ArticleNetwork neuroscience (Cambridge, Mass.)2023
Trade-offs among cost, integration, and segregation in the human connectome.
Article in Network neuroscience (Cambridge, Mass.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Fifteen years on: a review of the Cam-CAN study of the cognitive neuroscience of ageing.GeroScience · 2026Review
- Constructing the human brain metabolic connectome with MR spectroscopic imaging reveals cerebral biochemical organization.Nature communications · 2025Article
- Effects of sleep on the glymphatic functioning and multimodal human brain network affecting memory in older adults.Molecular psychiatry · 2025Article
- Functional differentiation of the default and frontoparietal control networks predicts individual differences in creative achievement: evidence from macroscale cortical gradients.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Reconfiguration of metabolic connectivity in ageing.Communications biology · 2024Article
- Trade-offs among brain structural network characteristics across the cognitive decline process in cerebral small vessel disease.Frontiers in aging neuroscience · 2024Article
- Trade-offs among cost, integration, and segregation in the human connectome.Network neuroscience (Cambridge, Mass.) · 2023Article
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Authors and funding
7 authors.
Funding
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Abstract
The human brain structural network is thought to be shaped by the optimal trade-off between cost and efficiency. However, most studies on this problem have focused on only the trade-off between cost and global efficiency (i.e., integration) and have overlooked the efficiency of segregated processing (i.e., segregation), which is essential for specialized information processing. Direct evidence on how trade-offs among cost, integration, and segregation shape the human brain network remains lacking. Here, adopting local efficiency and modularity as segregation factors, we used a multiobjective evolutionary algorithm to investigate this problem. We defined three trade-off models, which represented trade-offs between cost and integration (Dual-factor model), and trade-offs among cost, integration, and segregation (local efficiency or modularity; Tri-factor model), respectively. Among these, synthetic networks with optimal trade-off among cost, integration, and modularity (Tri-factor model [
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