Variance decomposition of MRI-based covariance maps using genetically informative samples and structural equation...

Area of Science:

• Neuroscience
• Behavioral Genetics
• Human Neuroimaging

Background:

• Understanding the genetic basis of intracortical relationships is crucial but understudied in humans.
• High-resolution imaging studies on genetic covariance of brain structure are lacking.

Purpose of the Study:

• To develop and apply a novel method for measuring genetic and environmental covariance of cortical surface features.
• To investigate the genetic underpinnings of relationships between different brain regions and global cortical thickness.

Main Methods:

• Combined quantitative genetic variance decomposition with semi-multivariate algorithms for high-resolution phenotypic covariance measurement.
• Analyzed a large pediatric sample (600 twins, siblings, singletons) using correlational mapping.
• Investigated genetic and environmental relationships between regions of interest and cortical surface.

Main Results:

• Demonstrated significant genetic correlations between the entire cortex and global mean cortical thickness.
• Found mean cortical thickness most strongly correlated with association cortices, driven by genetics.
• Observed high bilateral genetic correlations between homologous gyri, suggesting genetic mediation of interhemispheric covariance.

Conclusions:

• Genetics plays a substantial role in the global patterning of cortical thickness.
• Interhemispheric brain structural covariance is largely mediated by genetic factors.
• Findings support existing knowledge on cortical variability genetics and prior multivariate studies.