Mapping correlations of psychological and structural connectome properties of the dataset of the human connectome project with the maximum spanning tree method
- 167 Downloads
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 http://braingraph.org. 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.
KeywordsConnectome 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.
- Ha, H.-Y., Chen, S.-C., Chen, M. (2015). Fc-mst: Feature correlation maximum spanning tree for multimedia concept classification. In 2015 IEEE International Conference on Semantic Computing (ICSC) (pp. 276–283): IEEE.Google Scholar
- Jr., P.T.C., & McCrae, R.R. (1992). Revised NEO personality inventory and NEO Five-Factor inventory professional manual. Psychological Assessment Resources, Inc.Google Scholar
- Lawler, E.L. (1976). Combinatorial optimization: networks and matroids. USA: Courier Dover Publications.Google Scholar
- McNab, J.A., Edlow, B.L., Witzel, T., Huang, S.Y., Bhat, H., Heberlein, K., Feiweier, T., Liu, K., Keil, B., Cohen-Adad, J., Tisdall, M.D., Folkerth, R.D., Kinney, H.C., Wald, L.L. (2013). The Human Connectome Project and beyond: initial applications of 300 mT/m gradients. NeuroImage, 80, 234–245.CrossRefGoogle Scholar
- Riccelli, R., Toschi, N., Nigro, S., Terracciano, A., Passamonti, L. (2017). Surface-based morphometry reveals the neuroanatomical basis of the five-factor model of personality. Social Cognitive and Affective Neuroscience, 12, 671–684.Google Scholar
- Smith, S.M., Nichols, T.E., Vidaurre, D., Winkler, A.M., Behrens, T.E.J., Glasser, M.F., Ugurbil, K., Barch, D.M., Van Essen, D.C., Miller, K.L. (2015). A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Nature neuroscience, 18, 1565–1567.CrossRefGoogle Scholar
- Szalkai, B., & Grolmusz, V. (2018). Human sexual dimorphism of the relative cerebral area volumes in the data of the human connectome project, European Journal of Anatomy, 22,(3).Google Scholar
- Szalkai, B., Varga, B., Grolmusz, V. (2016). The graph of our mind. arXiv:1603.00904.
- Szalkai, B., Varga, B., Grolmusz, V. (2017). Brain size bias-compensated graph-theoretical parameters are also better in women’s connectomes. Brain Imaging and Behavior. Also in arXiv:1512.01156.
- Wonnacott, T.H., & Wonnacott, R.J. (1972). Introductory statistics Vol. 19690. New York: Wiley.Google Scholar
- živković, J., Mitrović, M., Tadić, B. (2009). Correlation patterns in gene expressions along the cell cycle of yeast, volume Complex Networks of Studies in Computational Intelligence. Berlin: Springer.Google Scholar