Structural covariance of brain region volumes is associated with both structural connectivity and transcriptomic similarity

Abstract

An organizational pattern seen in the brain, termed structural covariance, is the statistical association of pairs of brain regions in their anatomical properties. These associations, measured across a population as covariances or correlations usually in cortical thickness or volume, are thought to reflect genetic and environmental underpinnings.Here, we examine the biological basis of structural volume covariance in the mouse brain. We first examined large scale associations between brain region volumes using an atlas-based approach that parcellated the entire mouse brain into 318 regions over which correlations in volume were assessed, for volumes obtained from 153 mouse brain images via high-resolution MRI. We then used a seed-based approach and determined, for 108 different seed regions across the brain and using mouse gene expression and connectivity data from the Allen Institute for Brain Science, the variation in structural covariance data that could be explained by distance to seed, transcriptomic similarity to seed, and connectivity to seed.We found that overall, correlations in structure volumes hierarchically clustered into distinct anatomical systems, similar to findings from other studies and similar to other types of networks in the brain, including structural connectivity and transcriptomic similarity networks. Across seeds, this structural covariance was significantly explained by distance * Corresponding author Email address: yohan.yee@sickkids.ca (Yohan Yee) Preprint submitted to BioRxivAugust 31, 2017 . CC-BY-NC-ND 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/183004 doi: bioRxiv preprint first posted online (17% of the variation, up to a maximum of 49% for structural covariance to the visceral area of the cortex), transcriptomic similarity (13% of the variation, up to maximum of 28% for structural covariance to the primary visual area) and connectivity (15% of the variation, up to a maximum of 36% for structural covariance to the intermediate reticular nucleus in the medulla) of covarying structures. Together, distance, connectivity, and transcriptomic similarity explained 37% of structural covariance, up to a maximum of 63% for structural covariance to the visceral area. Additionally, this pattern of explained variation differed spatially across the brain, with transcriptomic similarity playing a larger role in the cortex than subcortex, while connectivity explains structural covariance best in parts of the cortex, midbrain, and hindbrain. These results suggest that both gene expression and connectivity underlie structural volume covariance, albeit to different extents depending on brain region, and this relationship is modulated by distance.

Organization of thalamocortical structural covariance and a corresponding 3D atlas of the mouse thalamus

“…We note that a consistent finding across multiple studies is that seed-based structural covariance maps are generally bilateral (i.e. homotopic regions tend to have similar structural covariance to seed (Mechelli et al, 2005;Pagani et al, 2016;Yee et al, 2018), reflecting the bilateral patterns of transcriptomic similarity, a major correlate of structural covariance (Yee et al, 2018)). Thus, including both hemispheres would be redundant and would likely give similar results to our findings below.…”

“…Structural covariance can be computed by correlating the structural properties (such as volumes) of pairs of brain regions across a group of individuals (Lerch et al, 2006;Alexander-Bloch et al, 2013a;Evans, 2013). Previous works have shown that structural covariance is associated with both structural (Lerch et al, 2006;Gong et al, 2012;Yee et al, 2018) and functional (Segall et al, 2012) connectivity, and also captures modes of coordinated growth (Alexander-Bloch et al, 2013b), potentially due to the spatially-coordinated expression of genes (Romero-Garcia et al, 2018;Yee et al, 2018). Similar to structural and functional connectivity, seed-based structural covariance maps have been used to parcellate structures including the striatum (Cohen et al, 2008), insula Kelly et al (2012), and hippocampus Plachti et al (2019).…”

“…Guided by the results of Yee et al (2018) that suggests that transcriptomic similarity is an important contributor to the structural covariance signal, the subsequent analyses described are performed using correlation data in which transcriptomic similarity is not regressed out. We did however repeat all analyses after regressing out distance and transcriptomic similarity and found a negligible difference in the overall results; for brevity, these are not presented here.…”

Adolescent tuning of association cortex in human structural brain networks

“…However, the security of this interpretation rests on the more fundamental assumption that structural correlation measured from MRI data on multiple subjects is a reasonable proxy marker of the average weight of axo-synaptic connectivity between regions . Beyond humans (Gong et al, 2012), there is evidence of such correspondence from animal models (Yee et al, 2017).…”

“…The identification of structural correlation networks in mice (Pagani et al, 2016) suggests that they might encompass general features of cortical architecture. Specifically, up to 35% variance in structural correlation in mice was explained by a combination of tract-tracingderived structural connectivity, gene expression and distance (Yee et al, 2017), providing a link of the macroscopic structural networks to underlying microscale cortical organisation.…”