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Cognitive Neurodynamics

, Volume 13, Issue 5, pp 453–460 | Cite as

The frequent subgraphs of the connectome of the human brain

  • Máté Fellner
  • Bálint Varga
  • Vince GrolmuszEmail author
Research Article
  • 101 Downloads

Abstract

In mapping the human structural connectome, we are in a very fortunate situation: one can compute and compare graphs, describing the cerebral connections between the very same, anatomically identified small regions of the gray matter among hundreds of human subjects. The comparison of these graphs has led to numerous recent results, as the (1) discovery that women’s connectomes have deeper and richer connectivity-related graph parameters like those of men, or (2) the description of more and less conservatively connected lobes and cerebral regions, and (3) the discovery of the phenomenon of the consensus connectome dynamics. Today one of the greatest challenges of brain science is the description and modeling of the circuitry of the human brain. For this goal, we need to identify sub-circuits that are present in almost all human subjects and those, which are much less frequent: the former sub-circuits most probably have functions with general importance, the latter sub-circuits are probably related to the individual variability of the brain structure and function. The present contribution describes the frequent connected subgraphs of at most six edges in the human brain. We analyze these frequent graphs and also examine sex differences in these graphs: we demonstrate numerous connected subgraphs that are more frequent in female or male connectomes. While there is no difference in the number of k edge connected subgraphs in males or females for \({\text{k}} = 1\), and for \({\text{k}} = 2\) males have slightly more frequent subgraphs, for \({\text{k}} = 6\) there is a very strong advantage in the case of female braingraphs. Our data source is the public release of the Human Connectome Project, and we are applying the data of 426 human subjects in this study.

Keywords

Connectome Braingraph Frequent braingraphs Sex differences 

Notes

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. VG and BV were partially supported by the VEKOP-2.3.2-16-2017-00014 program, supported by the European Union and the State of Hungary, co-financed by the European Regional Development Fund, and the NKFI-126472 and the NKFI-127909 grants of the National Research, Development and Innovation Office of Hungary. BV and MF was supported in part by the EFOP-3.6.3-VEKOP-16-2017-00002 grant, supported by the European Union, co-financed by the European Social Fund.

Author contributions

BV computed braingraphs from the HCP data and created Figs. 1, 2 and 3; MF analyzed braingraphs, invented the frequent braingraph finding extension of the apriori algorithm, contributed Figure 4 and the Supporting material, wrote the “Methods” section, and performed statistical analysis. VG has initiated the study, analyzed data and wrote the paper.

Data availability statement

The data source of this study is Human Connectome Project’s website at http://www.humanconnectome.org/documentation/S500 (McNab et al. 2013). The connectomes, computed by us can be freely downloaded from http://braingraph.org/download-pit-group-connectomes/ (Kerepesi et al. 2017). The large supporting tables can be downloaded from http://uratim.com/freq/Frequent-Supporting.zip.

Compliance with ethical standards

Conflict of interest

The authors declare no conflicts of interest.

Supplementary material

11571_2019_9535_MOESM1_ESM.pdf (5.5 mb)
Supplementary material 1 (pdf 5645 KB)

