caa a22 4500 606201688 CHVBK 20210128100949.0 cr unu---uuuuu 210128e20151101xx s 000 0 eng 10.1007/s00429-014-0849-y doi (NATIONALLICENCE)springer-10.1007/s00429-014-0849-y A predictive model of the cat cortical connectome based on cytoarchitecture and distance [Elektronische Daten] [Sarah Beul, Simon Grant, Claus Hilgetag] Information processing in the brain is strongly constrained by anatomical connectivity. However, the principles governing the organization of corticocortical connections remain elusive. Here, we tested three models of relationships between the organization of cortical structure and features of connections linking 49 areas of the cat cerebral cortex. Factors taken into account were relative cytoarchitectonic differentiation (‘structural model'), relative spatial position (‘distance model'), or relative hierarchical position (‘hierarchical model') of the areas. Cytoarchitectonic differentiation and spatial distance (themselves uncorrelated) correlated strongly with the existence of inter-areal connections, whereas no correlation was found with relative hierarchical position. Moreover, a strong correlation was observed between patterns of laminar projection origin or termination and cytoarchitectonic differentiation. Additionally, cytoarchitectonic differentiation correlated with the absolute number of corticocortical connections formed by areas, and varied characteristically between different cortical subnetworks, including a ‘rich-club' module of hub areas. Thus, connections between areas of the cat cerebral cortex can, to a large part, be explained by the two independent factors of relative cytoarchitectonic differentiation and spatial distance of brain regions. As both the structural and distance model were originally formulated in the macaque monkey, their applicability in another mammalian species suggests a general principle of global cortical organization. The Author(s), 2014 Anatomical tract tracing nationallicence Cerebral cortex nationallicence Connectivity nationallicence Cytoarchitecture nationallicence Neuroinformatics nationallicence Beul Sarah Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany aut Grant Simon Division of Optometry and Visual Science, Henry Wellcome Laboratories for Visual Sciences, City University London, EC1V 0HB, London, UK aut Hilgetag Claus Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany aut Brain Structure and Function Springer Berlin Heidelberg 220/6(2015-11-01), 3167-3184 1863-2653 220:6<3167 2015 220 429 https://doi.org/10.1007/s00429-014-0849-y text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00429-014-0849-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Beul Sarah Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany aut NATIONALLICENCE 700 1- Grant Simon Division of Optometry and Visual Science, Henry Wellcome Laboratories for Visual Sciences, City University London, EC1V 0HB, London, UK aut NATIONALLICENCE 700 1- Hilgetag Claus Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany aut NATIONALLICENCE 773 0- Brain Structure and Function Springer Berlin Heidelberg 220/6(2015-11-01), 3167-3184 1863-2653 220:6<3167 2015 220 429