NeuroImageNeurosciencePeer-reviewed journal article
Structural covariance of brain region volumes is associated with both structural connectivity and transcriptomic similarity
Yohan YeeiD, Darren FernandesiD, Leon FrenchiD, Jacob EllegoodiD, Lindsay S. CahilliD, Dulcie A. VousdeniD, Leigh Spencer NoakesiD, Jan ScholziD, Matthijs C. van EedeiD, Brian J. NiemaniD, John G. SlediD, Jason P. LerchiD
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.\n\nHere, 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.\n\nWe 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 (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.
- Citations
- 66
- FWCI
- 4.01
- Field percentile
- top 6%
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Organization of thalamocortical structural covariance and a corresponding 3D atlas of the mouse thalamus
Yohan Yee, Jacob Ellegood, … Jason P. Lerch·NeuroImagePeer-reviewed·Nov 17, 2023
- A cross-species analysis of neuroanatomical covariance sex differences in humans and mice
Linh Pham, Elisa Guma, … Armin Raznahan·Biology of Sex DifferencesPeer-reviewed·Jul 1, 2025
- Structural covariance networks are coupled to expression of genes enriched in supragranular layers of the human cortex
Rafael Romero-García, Kirstie Jane Whitaker, … Petra E. Vértes·NeuroImagePeer-reviewed·Dec 21, 2017
- Patterns of Cerebellar-Cortical Structural Covariance Mirror Anatomical Connectivity of Sensorimotor and Cognitive Networks
Zaki Alasmar, M. Mallar Chakravarty, … Christopher J. Steele·Human Brain MappingPeer-reviewed·Jan 1, 2025
- Brain structural covariances in the ageing brain in the UK Biobank
Chao Dong, Anbupalam Thalamuthu, … Wei Xiong Wen·Brain Structure and FunctionPeer-reviewed·Jan 1, 2022
- MIND Networks: Robust Estimation of Structural Similarity from Brain MRI
Isaac Sebenius, Jakob Seidlitz, … Sarah Elizabeth Morgan·bioRxiv·Oct 12, 2022