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. 2020 Apr 21;30(9):4771–4789. doi: 10.1093/cercor/bhaa060

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© The Author(s) 2020. Published by Oxford University Press.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

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Comparison between PC and regional wiring cost in the predicted weighted network. (A) Map of wiring cost based on the predicted weighted network . (B) Scatter plot of PCs versus regional wiring cost in the predicted network (dots). The two measures are strongly correlated (r = 0.52, P < ). The region prostriate has the maximal wiring cost, which is clearly larger than the others. Without considering this region, the wiring cost and PCs are still significantly correlated (r = 0.48, P < ). (C) Map of the regional wiring cost calculated from CoCoMac-based binary network . (D) Scatter plot of the PCs in the predicted weighted network versus regional wiring cost in the CoCoMac-based network, which are uncorrelated (r = 0.19, P = 0.07).

Inline graphic — Comparison between PC and regional wiring cost in the predicted weighted network. (A) Map of wiring cost based on the predicted weighted network . (B) Scatter plot of PCs versus regional wiring cost in the predicted network (dots). The two measures are strongly correlated (r = 0.52, P < ). The region prostriate has the maximal wiring cost, which is clearly larger than the others. Without considering this region, the wiring cost and PCs are still significantly correlated (r = 0.48, P < ). (C) Map of the regional wiring cost calculated from CoCoMac-based binary network . (D) Scatter plot of the PCs in the predicted weighted network versus regional wiring cost in the CoCoMac-based network, which are uncorrelated (r = 0.19, P = 0.07).