NeuroData's MRI to Graphs

ndmg provides robust and reliable estimates of MRI connectivity across a wide range of datasets at 1mm resolution, across 25 parcellations, ranging in size from 48 to 72,000 nodes, all in MNI152 standard space.

ndmg is a turn-key pipeline that provides session and group-level analysis, performing connectome estimation and summary statistic computation.

ndmg runs reliably and robustly datasets spanning multiple scanners, acquisition parameters, and population demographics.

Publications
  1. A. Nikolaidis, A. S. Heinsfeld, T. Xu, P. Bellec, J. T. Vogelstein, and M. Milham. Bagging Improves Reproducibility of Functional Parcellation of the Human Brain. bioRxiv, 2019.
  2. G. Kiar, E. Bridgeford, W. G. Roncal, C. f. R. (CoRR), Reproducibliity, 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.
  3. G. Kiar, E. Bridgeford, V. Chandrashekhar, D. Mhembere, R. Burns, W. R. G. Roncal, and J. T. Vogelstein. A comprehensive cloud framework for accurate and reliable human connectome estimation and meganalysis. bioRxiv, 2017.
  4. 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, 2017.