Brain Imaging and Behavior

, Volume 13, Issue 5, pp 1185–1192 | Cite as

Mapping correlations of psychological and structural connectome properties of the dataset of the human connectome project with the maximum spanning tree method

  • Balázs SzalkaiEmail author
  • Bálint Varga
  • Vince GrolmuszEmail author
Original Research


Genome-wide association studies (GWAS) opened new horizons in genomics and medicine by discovering novel genetic factors in numerous health conditions. The analogous analysis of the correlations of large quantities of psychological and brain imaging measures may yield similarly striking results in the brain science. Smith et al. (Nat Neurosci. 18(11): 1565–1567, 2015) presented a study of the associations between MRI-detected resting-state functional connectomes and behavioral data, based on the Human Connectome Project’s (HCP) data release. Here we analyze the pairwise correlations between 717 psychological-, anatomical- and structural connectome–properties, based also on the Human Connectome Project’s 500-subject dataset. For the connectome properties, we have focused on the structural (or anatomical) connectomes, instead of the functional connectomes. For the structural connectome analysis we have computed and publicly deposited structural braingraphs at the site Numerous non-trivial and hard-to-compute graph-theoretical parameters (like minimum bisection width, minimum vertex cover, eigenvalue gap, maximum matching number, maximum fractional matching number) were computed for braingraphs of each subject, gained from the left- and right hemispheres and the whole brain. The correlations of these parameters, as well as other anatomical and behavioral measures were detected and analyzed. For discovering and visualizing the most interesting correlations in the 717 x 717 matrix, we have applied the maximum spanning tree method. Apart from numerous natural correlations, which describe parameters computable or approximable from one another, we have found several significant, novel correlations in the dataset, e.g., between the score of the NIH Toolbox 9-hole Pegboard Dexterity Test and the maximum weight graph theoretical matching in the left hemisphere. We also have found correlations described very recently and independently from the HCP-dataset: e.g., between gambling behavior and the number of the connections leaving the insula: these already known findings independently validate the power of our method.


Connectome Braingraph Maximum spanning tree 



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 was partially funded the NKFI-126472 grant of the National Research, Development and Innovation Office of Hungary.

Compliance with Ethical Standards

This article does not contain any studies with human participants or animals performed by any of the authors.

Conflict of interests Statement

The authors declare no conflicts of interests.

Supplementary material

11682_2018_9937_MOESM1_ESM.pdf (136 kb)
(PDF 135 KB)
11682_2018_9937_MOESM2_ESM.pdf (154 kb)
(PDF 153 KB)


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

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

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