Genetic and environmental influences on the size of specific brain regions in midlife: The VETSA MRI study
Section snippets
Participants
An overview of the longitudinal VETSA project can be found elsewhere (Kremen et al., 2006). The study was approved by the Human Subjects Committees of all involved institutions, and all participants gave written informed consent. A total of 1237 twins participated in wave 1. They were randomly selected from a larger pool of individuals in a prior Vietnam Era Twin Registry study (Tsuang et al., 2001). Registry members are male–male twin pairs born between 1939 and 1957 who both served in the
Results
MZ and DZ correlations and the proportions of variance accounted for by genetic, shared environmental, and individual-specific environmental influences for each of the age, site, and TIV-adjusted volume-based ROIs are shown in Table 1. The same indices for the ROIs measured by thickness (adjusted for age and site only) are shown in Table 2 and in Fig. 3. MZ correlations were consistently higher than DZ correlations, suggesting genetic influences on the size of almost all ROIs. The full (ACE)
Discussion
To our knowledge, this is the first large-scale study to comprehensively examine genetic and environmental influences on the size of specific cortical, subcortical, and ventricular brain structures all in the same individuals. On average, about 70% of the variance in the size of subcortical ROIs and ventricles is determined by genetic factors. Cortical ROIs showed a moderate degree of genetic influence, accounting, on average, for about 45% of the variance in thickness. There was also greater
Acknowledgments
Funded by the National Institute on Aging (AG022381, AG018384, AG018386, AG022982); the National Center for Research Resources (P41-RR14075; NCRR BIRN Morphometric Project BIRN002); the National Institute for Biomedical Imaging and Bioengineering (R01EB006758); the National Institute for Neurological Disorders and Stroke (R01 NS052585-01); and the Mental Illness and Neuroscience Discovery (MIND) Institute, part of the National Alliance for Medical Image Computing (NAMIC), funded by the National
References (49)
- et al.
A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume
NeuroImage
(2004) - et al.
Quantitative genetic modeling of regional brain volumes and cognitive performance in older male twins
Biol. Psychol.
(2002) - et al.
Cortical surface-based analysis. I: segmentation and surface reconstruction
NeuroImage
(1999) - et al.
An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest
NeuroImage
(2006) - et al.
Detection of cortical thickness correlates of cognitive performance: reliability across MRI scan sessions, scanners, and field strengths
NeuroImage
(2008) - et al.
Cortical surface-based analysis. II: inflation, flattening, and a surface-based coordinate system
NeuroImage
(1999) - et al.
Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain
Neuron
(2002) - et al.
Sequence-independent segmentation of magnetic resonance images
NeuroImage
(2004) - et al.
Reliability of MRI-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer
NeuroImage
(2006) - et al.
Reliability in multi-site structural MRI studies: effects of gradient non-linearity correction on phantom and human data
NeuroImage
(2006)
MRI-derived measurements of human subcortical, ventricular and intracranial brain volumes: reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths
NeuroImage
A multivariate analysis of neuroanatomic relationships in a genetically informative pediatric sample
NeuroImage
Cortical volume and speed-of-processing are complementary in prediction of performance intelligence
Neuropsychologia
Genetic contributions to regional variability in human brain structure: methods and preliminary results
NeuroImage
Quantitative genetic modeling of variation in human brain morphology
Cereb. Cortex
Controlling the false discovery rate: a practical and powerful approach to multiple testing
J. R. Stat. Soc. Ser. B (Methodological)
Evidence for genetic variance in white matter hyperintensity volume in normal elderly male twins
Stroke
Mapping genetic influences on ventricular structure in twins
NeuroImage
Normal brain development and aging: quantitative analysis at in vivo MR imaging in healthy volunteers
Radiology
Improved localization of cortical activity by combining EEG and MEG with MRI cortical surface reconstruction: a linear approach
J. Cogn. Neurosci.
Neuroplasticity: changes in grey matter induced by training
Nature
Model-fitting approaches to the analysis of human behavior
Heredity
Introduction to Quantitative Genetics
Feasibility of multi-site clinical structural neuroimaging studies of aging using legacy data
Neuroinformatics
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