M2G: Reliable Human Connectomes At Scale

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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.

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Functional MRI

Dataset Covariates Processed fMRI Code
BNU1 [@csv] v0.0.1f
BNU2 [@csv] v0.0.1f
BNU3 [@csv] v0.0.1f
HNU1 [@csv] v0.0.1f
IBATRT [@csv] v0.0.1f
IPCAS1 [@csv] v0.0.1f
IPCAS2 [@csv] v0.0.1f
IPCAS5 [@csv] v0.0.1f
IPCAS6 [@csv] v0.0.1f
IPCAS8 [@csv] v0.0.1f
NYU1 [@csv] v0.0.1f
SWU1 [@csv] v0.0.1f
SWU2 [@csv] v0.0.1f
SWU3 [@csv] v0.0.1f
SWU4 [@csv] v0.0.1f
UWM [@csv] v0.0.1f
XHCUMS [@csv] v0.0.1f

Publications

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.

Related Papers

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.