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.
Highlights
The large-scale network of cortical areas in the brain supports and integrates brain functions that are distributed across spatially segregated regions [1]
Optimizing wiring cost on its own leads to a physically cheaper network, which is, less efficient; with the wiring cost lp reduced to 77.1% and the efficiency lg increased to 105% of the real network
By contrast, optimizing the processing efficiency on its own increases efficiency, but at the expense of wiring cost
Summary
The large-scale network of cortical areas in the brain supports and integrates brain functions that are distributed across spatially segregated regions [1]. While the placement of regions in some sub-systems (e.g., macaque monkey frontal cortex and C. elegans ganglia) appeared to be optimal [15,16,17,18], the global network appears not to minimize wiring cost, and comprises a substantial admixture of long-distance connections [13]. A scheme of wiring cost minimization with fixed network topology does not permit to explicitly study the competition between physical cost and network functionality. It is an unresolved question of how to quantify the
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