Cloud Computing for Voxel-Wise SEM Analysis of MRI Data

Abstract

As data collection costs fall and vast quantities of data are collected, data analysis time can become a bottleneck. For massively parallel analyses, cloud computing offers the short-term rental of ample processing power. Recent software innovations have reduced the effort needed to take advantage of cloud computing. To demonstrate, we replicate a voxel-wise examination of the genetic contributions to cortical development by age using evidence from 1748 MRI scans. Specifically, we employ off-the-shelf Kubernetes software that permits us to re-run our analyses using almost the same computer code as was published in the original article. Large, well funded institutions may continue to maintain their own computing clusters. However, the modest cost of renting and ease of utilizing cloud computing services makes unprecedented compute power available to all researchers, whether or not affiliated with a research institution. We expect this to spur innovation in the sophisticated modeling of large datasets.

Keywords:

• big data
• cloud computing
• developmental
• ecological momentary assessment
• FMRI
• genetics
• genomic SEM
• GWAS
• neuroimaging
• time series
• twins

Notes

¹ For advanced uses, multithreaded workers are possible up to the maximum number of CPUs per node.

² This is Bourne shell syntax. Csh and other shells may use a different syntax.

Additional information

Funding

This research was supported in part by National Institute of Health grants R01-DA018673 and R25-DA026119 (PI Neale). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Institutes of Health;NIH Office of the Director [R01-DA018673,R25-DA026119];