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Brain Imaging and Behavior

, Volume 12, Issue 3, pp 663–673 | Cite as

Brain size bias compensated graph-theoretical parameters are also better in women’s structural connectomes

  • Balázs Szalkai
  • Bálint Varga
  • Vince Grolmusz
Original Research

Introduction

While the neuronal-scale mapping of the connections of the whole human brain with more than 80 billion neurons is not possible today, a diffusion MRI-based workflow is available for mapping these connections with much less resolution (Hagmann et al. 2012; Craddock et al. 2013; McNab et al. 2013; Daducci et al. 2012). The result of that workflow is the connectome, or the braingraph of the subject: the several hundred nodes of this graph correspond to distinct areas of the gray matter of the brain, and two nodes are connected by an edge if the workflow finds fibers of axons connecting the areas, corresponding to these two nodes.

These connectomes describe tens of thousands of connections between distinct cerebral areas in a much more detailed manner than was possible before the era of diffusion MRI imaging. Additionally, the braingraphs make possible the quantitative analysis of the connections of the human brain.

One natural question is finding the connections that are...

Keywords

Connectome Sex differences 

Notes

Acknowledgments

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.

Author Contributions

V.G. initiated the study, analyzed data, wrote the paper; B.V. contributed analytic- and software tools; B.S. computed graph parameters and performed statistical analysis; all authors reviewed the manuscript.

Compliance with Ethical Standards

Conflict of interests

The authors declare no conflicts of interests.

Ethical approval

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

Funding

BS was supported through the new national excellence program of the Ministry of Human Capacities of Hungary.

Supplementary material

11682_2017_9720_MOESM1_ESM.pdf (193 kb)
(PDF 192 KB)

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

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

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