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, we also genetics (Steinmetz et al., 1994). investigated whether multi-template normalization can provide greater Computational mapping methods allow the mapping of genetic power for TBM studies (Leporé et al., 2008d). In that approach, instead influences on structure volumes throughout the brain, without of aligning images to a single brain, we aligned all images to a set of requiring a priori specification of regions of interest. Among them, different templates, and the results were averaged (Chou et al., 2008, voxel-based methods, such as voxel-based morphometry (VBM) 2009a, 2009b); this approach was originally termed targetless spatial (Ashburner and Friston, 2000), have revealed genetically mediated normalization (Kochunov et al., 2002) and is related to multi-atlas deficits in attention deficit hyperactivity disorder (Van't Ent et al., segmentation methods (Heckemann et al., 2006). This multi-template 2007), anxiety disorders (De Geus et al., 2006) and schizophrenia method can increase the statistical power to detect disease effects and (Hulshoff Pol et al., 2006b). Tensor-Based Morphometry (TBM) is also leads to more accurate segmentations, as it reduces the overall another voxel-based method that has been used successfully to impact of errors in individual registrations by fusing information from a detect morphometric differences associated with aging and Alzhei- large number of registrations among different pairs of brains in the mer's disease (Hua et al., 2008a, 2008b), HIV/AIDS (Brun et al., study. We assessed anatomical resemblance within twin pairs by 2007; Chiang et al., 2006; Leporé et al., 2008a), Williams syndrome computing intraclass correlation coefficient (ICC) maps for the MZ and (Chiang et al., 2007), Fragile X syndrome (Lee et al., 2007), DZ twins. Falconer's heritability statistics (Falconer, 1989), which schizophrenia (Gogtay et al., 2008), and normal brain development estimate the proportion of variance due to genetic differences, were (Hua et al., 2009). computed at each voxel and displayed as a map (as in Thompson et al. As TBM has been extensively used in past studies, we chose this (2001)). Using a more advanced modeling approach (developed in method to analyze our dataset of 23 pairs of MZ and 23 pairs of Chiang et al. (2008), Lee et al. (2009), and based on Neale et al. (1999)), same-sex DZ twins. TBM combines a warping step and a statistical we fitted a structural equation model (SEM) to estimate the proportion step to determine local volume changes. Here, we detected local of local volumetric variability attributable to genetics, shared and similarities between MZ and between DZ twins and then compared unique environmental factors across the whole brain. This variability these two groups, to determine the genetic and environmental was estimated both before and after adjusting the data for individual effects on brain structures. We first hypothesized that brain differences in overall brain scale. The same technique was applied for structure volumes would be more genetically influenced when various lobar and subcortical regions of interest (ROIs) traced on the the data is not adjusted for the overall brain size. We also brain template. predicted that the volumes of brain regions that mature earliest in infancy (e.g., occipital lobes) would be the most highly heritable, Participants while environmental effects would be more readily detected in structures that have a more protracted maturational time-course, We scanned 23 pairs of monozygotic (MZ) (11 male and 12 female such as the frontal lobes. pairs) and 23 pairs of same-sex dizygotic (DZ) twins (10 male and 13 C.C. Brun et al. / NeuroImage 48 (2009) 37–49 39 female pairs), recruited as part of a 5-year research project that will described in Leporé et al. (2008d), which is a variant of previously eventually evaluate 1150 twins. As explained below, to eliminate bias proposed multi-template registration approaches; we registered all and improve registration accuracy, we used a multi-template the subjects to four different templates. The use of four templates approach in which we aligned all the scans to a set of 5 target brains offers a reasonable trade-off between using more templates, which randomly selected from our database (Leporé et al., 2008d). These can reduce registration errors but is very time consuming, and scans were performed with the same protocol on 5 healthy subjects preserving computational efficiency, which requires keeping the that are not included in the genetic analysis (one of them was a number of templates reasonably low (see Chou et al. (2008), for an monozygotic twin, 2 of them were members of two different dizygotic optimization study). twin pairs, and 2 did not have twin siblings). These scans were used as These four templates were fluidly registered to a common 5th templates for the fluid registration step (see Image acquisition and template (see Image acquisition and preprocessing for a description of preprocessing), and all the other images were