 Features of spatial and functional segregation and integration of the primate connectome revealed by trade-off between wiring cost and efficiencyYuhan Chen, Shengjun Wang, Claus C Hilgetag and Changsong Zhou PLOS Computational Biology, 2017, vol. 13, issue 9, 1-37 Abstract: The primate connectome, possessing a characteristic global topology and specific regional connectivity profiles, is well organized to support both segregated and integrated brain function. However, the organization mechanisms shaping the characteristic connectivity and its relationship to functional requirements remain unclear. The primate brain connectome is shaped by metabolic economy as well as functional values. Here, we explored the influence of two competing factors and additional advanced functional requirements on the primate connectome employing an optimal trade-off model between neural wiring cost and the representative functional requirement of processing efficiency. Moreover, we compared this model with a generative model combining spatial distance and topological similarity, with the objective of statistically reproducing multiple topological features of the network. The primate connectome indeed displays a cost-efficiency trade-off and that up to 67% of the connections were recovered by optimal combination of the two basic factors of wiring economy and processing efficiency, clearly higher than the proportion of connections (56%) explained by the generative model. While not explicitly aimed for, the trade-off model captured several key topological features of the real connectome as the generative model, yet better explained the connectivity of most regions. The majority of the remaining 33% of connections unexplained by the best trade-off model were long-distance links, which are concentrated on few cortical areas, termed long-distance connectors (LDCs). The LDCs are mainly non-hubs, but form a densely connected group overlapping on spatially segregated functional modalities. LDCs are crucial for both functional segregation and integration across different scales. These organization features revealed by the optimization analysis provide evidence that the demands of advanced functional segregation and integration among spatially distributed regions may play a significant role in shaping the cortical connectome, in addition to the basic cost-efficiency trade-off. These findings also shed light on inherent vulnerabilities of brain networks in diseases.Author summary: The intricate primate structural connectome, as the network substrate for distributed and integrated brain function, is shaped by fundamental physical factors and functional requirements. We addressed a trade-off between two competing basic factors of wiring cost and processing efficiency as well as additional requirements of functional segregation and integration on regional structural connectivity profiles by applying cost–efficiency trade-off model to reconstruct the macaque cortical network. We also compared this model with a generative model combining spatial distance and topological similarity. The trade-off model balancing the two basic factors recovered the main topological features that were explicitly specified as the objective functions in the generative model. Moreover, 67% of all connections and most regional connectivity profiles were recovered by the cost-efficiency trade-off, substantially outperforming the generative model. Most long-distance links, unexplained by the cost-efficiency trade-off, are concentrated on a few special regions constituting long-distance connectors (LDCs). Departing from previous findings, LDCs are mostly non-hubs, but are crucial for supporting functional integration and, counter-intuitively also for proper segregation across different hierarchical scales of the cortical network. The perspective of a trade-off between basic factors on brain structural organization sheds light on the organization principles and potential vulnerabilities of the cortical connectome. Date: 2017 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1005776 DOI: 10.1371/journal.pcbi.1005776 Access Statistics for this article More articles in PLOS Computational Biology from Public Library of Science |
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