Structural covariance networks are coupled to expression of genes enriched in supragranular layers of the human cortex
作者： Rafael Romero-Garcia , Kirstie J. Whitaker , František Váša , Jakob Seidlitz , Maxwell Shinn
DOI: 10.1016/J.NEUROIMAGE.2017.12.060
关键词:
摘要: Abstract Complex network topology is characteristic of many biological systems, including anatomical and functional brain networks (connectomes). Here, we first constructed a structural covariance from MRI measures cortical thickness on 296 healthy volunteers, aged 14–24 years. Next, designed new algorithm for matching sample locations the Allen Brain Atlas to nodes SCN. Subsequently used this define, transcriptomic by estimating gene co-expression between pairs regions. Finally, explored hypothesis that transcriptional connectomes are coupled. A (TBN) (SCN) were correlated across connection weights showed qualitatively similar complex topological properties: assortativity, small-worldness, modularity, rich-club. In both networks, weight an edge was inversely related (Euclidean) distance There differences in degree distributions: had less fat-tailed distribution positively skewed than However, areas connected each other within modules SCN significantly higher levels whole genome expected chance. Nodes especially high expression human supragranular enriched (HSE) set has been specifically located layers cerebral cortex known be important large-scale, long-distance cortico-cortical connectivity. This coupling transcriptome connectome topologies largely but not entirely accounted common constraint physical networks.
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