fluidly registered to these subjects). These registration vector fields were concatenated each of them. The age range for the subjects was 22–25 years old for with the previous registrations, so that all the obtained vector fields (4 all the subjects, including the templates (mean age: 23.8±1.8 SD per subject) sit in a common space. years). Each subject was informed of the goals of the study and signed For each subject, Jacobian matrices J were derived from these four a consent form. The study was approved by the appropriate vector fields (see Measuring volumetric differences). As all the Institutional Review and Research Ethics Boards. defined mathematical entities (four vector fields and their four Zygosity was established objectively by typing nine independent corresponding Jacobian matrices per subject) were computed in a

DNA microsatellite polymorphisms (Polymorphism Information common space, we computed Javerage from the four Jacobian matrices Content N0.7), using standard polymerase chain reaction (PCR) existing at each voxel. We used the mean defined in Arsigny et al. methods and genotyping. These results were cross-checked with (2006), which means is defined in a Riemannian space). blood group (ABO, MNS and Rh), and phenotypic data (hair, skin and The first deformable registration methods were inspired by eye color), giving an overall probability of correct zygosity assign- continuum mechanics. In these elastic or fluid registration ment of greater than 99.99%. All subjects underwent physical and approaches, the image is considered to be embedded in a 3D psychological screening to exclude cases of pathology known to elastic or fluid medium and its deformation at each voxel is affect brain structure. None of the twin subjects reported a history of governed by mechanical equilibrium equations. A similarity significant head injury, neurological or psychiatric illness, substance measure is defined between the deforming image and the target abuse or dependence, or had a first-degree relative with a psychiatric image (simple squared-intensity difference, cross-correlation or disorder. information-theoretic measures, such as normalized mutual infor- mation or Jensen–Rényi divergence (Chiang et al., 2006)) and this Image acquisition and preprocessing similarity term is included as a force term in a partial differential equation (PDE), that regularizes the deformation to avoid any All MR images were collected using a 4 T Bruker Medspec whole folds or tears in the image (see Christensen et al. (1996); Bro- body scanner (Bruker Medical, Ettingen, Germany) at the Center for Nielsen and Gramkow (1996); Gramkow (1996); Leporé et al. Magnetic Resonance (University of Queensland, Australia). Three- (2008b)). dimensional T1-weighted images were acquired with a magnetization Fluid registration methods have been advocated as they prepared rapid gradient echo (MP-RAGE) sequence to resolve anatomy guarantee diffeomorphisms (i.e., smooth, invertible, one-to-one at high resolution. Acquisition parameters were: inversion time (TI)/ mappings) even when the magnitude of the deformations is large repetition time (TR)/echo time (TE)=1500/2500/3.83 ms; flip (Christensen et al., 1996), whereas elastic registration methods are angle=15°; slice thickness=0.9 mm with a 256×256×256 acquisi- typically simpler to implement but only generate diffeomorphic tion matrix. mappings when the deformations are small in magnitude. Our Extracerebral (non-brain) tissues were manually deleted from the study used a fluid registration algorithm built upon an elastic MRI images using the 3D interactive program, Display (Montreal registration method that was developed in a Riemannian frame- Neurological Institute, McGill University, Canada). All scans were then work by Pennec et al. (2005). This method ensures diffeomorphic aligned to the ICBM53 template using 9-parameter registration (i.e., maps when matching anatomy across subjects (for details, see Brun translational and rotational alignment, allowing scaling in 3 indepen- et al. (2007, 2008)). dent directions) found in the FMRIB's Linear Image Registration It is worth noting that the twins' images are not registered by Toolbox, FLIRT (Jenkinson et al., 2002). The ICBM53 template is one of aligning one twin's image directly to the other twin's image. Instead the several standardized adult brain templates, and was generated by they are each individually aligned to a common target image from a nonlinearly registering and averaging 53 high-resolution brain MRI randomly selected control subject who is not a member or the twin scans in the ICBM standard space to improve the signal-to-noise ratio pair. As such, because the MZ twins vary as much, as a group, as the (Collins et al. (1995); Montreal Neurological Institute, McGill DZ group (and the general population). The most important factor University, Canada). for the registration is how much they deviate from the target image. As the target image was not the other member of a twin pair used Fluid registration in the analysis, and was an unrelated individual, a