Optimally wired subnetwork determines neuroanatomy of Caenorhabditis elegans

Wiring cost minimization has successfully explained many structures of nervous systems. However, in the nematode Caenorhabditis elegans, for which anatomical data are most detailed, wiring economy is thought to play only a partial role and alone has failed to account for the grouping of neurons into...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2007-10, Vol.104 (43), p.17180-17185
Main Authors: Pérez-Escudero, Alfonso, de Polavieja, Gonzalo G
Format: Article
Language:English
Subjects:
Animals
Behavioral economics
Biological Sciences
Caenorhabditis elegans
Caenorhabditis elegans - anatomy & histology
Caenorhabditis elegans - cytology
Cells
Cluster Analysis
Connectivity
Economic forecasting
Ganglia
Minimization of cost
Motor neurons
Motor Neurons - physiology
Nematoda
Nematodes
Nerve Net - physiology
Neuroanatomy
Neurons
Neurons, Afferent - physiology
Neurosciences
Position errors
Sensors
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Summary:Wiring cost minimization has successfully explained many structures of nervous systems. However, in the nematode Caenorhabditis elegans, for which anatomical data are most detailed, wiring economy is thought to play only a partial role and alone has failed to account for the grouping of neurons into ganglia [Chen BL, Hall DH, Chklovskii DB (2006) Proc Natl Acad Sci USA 103:4723-4728; Kaiser M, Hilgetag CC (2006) PLoS Comput Biol 2:e95; Ahn Y-Y, Jeong H, Kim BJ (2006) Physica A 367:531-537]. Here, we test the hypothesis that optimally wired subnetworks can exist within nonoptimal networks, thus allowing wiring economy to give an improved prediction of spatial structure. We show in C. elegans that the small subnetwork of wires connecting sensory and motor neurons with sensors and muscles, comprising only 15% of connections, is close to optimal and alone predicts the main features of the spatial segregation of neurons into ganglia and encephalization. Moreover, a method to dissect networks into optimal and nonoptimal components is shown to find a large near-optimal subnetwork of 84% of neurons with a very low position error of 5.4%, and that explains clustering of neurons into ganglia and encephalization to fine detail. In general, we expect realistic networks not to be globally optimal in wire cost. We thus propose the strategy of using near-optimal subnetworks to understand neuroanatomical structure.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.0703183104