Kavli Affiliate: Brian Caffo Randal Burns Carey Priebe Joshua Vogelstein
| Authors: Jaewon Chung, Ross Lawrence, Alexander Loftus, Gregory Kiar, Eric Bridgeford, Consortium for Reliability and Reproducibility, Vikram Chandrashekhar, Disa Mhembere, Sephira Ryman, Xi-Nian Zuo, Daniel Margulies, R. Cameron Craddock, Carey E. Priebe, Rex Jung, Vince D. Calhoun, Brian Caffo, Randal Burns, Michael P. Milham and Joshua Vogelstein
| Summary:
Connectomics—the study of brain networks—provides a unique and valuable opportunity to study the brain. Research in human connectomics, leveraging functional and diffusion Magnetic Resonance Imaging (MRI), is a resource-intensive practice. Typical analysis routines require significant computational capabilities and subject matter expertise. Establishing a pipeline that is low-resource, easy to use, and off-the-shelf (can be applied across multifarious datasets without parameter tuning to reliably estimate plausible connectomes), would significantly lower the barrier to entry into connectomics, thereby democratizing the field by empowering a more diverse and inclusive community of connectomists. We therefore introduce ‘MRI to Graphs’ (m2g). To illustrate its properties, we used m2g to process MRI data from 35 different studies (≈ 6,000 scans) from 15 sites without any manual intervention or parameter tuning. Every single scan yielded an estimated connectome that adhered to established properties, such as stronger ipsilateral than contralateral connections in structural connectomes, and stronger homotopic than heterotopic correlations in functional connectomes. Moreover, the connectomes estimated by m2g are more similar within individuals than between them, suggesting that m2g preserves biological variability. m2g is portable, and can run on a single CPU with 16 GB of RAM in less than a couple hours, or be deployed on the cloud using its docker container. All code is available on https://github.com/neurodata/m2g and documentation is available on docs.neurodata.io/m2g