RT Journal Article
SR Electronic
T1 A low-resource reliable pipeline to democratize multi-modal connectome estimation and analysis
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.11.01.466686
DO 10.1101/2021.11.01.466686
A1 Ross Lawrence
A1 Alex Loftus
A1 Gregory Kiar
A1 Eric W. Bridgeford
A1 William Gray Roncal
A1 Vikram Chandrashekhar
A1 Disa Mhembere
A1 Sephira Ryman
A1 Xi-Nian Zuo
A1 Daniel S. Margulies
A1 R. Cameron Craddock
A1 Carey E. Priebe
A1 Rex Jung
A1 Vince D. Calhoun
A1 Brian Caffo
A1 Randal Burns
A1 Michael P. Milham
A1 Joshua T. Vogelstein
A1 Consortium for Reliability and Reproducibility (CoRR)
YR 2021
UL http://biorxiv.org/content/early/2021/11/03/2021.11.01.466686.abstract
AB Connectomics—the study of brain networks—provides a unique and valuable opportunity to study the brain. However, research in human connectomics, accomplished via Magnetic Resonance Imaging (MRI), is a resource-intensive practice: typical analysis routines require impactful decision making and significant computational capabilities. Mitigating these issues requires the development of low-resource, easy to use, and flexible pipelines which can be applied across data with variable collection parameters. In response to these challenges, we have developed the MRI to Graphs (m2g) pipeline. m2g leverages functional and diffusion datasets to estimate connectomes reliably. To illustrate, m2g was used 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 followed established properties, such as stronger ipsilateral than contralateral connections in structural connectomes, and stronger homotopic than heterotopic correlations in functional connectomes. Moreover, the connectomes generated 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://neurodata.io/mri/.Competing Interest StatementThe authors have declared no competing interest.