Cortical Thickness or Grey Matter Volume? The Importance of Selecting the Phenotype for Imaging Genetics Studies

By Anderson M. Winkler, Peter V. Kochunov, John Blangero, Laura Almasy, Karl Zilles, Peter T. Fox, R. Duggirala, David C. Glahn. NeuroImage, 2009

1,178 citations recorded by the source corpus.

Choosing the appropriate neuroimaging phenotype is critical to successfully identify genes that influence brain structure or function. While neuroimaging methods provide numerous potential phenotypes, their role for imaging genetics studies are unclear. Here we examine the relationship between brain volume, grey matter volume, cortical thickness and surface area, from a genetic standpoint. Four hundred and eighty-six individuals from randomly ascertained extended pedigrees with high-quality T1-weighted neuroanatomic MRI images participated in the study. Surface-based and voxel-based representations of brain structure were derived, using automated methods, and these measurements were analysed using a variance-components method to identify the heritability of these traits and their genetic correlations. All neuroanatomic traits were significantly influenced by genetic factors. Cortical thickness and surface area measurements were found to be genetically and phenotypically independent. While both thickness and area influenced volume measurements of cortical grey matter, volume was more closely related to surface area than cortical thickness. This trend was observed for both the volume-based and surface-based techniques. The results suggest that surface area and cortical thickness measurements should be considered separately and preferred over gray matter volumes for imaging genetic studies.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/8da908f6e343e67c26822f3547c2f1fe47974015

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Citations in the corpus

What is normal in normal aging? Effects of Aging, Amyloid and Alzheimer’s Disease on the Cerebral Cortex and the Hippocampus (2014, background): “In human adults, weak (Winkler et al., 2010) or even negative (Hogstrom et al., 2012) correlations between thickness and area have been observed.”
Mindboggling morphometry of human brains (2016, background): “Volume measurements are influenced by various factors such as cortical thickness, surface area [79], and microstructural tissue properties [80].”
Cortical brain abnormalities in 4474 individuals with schizophrenia and 5098 controls via the ENIGMA consortium (2018, background): “This meta-analysis focuses on regional cortical thickness and surface area rather than volume, based on evidence that they are influenced by separate sets of genes (13,14).”
Cortical abnormalities in bipolar disorder: an MRI analysis of 6503 individuals from the ENIGMA Bipolar Disorder Working Group (2017, neutral): “24,25 By examining regional cortical thickness and surface area differences in individuals with BD relative to healthy controls, we may identify biologically meaningful markers of disease.”
Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: A pilot project of the ENIGMA–DTI working group (2013, methodology): “Methods used to calculate heritability in SOLAR are detailed elsewhere (Kochunov et al., 2010; Winkler et al., 2010).”
The genetic architecture of the human cerebral cortex (2020, background): “and are influenced by largely different genetic factors (5-7).”
Where smart brains are different: A quantitative meta-analysis of functional and structural brain imaging studies on intelligence (2015, background): “However, these measures capture different morphological properties of the brain than VBMstudies on the amount of greymatter (Sanabria-Diaz et al., 2010; Winkler et al., 2010).”
The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data (2014, neutral): “…designs had shown that many brain-derived measures were significantly heritable (Thompson et al. 2001; Baaré et al. 2001; White et al. 2002; Wright et al. 2002; van Erp et al. 2004a, b; Hulshoff Pol et al. 2006; Winkler et al. 2010; Kochunov et al. 2010; Blokland et al. 2012; Koten et al. 2009).”