Abstract
Based on the data of the NIH-funded Human Connectome Project, we have computed structural connectomes of 426 human subjects in five different resolutions of 83, 129, 234, 463 and 1015 nodes and several edge weights. The graphs are given in anatomically annotated GraphML format that facilitates better further processing and visualization. For 96 subjects, the anatomically classified sub-graphs can also be accessed, formed from the vertices corresponding to distinct lobes or even smaller regions of interests of the brain. For example, one can easily download and study the connectomes, restricted to the frontal lobes or just to the left precuneus of 96 subjects using the data. Partially directed connectomes of 423 subjects are also available for download. We also present a GitHub-deposited set of tools, called the Brain Graph Tools, for several processing tasks of the connectomes on the site http://braingraph.org.
This is a preview of subscription content, log in via an institution to check access.
References
Basser PJ, Pierpaoli C (1996) Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson 213(2):560–570. doi:10.1016/j.jmr.2011.09.022
Daducci A, Gerhard S, Griffa A, Lemkaddem A, Cammoun L, Gigandet X, Meuli R, Hagmann P, Thiran J-P (2012) The connectome mapper: an open-source processing pipeline to map connectomes with MRI. PLoS ONE 7(12):e48121. doi:10.1371/journal.pone.0048121
Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ (2006) An automated labeling system for subdividing the human cerebral cortex on mri scans into gyral based regions of interest. Neuroimage 31(3):968–980. doi:10.1016/j.neuroimage.2006.01.021
Fischl B (2012) Freesurfer. Neuroimage 62(2):774–781
Kerepesi C, Szalkai B, Varga B, Grolmusz V (2015) Comparative connectomics: mapping the inter-individual variability of connections within the regions of the human brain. arXiv preprint arXiv:1507.00327
Kerepesi C, Szalkai B, Varga B, Grolmusz V (2016a) How to direct the edges of the connectomes: dynamics of the consensus connectomes and the development of the connections in the human brain. PLoS ONE 11(6):e0158680. doi:10.1371/journal.pone.0158680
Kerepesi C, Varga B, Szalkai B, Grolmusz V (2016b) The dorsal striatum and the dynamics of the consensus connectomes in the frontal lobe of the human brain. arXiv:1605.01441
McNab JA, Edlow BL, Witzel T, Huang SY, Bhat H, Heberlein K, Feiweier T, Liu K, Keil B, Cohen-Adad J, Tisdall MD, Folkerth RD, Kinney HC, Wald LL (2013) The human connectome project and beyond: initial applications of 300 mT/m gradients. Neuroimage 80:234–245. doi:10.1016/j.neuroimage.2013.05.074
Szalkai B, Kerepesi C, Varga B, Grolmusz V (2015a) The Budapest reference connectome server v2. 0. Neurosci Lett 595:60–62
Szalkai B, Varga B, Grolmusz V (2015b) Graph theoretical analysis reveals: women’s brains are better connected than men’s. PLoS ONE 10(7):e0130045. doi:10.1371/journal.pone.0130045
Szalkai B, Kerepesi C, Varga B, Grolmusz V (2016a) Parameterizable consensus connectomes from the human connectome project: the Budapest reference connectome server v3.0. Cogn Neurodyn. doi:10.1007/s11571-016-9407-z
Szalkai B, Varga B, Grolmusz V (2016b) The graph of our mind. arXiv preprint arXiv:1603.00904
Szalkai B, Varga B, Grolmusz V (2016c) Mapping correlations of psychological and connectomical properties of the dataset of the human connectome project with the maximum spanning tree method. arXiv:1602.04776
Szalkai B, Kerepesi C, Varga B, Grolmusz V (2016d) High-resolution directed human connectomes and the consensus connectome dynamics. arXiv:1609:09036
Szalkai B, Varga B, Grolmusz V (2017) Brain size bias-compensated graph-theoretical parameters are also better in women’s connectomes. Brain Imaging Behav. doi:10.1007/s11682-017-9720-0
Tournier J, Calamante F, Connelly A et al (2012) Mrtrix: diffusion tractography in crossing fiber regions. Int J Imaging Syst Technol 22(1):53–66
Acknowledgements
Data were provided in part by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. CK was supported by Momentum Grant of the Hungarian Academy of Sciences (LP2012-19/2012).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflict of interest.
Additional information
Csaba Kerepesi, Balázs Szalkai, and Bálint Varga: joint first authors.
Rights and permissions
About this article
Cite this article
Kerepesi, C., Szalkai, B., Varga, B. et al. The braingraph.org database of high resolution structural connectomes and the brain graph tools. Cogn Neurodyn 11, 483–486 (2017). https://doi.org/10.1007/s11571-017-9445-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11571-017-9445-1