Motifs in Brain Networks.

by: Olaf Sporns, Rolf Kötter

PLoS Biol, Vol. 2, No. 11. (November 2004)

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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.

@article{citeulike:62, 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.}, address = {Department of Psychology and Programs in Cognitive and Neural Science, Indiana University, Bloomington, Indiana, United States of America. osporns@indiana.edu.}, author = {Sporns, Olaf and K\"{o}tter, Rolf }, citeulike-article-id = {62}, doi = {http://dx.doi.org/10.1371/journal.pbio.0020369}, issn = {1544-9173}, journal = {PLoS Biol}, keywords = {background, connectivity, kristina}, month = {November}, number = {11}, posted-at = {2008-07-03 00:09:49}, priority = {2}, title = {Motifs in Brain Networks.}, url = {http://dx.doi.org/10.1371/journal.pbio.0020369}, volume = {2}, year = {2004} }

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TY - JOUR ID - citeulike:62 L3 - citeulike-article-id:62 TI - Motifs in Brain Networks. JF - PLoS Biol VL - 2 IS - 11 AD - Department of Psychology and Programs in Cognitive and Neural Science, Indiana University, Bloomington, Indiana, United States of America. osporns@indiana.edu. SN - 1544-9173 N2 - 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. KW - background KW - connectivity KW - kristina AU - Sporns, Olaf AU - Kötter, Rolf PY - 2004/11// UR - http://dx.doi.org/10.1371/journal.pbio.0020369 ER -

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