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Mapping the Regional Influence of Genetics on Brain Structure Variability NeuroImage 48 (2009) 37–49 Contents lists available at ScienceDirect NeuroImage journal homepage: www.elsevier.com/locate/ynimg Mapping the regional influence of genetics on brain structure variability — A Tensor-Based Morphometry study Caroline C. Brun a, Natasha Leporé a, Xavier Pennec b, Agatha D. Lee a, Marina Barysheva a, Sarah K. Madsen a, Christina Avedissian a, Yi-Yu Chou a, Greig I. de Zubicaray c, Katie L. McMahon c, Margaret J. Wright d, Arthur W. Toga a, Paul M. Thompson a,⁎ a Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, 635 Charles Young Drive South Suite 225, Los Angeles, CA 90095-7334, USA b Asclepios Research Project, INRIA, 2004 route des Lucioles - BP 93, 06902 Sophia-Antipolis Cedex, France c Centre for Magnetic Resonance, University of Queensland, Brisbane, Queensland, 4072, Australia d Genetic Epidemiology Lab, Queensland Institute of Medical Research, PO Royal Brisbane Hospital, Queensland 4029, Australia article info abstract Article history: Genetic and environmental factors influence brain structure and function profoundly. The search for Received 6 December 2008 heritable anatomical features and their influencing genes would be accelerated with detailed 3D maps Revised 4 May 2009 showing the degree to which brain morphometry is genetically determined. As part of an MRI study that will Accepted 5 May 2009 scan 1150 twins, we applied Tensor-Based Morphometry to compute morphometric differences in 23 pairs of Available online 14 May 2009 identical twins and 23 pairs of same-sex fraternal twins (mean age: 23.8±1.8 SD years). All 92 twins' 3D brain MRI scans were nonlinearly registered to a common space using a Riemannian fluid-based warping approach to compute volumetric differences across subjects. A multi-template method was used to improve volume quantification. Vector fields driving each subject's anatomy onto the common template were analyzed to create maps of local volumetric excesses and deficits relative to the standard template. Using a new structural equation modeling method, we computed the voxelwise proportion of variance in volumes attributable to additive (A) or dominant (D) genetic factors versus shared environmental (C) or unique environmental factors (E). The method was also applied to various anatomical regions of interest (ROIs). As hypothesized, the overall volumes of the brain, basal ganglia, thalamus, and each lobe were under strong genetic control; local white matter volumes were mostly controlled by common environment. After adjusting for individual differences in overall brain scale, genetic influences were still relatively high in the corpus callosum and in early-maturing brain regions such as the occipital lobes, while environmental influences were greater in frontal brain regions that have a more protracted maturational time-course. © 2009 Elsevier Inc. All rights reserved. Introduction genetics (Gray and Thompson, 2004) and are correlated with measures of brain structure (Reiss et al., 1996; Thompson et al., 3D maps showing the relative contribution of genetic, shared and 2001; Haier et al., 2004). These image-derived measures (such as unique environmental factors to brain structure can facilitate the gray matter volume) are often called intermediate phenotypes understanding of the influence of genetics on anatomical variability. when they are associated with an illness and are more amenable to Twins have been studied with quantitative genetic models to quantitative genetic analysis (see Glahn et al. (2007a) for a review estimate these different factors. This approach has detected highly of the endophenotype concept). Using this approach, researchers heritable (i.e., genetically influenced) brain features, such as the have identified and confirmed specific genes that are associated whole brain volume and total gray and white matter volumes with structural brain deficits in schizophrenia patients (Cannon (Posthuma et al., 2002). et al., 2002, 2005; Pietiläinen et al., 2008; Narr et al., 2008). Identifying genetically influenced features is important, as genes Association studies (Sullivan, 2007; Hattersley and McCarthy, 2005) at least partially mediate many psychiatric disorders (Van't Ent et and twin studies using a cross-twin cross-trait design (bivariate al., 2007). In addition, many cognitive or behavioral measures in genetic models) (Posthuma et al., 2002) have also found specific normal individuals, such as full-scale IQ, are highly influenced by genes or common sets of genes influencing brain morphology and cognitive performance. Environmental factors (e.g., cardiovascular health, nutrition, ⁎ Corresponding author. exercise, and education) may also exert protective or harmful effects E-mail address: [email