References

  1. Agosta F, Galantucci S, Valsasina P, Canu E, Meani A, Marcone A, Magnani G, Falini A, Comi G, Filippi M (2014) Disrupted brain connectome in semantic variant of primary progressive aphasia. Neurobiol Aging.  https://doi.org/10.1016/j.neurobiolaging.2014.05.017 CrossRefPubMedGoogle Scholar
  2. Agrawal R, Srikant R et al (1994) Fast algorithms for mining association rules. In: Proceedings of 20th international conference very large data bases, VLDB, vol 1215, pp 487–499Google Scholar
  3. Ball G, Aljabar P, Zebari S, Tusor N, Arichi T, Merchant N, Robinson EC, Ogundipe E, Rueckert D (2014) Rich-club organization of the newborn human brain. Proc Natl Acad Sci USA 111(20):7456–7461.  https://doi.org/10.1073/pnas.1324118111 CrossRefPubMedGoogle Scholar
  4. Déli E, Tozzi A, Peters JF (2017) Relationships between short and fast brain timescales. Cogn Neurodyn 11(6):539–552CrossRefPubMedPubMedCentralGoogle Scholar
  5. Fields C, Glazebrook JF (2017) Disrupted development and imbalanced function in the global neuronal workspace: a positive-feedback mechanism for the emergence of asd in early infancy. Cogn Neurodyn 11(1):1–21CrossRefPubMedGoogle Scholar
  6. Fischl B (2012) Freesurfer. Neuroimage 62(2):774–781CrossRefPubMedPubMedCentralGoogle Scholar
  7. Hagmann P, Grant PE, Fair DA (2012) MR connectomics: a conceptual framework for studying the developing brain. Front Syst Neurosci 6:43.  https://doi.org/10.3389/fnsys.2012.00043 CrossRefPubMedPubMedCentralGoogle Scholar
  8. Han J, Kamber M (2000) Data mining: concepts and techniques. Morgan Kaufmann Publishers, San FranciscoGoogle Scholar
  9. Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6(2):65–70Google Scholar
  10. Kerepesi C, Szalkai B, Varga B, Grolmusz V (2016) 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.  https://doi.org/10.1371/journal.pone.0158680 CrossRefPubMedPubMedCentralGoogle Scholar
  11. Kerepesi C, Szalkai B, Varga B, Grolmusz V (2017) The braingraph.org database of high resolution structural connectomes and the brain graph tools. Cogn Neurodyn 11(5):483–486CrossRefPubMedPubMedCentralGoogle Scholar
  12. Kerepesi C, Varga B, Szalkai B, Grolmusz V (2018a) The dorsal striatum and the dynamics of the consensus connectomes in the frontal lobe of the human brain. Neurosci Lett 673:51–55.  https://doi.org/10.1016/j.neulet.2018.02.052 CrossRefPubMedGoogle Scholar
  13. Kerepesi C, Szalkai B, Varga B, Grolmusz V (2018b) Comparative connectomics: mapping the inter-individual variability of connections within the regions of the human brain. Neurosci Lett 662(1):17–21.  https://doi.org/10.1016/j.neulet.2017.10.003 CrossRefPubMedGoogle Scholar
  14. 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.  https://doi.org/10.1016/j.neuroimage.2013.05.074 CrossRefPubMedGoogle Scholar
  15. Meier J, Märtens M, Hillebrand A, Tewarie P, Van Mieghem P (2016) Motif-based analysis of effective connectivity in brain networks. In: International workshop on complex networks and their applications. Springer, pp 685–696Google Scholar
  16. Peters JF, Tozzi A, Ramanna S, Inan E (2017) The human brain from above: an increase in complexity from environmental stimuli to abstractions. Cogn Neurodyn 11(4):391–394CrossRefPubMedPubMedCentralGoogle Scholar
  17. Rao AR (2018) An oscillatory neural network model that demonstrates the benefits of multisensory learning. Cogn Neurodyn 12(5):481–499CrossRefPubMedPubMedCentralGoogle Scholar
  18. Sporns O, Kötter R (2004) Motifs in brain networks. PLoS Biol 2(11):e369CrossRefPubMedPubMedCentralGoogle Scholar
  19. Szalkai B, Kerepesi C, Varga B, Grolmusz V (2015a) The Budapest reference connectome server v2.0. Neurosci Lett 595:60–62CrossRefPubMedGoogle Scholar
  20. 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.  https://doi.org/10.1371/journal.pone.0130045 CrossRefPubMedPubMedCentralGoogle Scholar
  21. Szalkai B, Kerepesi C, Varga B, Grolmusz V (2017a) Parameterizable consensus connectomes from the Human Connectome Project: the Budapest reference connectome server v3.0. Cogn Neurodyn 11(1):113–116.  https://doi.org/10.1007/s11571-016-9407-z CrossRefPubMedGoogle Scholar
  22. Szalkai B, Varga B, Grolmusz V (2017b) Brain size bias-compensated graph-theoretical parameters are also better in women’s connectomes. Brain Imaging Behav.  https://doi.org/10.1007/s11682-017-9720-0 CrossRefGoogle Scholar
  23. Szalkai B, Varga B, Vi G (2017c) The robustness and the doubly-preferential attachment simulation of the consensus connectome dynamics of the human brain. Sci Rep 7:16118.  https://doi.org/10.1038/s41598-017-16326-0 CrossRefPubMedPubMedCentralGoogle Scholar
  24. Szalkai B, Varga B, Grolmusz V (2018) Comparing advanced graph-theoretical parameters of the connectomes of the lobes of the human brain. Cogn Neurodyn 12(6):549–559CrossRefPubMedGoogle Scholar
  25. Szalkai B, Varga B, Grolmusz V (2016a) The graph of our mind. arXiv preprint arXiv:1603.00904
  26. Szalkai B, Kerepesi C, Varga B, Grolmusz V (2016b) High-resolution directed human connectomes and the consensus connectome dynamics. arXiv:1609.09036, September
  27. Tozzi A, Peters JF (2017) From abstract topology to real thermodynamic brain activity. Cogn Neurodyn 11:283–292.  https://doi.org/10.1007/s11571-017-9431-7 ISSN 1871-4080CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.PIT Bioinformatics GroupEötvös UniversityBudapestHungary
  2. 2.Uratim Ltd.BudapestHungary

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