human brain is fundamental to understanding the principles of cognitive function, and the mechanisms by which it can go awry. To that extent, tools for estimating human brain networks are required for single subject, group level, and cross-study analyses. We have developed an open-source, cloud-enabled, turn-key pipeline that operates on (groups of) raw diffusion and structure magnetic resonance imaging data, estimating brain networks (connectomes) across 24 different spatial scales, with quality assurance visualizations at each stage of processing. Running a harmonized analysis on 10 different datasets comprising 2,295 subjects and 2,861 scans reveals that the connectomes across datasets are similar on coarse scales, but quantitatively different on fine scales. Our framework therefore illustrates that while general principles of human brain organization may be preserved across experiments, obtaining reliable p-values and clinical biomarkers from connectomics will require further harmonization efforts.