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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 the segregation of functional modules in the CoCoMac-based binary network (A and B) and the predicted weighted network (C and D). The module detection algorithm (Newman 2006) has identified two modules (M1, M2) in the CoCoMac-based network with modularity Q = 0.32, Z-score = 44.5 (A) but five modules (M1–M5) in the predicted weighted network with Q = 0.58, Z-score = 50.6 (C). For both networks, (B) and (D) correspondingly exhibit the functional domains (red: visual; green: somatosensory; blue: motor; pink: temporal; and black: frontal) for the regions in each module represented by a pie chart. The areas shown in different colors in each pie denote the numbers of regions belonging to the corresponding functional domains for each module.

Inline graphic — Comparison between the segregation of functional modules in the CoCoMac-based binary network (A and B) and the predicted weighted network (C and D). The module detection algorithm (Newman 2006) has identified two modules (M1, M2) in the CoCoMac-based network with modularity Q = 0.32, Z-score = 44.5 (A) but five modules (M1–M5) in the predicted weighted network with Q = 0.58, Z-score = 50.6 (C). For both networks, (B) and (D) correspondingly exhibit the functional domains (red: visual; green: somatosensory; blue: motor; pink: temporal; and black: frontal) for the regions in each module represented by a pie chart. The areas shown in different colors in each pie denote the numbers of regions belonging to the corresponding functional domains for each module.