zygosity×regis- tration error interaction is unlikely. Consequently, statistical mea- Non-rigid warping is usually performed by registering each sures assessing resemblance between members of twin pairs can be subject's image, S, to a common target image, T. Most commonly, computed from registration-derived measures without any bias this target is an image selected from the data set or a specially introduced by the registration. constructed Mean Deformation Template (MDT), which has the average geometry (shape) and intensity for the group of subjects Statistical analysis studied (Kochunov et al., 2001). Sometimes, a single image is used rather than a group average, because it has sharper features and Measuring volumetric differences greater anatomical detail. The choice of an MDT can affect the For each registration, there is a corresponding displacement vector precision of the registration (Kochunov et al., 2001; Leporé et al., field. At each voxel, each of the registrations (four per subject) gives a

2008b). To avoid any bias in the registration, we did not use one 3-component vector (noted ux, uy, uz), that describes the displacement single target image. Instead we used the multi-template method of a particle from the template to the initial image (defined in the 40 C.C. Brun et al. / NeuroImage 48 (2009) 37–49 template coordinate system). From those vectors, a Jacobian matrix is observed pattern of effects in the statistical maps (Nichols and computed, defined as Holmes, 2002). These values were computed for both MZ and DZ 0 1 probability maps. AðÞ− AðÞ− AðÞ− x ux y ux z ux B C B Ax Ay Az C Structural equation modeling. Eight regions of interest (ROIs) were B C B C manually traced on the 5th (common) template, using BrainSuite B A − A − A − C B x uy y uy z uy C fi J = B C (Shattuck and Leahy, 2002). These ROIs consisted of the ve lobes, B Ax Ay Az C B C the thalamus, the lateral ventricles and the basal ganglia. For each @ AðÞ− AðÞ− AðÞ− A x uz y uz z uz individual, the volume of each ROI was computed using the Ax Ay Az determinant of the Jacobian matrix, by integrating its value over the appropriate region in the template (see Measuring volumetric Scalar values such as the trace, geodesic anisotropy or the determinant differences). This allows the volume of the ROI in each subject to of the matrices may be analyzed (Leporé et al., 2008a). Here, we chose be estimated. to focus on the analysis of det(J), the expansion factor that detects A variance component analysis was performed at each voxel in fi local volume changes; this is the most common form of TBM analysis. addition to the analysis of the lobar ROI volumes by tting structural → N det J u 1 indicates a local volumetric expansion of the image being equation models (SEMs) at each voxel in the images. This method → studied in comparison to the template and det J u b1 indicates a local was first implemented and promoted by Neale et al. (1999) with the volume shrinkage. genetic modeling program, Mx. Here, we used a more computation- ally efficient version that we recently implemented to permit a voxel- Measuring the genetic influence on brain structures wise analysis (Chiang et al., 2008; Lee et al., 2009). To analyze genetic and environmental correlations in twins, structural equation models Intraclass correlation and Falconer's heritability statistic. To measure can compute the relative contributions of additive genetic (A), shared the resemblance between twin pairs, we first computed the intraclass environmental (C) and random environmental (E) components to correlation coefficient (ICC) for both the MZ and the DZ groups, the variance in the observed local and ROI volume, y. For each twin according to the formula: subject, we can model the value of y as the combination of three latent factors, y=aA+cC+eE; by standard analysis (Neale et al., σ 2 = σ 2 σ 2 : ð Þ ∑ T ICC = b b + w 1 1999), the covariance matrix for the vector (y1, y2) , where 1 or 2 stands for the first or second twin in the same pair, can be modeled 2 2 by: Here, σb is the pooled variance between pairs and σw is the variance within pairs (Scout and Fleiss, 1979). ! Heritability is the proportion of the variation in a measurement ðÞ; ðÞ; 2 2 2 γ 2 2 ∑ cov y1 y1 cov y1 y2 a + c + e a + c that is attributable to genetic factors. We first computed Falconer's = ðÞ; ðÞ; = 2 2 2 2 2 cov y1 y2 cov y2 y2 γa + c a + c + e heritability statistic, h2,defined as twice the difference in correlation ð Þ between MZ and DZ pairs. 2 h2 =24ðÞrðÞMZ − rðÞDZ where cov(u, v) means covariance between u and v. γ=1 for MZ twins, and γ=1/2 for DZ twins, as MZ twins share all and DZ twins Here, r(MZ) and r(DZ) are the ICC measures for the MZ and DZ share on average half of their genetic polymorphisms. Since A, C, and groups, respectively. This statistic has been debated in the twin E are unobservable variables, the path coefficients Θ=(a, c, e) are literature as it is based on the equal environments assumption (that estimated by comparing the covariance matrix implied by the model, MZ and DZ twins have comparable rearing environments; see ∑(Θ), with the sample covariance matrix (S) of the observed variables