protected] (P.M. Thompson). on the structural integrity of the brain (Raji et al., 2008). In 1053-8119/$ – see front matter © 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2009.05.022 38 C.C. Brun et al. / NeuroImage 48 (2009) 37–49 epidemiological studies and drug trials, accounting for genetic and Methods environmental influences on disease progression (e.g., the ApoE4 risk allele in Alzheimer's disease; Hua et al. (2008b)), may adjust for Overview confounds in the analysis of treatment effects (Jack et al., 2008). Twin studies can reveal whether specific neuroanatomical measures InTBM, a population of images is linearly aligned to a common space, are predominantly influenced by genetics or shared or individual then nonrigidly registered (i.e., warped) to a common target brain, environments (see Peper et al. (2007) for a review), by comparing chosen either as one of the subjects in the study or as a specially twin pairs with different degrees of genetic affinity. Identical (or constructed template with the mean geometry for the group of subjects monozygotic, MZ) twins share the same genetic material, whereas being studied. The local expansion or compression factor applied during fraternal (or dizygotic, DZ) twins share, on average, only half of the warping process (also called the Jacobian determinant) is a useful their genetic polymorphisms (random DNA sequence variations that index of volumetric differences between each subject and the template. occur among normal individuals). DZ twins are commonly studied in Morphometric differences are assessed by performing a statistical lieu of other siblings because they are the same age, preventing any analysis of the volumetric differences at each location in the brain. Here, age-related confounds. Identical and fraternal twin pairs are we performed the registration using a fluid registration algorithm first compared to ensure, to the greatest possible extent, comparable proposed in Brun et al. (2007) and further developed in Brun et al. upbringings and family environments despite varying degrees of (2008). For more precise quantification of volumes, we used a multi- genetic resemblance. template scheme described in Leporé et al. (2008c) rather than using The earliest neuroanatomical genetic studies used traditional one single target brain image. We previously studied whether TBM volumetric measures and region of interest analyses to quantify results depend on the choice of template for normalization (Leporé et al., similarity between MZ and DZ twins. Whole brain and hemispheric 2007, 2008c, 2008d). In general, we found that the findings regarding volumes were found to be highly heritable (N80% for the whole brain genetic influences are highly consistent, regardless of which template is in Pfefferbaum et al. (2000); Sullivan et al. (2001) and N94% for the used for spatial normalization. This is encouraging because the power to hemispheric volume in Bartley et al. (1997)). Gray matter and white resolve morphometric variation in TBM depends on the ability to align matter volumes were shown to be 82% and 88% genetically deter- each image accurately to the chosen template. In addition, we also found mined (Baaré et al., 2001), respectively. Oppenheim et al. (1989), that it makes very little difference to the results if the warping is Pfefferbaum et al. (2000), Scamvougeras et al. (2003), and Hulshoff performed on images that have already been segmented into gray and Pol et al. (2006a) showed that the corpus callosum is mostly white matter before spatial normalization (Chou et al., 2008). In Leporé controlled by genes, and this was verified at different stages in life. et al. (2007), we built a mean brain template based on averaging a set of Findings were less consistent for ventricular volume and shape. deformation fields, such that the mean deformation tensor of the Reveley et al. (1982), Pfefferbaum et al. (2000) and Styner et al. mappings from each individual to the template was minimized in a log- (2005) showed these structures to be highly heritable, whereas Euclidean metric that measured deviations from the identity tensor. This other studies (Baaré et al., 2001; Wright et al., 2002) determined involved iterative perturbation of the template until the criterion for the that ventricular volumes are equally influenced by genetics (58%) mean deformation was optimized. Even though the results with this and environment (42%). Gyral and sulcal patterns were shown to be template were very similar to those achieved with an individual brain as widely variable in MZ twins (Weinberger et al., 1992; Bartley et al., the target, it can be used to rule out the possibility that template 1997), suggesting strong environmental influences independent of selection is a source of bias. In complementary work,
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