title: Motifs in brain networks.
abstract: Complex brains have evolved a highly efficient network architecture whose structural connectivity is capable of generating a large repertoire of functional states. We detect characteristic network building blocks (structural and functional motifs) in neuroanatomical data sets and identify a small set of structural motifs that occur in significantly increased numbers. Our analysis suggests the hypothesis that brain networks maximize both the number and the diversity of functional motifs, while the repertoire of structural motifs remains small. Using functional motif number as a cost function in an optimization algorithm, we obtain network topologies that resemble real brain networks across a broad spectrum of structural measures, including small-world attributes. These results are consistent with the hypothesis that highly evolved neural architectures are organized to maximize functional repertoires and to support highly efficient integration of information.
authors.affiliation: Department of Psychology and Program in Cognitive Science, Indiana University, Bloomington, Indiana, USA. osporns@indiana.edu
authors.forename: Olaf
authors.initials: O
authors.lastname: Sporns
authors.forename: Rolf
authors.initials: R
authors.lastname: Kötter
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D000818:Animals; D001921:Brain / Q000033:anatomy & histology* / Q000473:pathology*; D001931:Brain Mapping; D017173:Caenorhabditis elegans; D019295:Computational Biology; D003198:Computer Simulation; D005786:Gene Expression Regulation; D006801:Humans; D008251:Macaca; D008954:Models, Biological; D008959:Models, Neurological; D008962:Models, Theoretical; D009415:Nerve Net; D009445:Neuroanatomy / Q000379:methods*; D009446:Neurobiology; D009474:Neurons / Q000378:metabolism; D013045:Species Specificity