RT Journal Article
SR Electronic
T1 Structural covariance networks are coupled to expression of genes enriched in supragranular layers of the human cortex
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 163758
DO 10.1101/163758
A1 Rafael Romero-Garcia
A1 Kirstie J Whitaker
A1 František Váša
A1 Jakob Seidlitz
A1 Maxwell Shinn
A1 Peter Fonagy
A1 Raymond J Dolan
A1 Peter B Jones
A1 Ian M Goodyer
A1 the NSPN Consortium
A1 Edward T Bullmore
A1 Petra E Vértes
YR 2017
UL http://biorxiv.org/content/early/2017/07/21/163758.abstract
AB Complex network topology is characteristic of many biological systems, including anatomical and functional brain networks (connectomes). Here, we first constructed a structural covariance network (SCN) from MRI measures of cortical thickness on 296 healthy volunteers, aged 14-24 years. Next, we designed a new algorithm for matching sample locations from the Allen Brain Atlas to the nodes of the SCN. Subsequently we use this to define, transcriptomic brain networks (TBN) by estimating gene co-expression between pairs of cortical regions. Finally, we explore the hypothesis that TBN and the SCN are coupled.TBN and SCN were correlated across connection weights and showed qualitatively similar complex topological properties. There were differences between networks in degree and distance distributions. However, cortical areas connected to each other within modules of the SCN network had significantly higher levels of whole genome co-expression than expected by chance.Nodes connected in the SCN had significantly higher levels of expression and co-expression of a Human Supragranular Enriched (HSE) gene set that are known to be important for large-scale cortico-cortical connectivity. This coupling of brain transcriptome and connectome topologies was largely but not completely related to the common constraint of physical distance on both networks.