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Nonoptimal component placement of the human connectome supports variable brain dynamics

Hayward, Christopher James; Huo, Siyu; Chen, Xue; Kaiser, Marcus


Christopher James Hayward

Siyu Huo

Xue Chen

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Professor of Neuroinformatics


Neural systems are shaped by multiple constraints, balancing region communication with the cost of establishing and maintaining physical connections. It has been suggested that the lengths of neural projections be minimized, reducing their spatial and metabolic impact on the organism. However, long-range connections are prevalent in the connectomes across various species, and thus, rather than rewiring connections to reduce length, an alternative theory proposes that the brain minimizes total wiring length through a suitable positioning of regions, termed component placement optimization. Previous studies in nonhuman primates have refuted this idea by identifying a nonoptimal component placement, where a spatial rearrangement of brain regions in silico leads to a reduced total wiring length. Here, for the first time in humans, we test for component placement optimization. We show a nonoptimal component placement for all subjects in our sample from the Human Connectome Project (N = 280; aged 22–30 years; 138 females), suggesting the presence of constraints—such as the reduction of processing steps between regions—that compete with the elevated spatial and metabolic costs. Additionally, by simulating communication between brain regions, we argue that this suboptimal component placement supports dynamics that benefit cognition.


Hayward, C. J., Huo, S., Chen, X., & Kaiser, M. (2023). Nonoptimal component placement of the human connectome supports variable brain dynamics. Network Neuroscience, 7(1), 254-268.

Journal Article Type Article
Acceptance Date Sep 28, 2022
Online Publication Date Dec 2, 2022
Publication Date Jan 1, 2023
Deposit Date Nov 20, 2022
Publicly Available Date Dec 1, 2022
Journal Network Neuroscience
Electronic ISSN 2472-1751
Publisher Massachusetts Institute of Technology Press
Peer Reviewed Peer Reviewed
Volume 7
Issue 1
Pages 254-268
Keywords Applied Mathematics; Artificial Intelligence; Computer Science Applications; General Neuroscience
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