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 (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.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.neuroimage.2018.05.028 | DOI Listing |
Equine Vet J
January 2025
UCD School of Agriculture and Food Science, University College Dublin, Dublin, Ireland.
Background: Equine recurrent laryngeal neuropathy (RLN) is an economically important upper respiratory tract (URT) disease with a genetic contribution to risk, but genetic variants independent of height have not been identified for Thoroughbreds. The method of clinical assessment for RLN is critical to accurately phenotype groups for genetic studies.
Objectives: To identify genetic risk loci for RLN in Thoroughbreds in a genome-wide association study (GWAS) following high-resolution phenotyping.
Hum Brain Mapp
January 2025
Department of Psychology, Concordia University, Montreal, Quebec, Canada.
The cortex and cerebellum are densely connected through reciprocal input/output projections that form segregated circuits. These circuits are shown to differentially connect anterior lobules of the cerebellum to sensorimotor regions, and lobules Crus I and II to prefrontal regions. This differential connectivity pattern leads to the hypothesis that individual differences in structure should be related, especially for connected regions.
View Article and Find Full Text PDFHum Brain Mapp
January 2025
Center for MR Research, University Children's Hospital Zurich, Zurich, Switzerland.
The human brain connectome is characterized by the duality of highly modular structure and efficient integration, supporting information processing. Newborns with congenital heart disease (CHD), prematurity, or spina bifida aperta (SBA) constitute a population at risk for altered brain development and developmental delay (DD). We hypothesize that, independent of etiology, alterations of connectomic organization reflect neural circuitry impairments in cognitive DD.
View Article and Find Full Text PDFCommun Stat Theory Methods
March 2024
Division of Biostatistics, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, 53226, Wisconsin,USA.
Competing risks data in clinical trial or observational studies often suffer from cluster effects such as center effects and matched pairs design. The proportional subdistribution hazards (PSH) model is one of the most widely used methods for competing risks data analyses. However, the current literature on the PSH model for clustered competing risks data is limited to covariate-independent censoring and the unstratified model.
View Article and Find Full Text PDFPublic Health Nutr
January 2025
Department of Anthropology and Institute for Policy Research, Northwestern University, Evanston, IL, USA.
Objective: Explore the relationship between water insecurity and food security and their covariates in Mexican households.
Design: A cross-sectional study with nationally representative data from the National Health and Nutrition Survey-Continuous 2021 (in Spanish, ENSANUT-Continua 2021), collected data from 12,619 households.
Setting: Water insecurity was measured using the Household Water Insecurity Experiences (HWISE) Scale in Spanish and adapted to the Mexican context.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!