title: Comparing connectivity pattern and small-world organization between structural correlation and resting-state networks in healthy adults.
abstract: In recent years, coordinated variations in brain morphology (e.g. volume, thickness, surface area) have been employed as a measure of structural association between brain regions to infer large-scale structural correlation networks (SCNs). However, it remains unclear how morphometric correlations relate to functional connectivity between brain regions. Resting-state networks (RSNs), derived from coordinated variations in neural activity at rest, have been shown to reflect connectivity between functionally related regions as well as, to some extent, anatomical connectivity between brain regions. Therefore, it is intriguing to investigate similarities between SCN and RSN to help identify how morphometric correlations relate to connections defined by resting-state connectivity. We investigated the similarities in connectivity patterns and small-world organization between SCN, derived from correlations of regional gray matter volume across individuals, and RSN in 36 healthy individuals. The results showed a significant similarity between SCN and RSN (60% for positive connections and 40% for negative connections) that might be explained by shared experience-related functional connectivity underlying both SCN and RSN. Conversely, the small-world parameters of the networks were significantly different, suggesting that SCN topological parameters cannot be regarded as a substitute for topological organization in resting-state networks. While our data suggest that using structural correlation networks can be useful in understanding alterations in structural associations in various brain disorders, it should be noted that a portion of the observed alterations might be explained by factors other than those reflecting resting-state connectivity.
authors.affiliation: Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305-5795, USA. hosseiny@stanford.edu
authors.forename: S M Hadi
authors.initials: SM
authors.lastname: Hosseini
authors.forename: Shelli R
authors.initials: SR
authors.lastname: Kesler
mesh_terms: D000328:Adult; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D009415:Nerve Net / Q000502:physiology*; D012146:Rest / Q000502:physiology*