M2G: Reliable Human Connectomes At Scale
This page hosts connectomes produced with NeuroData's MRI Graphs pipeline (m2g). A 'projectome' is a large-scale mapping between regions of the brain, created from fMRI or DTI. Data are from a multitude of labs and experiments. The data are run through m2g, and after processing, we provide summary statistics, QA, and the projectomes themselves.
Links to the most recent collections of connectomes for each of the datasets below can be downloaded (as .zip folders) using the
Download Connectomes button. This will download a zipped folder containing all connectomes from the given dataset. There is also a link to explore all files related to the processing of each dataset using m2g. With this you can explore the directory structure and download individual files. The datasets are stored on the open-neurodata aws bucket, which supports more detailed downloading using aws-cli. The particular command for downloading all of the files generated by m2g using aws-cli can be copied to your Clipboard by clicking on the "Copy AWS Command to Clipboard" button.
G. Kiar, E. Bridgeford, W. G. Roncal, (CoRR), V. Chandrashekhar, D. Mhembere, S. Ryman, X. Zuo, D. S. Marguiles, R. C. Craddock, C. E. Priebe, R. Jung, V. Calhoun, B. Caffo, R. Burns, M. P. Milham, and J. Vogelstein. A High-Throughput Pipeline Identifies Robust Connectomes But Troublesome Variability. bioRxiv, 2018.
G. Kiar, K. J. Gorgolewski, D. Kleissas, W. G. Roncal, B. Litt, B. Wandell, R. A. Poldrack, M. Wiener, R. J. Vogelstein, R. Burns, and J. T. Vogelstein. Science in the cloud (SIC): A use case in MRI connectomics. GigaScience, (5)6:1–10, 2017.
Previous Pipeline Papers
W. G. Roncal, Z. H. Koterba, D. Mhembere, D. M. Kleissas, J. T. Vogelstein, R. Burns, A. R. Bowles, D. K. Donavos, S. Ryman, R. E. Jung, L. Wu, V. Calhoun, and R. J. Vogelstein. MIGRAINE: MRI graph reliability analysis and inference for connectomics. 2013 IEEE Global Conference on Signal and Information Processing, 2013.
W. R. Gray, J. A. Bogovic, J. T. Vogelstein, B. A. Landman, J. L. Prince, and R. J. Vogelstein. Magnetic Resonance Connectome Automated Pipeline: An Overview. IEEE Pulse, (2)3:42–48, 2012.
D. Mhembere, W. Gray Roncal, D. Sussman, C. E. Priebe, R. Jung, S. Ryman, R. J. Vogelstein, J. T. Vogelstein, and R. Burns. Computing scalable multivariate glocal invariants of large (brain-) graphs. 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings, 2013.
Data on this site are licensed under a ODC-By v1.0 license.