Thompson et al. (2002) for a discussion). Even so, it is perhaps the (y1 and y2), using maximum-likelihood fitting (Fornell and Larcker, most widely-reported index of genetic effects on a trait (e.g., in the 1981). twin studies reviewed by Peper et al., 2007), so we report it first before j∑ðÞjΘ ∑ − 1ðÞΘ S − jSj − ð Þ going on to a more sophisticated analysis of genetic effects, based on FLM;β = log + trace log p 3 structural equation models. We did not want to assume that the data on volumetric differences Here, p=2 is the number of observed variables. Under the null (here det(J)) were normally distributed, so we computed p-values hypothesis that S=∑(Θ), minus twice the logarithm of the like- at each voxel with a voxelwise permutation test, to establish a null lihood ratio was simplified to TLM,Θ =(nMZ − 1)FML,Θ,MZ +(nDZ − 1) 2 distribution for the ICC statistics at each voxel (Nichols and Holmes, FML,Θ,DZ, which follows a χ distribution with p(p +1)t degrees of 2002). The null hypothesis for the intraclass correlation was freedom, where t =3 is the number of free model parameters, and

ICC=0 (no correlation). At each permutation, a given subject's scalar nMZ and nDZ are the number of MZ and DZ twin pairs. Acceptance map det(J) was randomly assigned to another subject, and a null of the null hypothesis (p N0.05) indicates a good fit for the model. distribution was computed at each voxel; the r-values computed from The maximum-likelihood computation was performed to find the this randomly-generated distribution were compared to the r-values model parameters and expected covariance for both MZ and DZ for the true assignment to give statistical maps of the significance of pairs. the ICCs. All voxels from the same subject were permuted in the If the variance in the data at each voxel is assumed to be normal, same way at each randomization, as is required to maintain spatial χ2 values could be used to measure the agreement between the continuity of the null distribution. The resulting permutation-based observed and expected covariance matrices, as χ2 is essentially a (non-parametric) p-value is defined as the quantile of the empirical null goodness-of-fit index. To free our fitted SEMs from any assumption distribution where the real data falls. To control the standard error of that the data were normally distributed, we used a permutation- p, we performed 5000 permutations at each voxel (Edgington, 1995). based method to determine the goodness-of-fit(Lee et al., 2009;

To control for multiple spatial comparisons implicit in computing Chiang et al., 2008; Bollen and Stein, 1992). FML,Θ,DZ was minimized maps of statistics, we computed the omnibus probability, which we using an optimization method in the original and in the 5000 call pcorrected, using the suprathreshold volume, for maps thresholded permuted samples in which the twin pair's MZ and DZ labels were at p=0.05. This value assesses the overall significance of the randomly shuffled. At each permutation, three hypotheses with C.C. Brun et al. / NeuroImage 48 (2009) 37–49 41 different Θ were evaluated, in order to fit the ACE, AE or CE models differ from its original distribution. At each voxel and for each region, and the p-values pace, pce, and pae were determined separately by the three probabilities p-values pace, pce and pae were compared and comparing the value of TLM,Θ for the real distribution to the one of the the A, C and E values resulting from the best model were retained. permuted distribution. For each null hypothesis, the sample data was The best model was defined by the p-value that was greater than the 2 rescaled since the permutation distribution of χ statistics, TLM,Θ can two others and also greater than 0.05 (a p-valueb0.05 indicates a

Fig. 1. Top row: Intraclass correlation maps are shown for the monozygotic twins (rMZ; left panel) and for the dizygotic twins (rDZ; right panel). Middle row: An anatomical image (left) shows the sections for which statistics are displayed; maps of Falconer's heritability estimate (h2) show high heritability in subcortical regions. Bottom row: Maps show the p- values (significance) of the intraclass correlation in monozygotic twins (ICCMZ) and dizygotic twins (ICCDZ). 42 C.C. Brun et al. / NeuroImage 48 (2009) 37–49 lack of fit to the data and rejection of the model — note that this is Estimating phenotypic variance in the population analogous to the convention in standard multivariate regression, To examine the variability of the measured phenotypes (Jacobian where a p-valueb0.05 denotes rejection of the null hypothesis, but in determinants, det(J), at each voxel, and ROIs volumes), we our case the genetic model, consisting of the ACE factors, is rejected computed the mean and variance of each volumetric measure when pb0.05. The model is therefore considered to fit when the p- across the sample. To avoid any bias in estimating variance, we valueN0.05. Even so, the significance of a specific term in a model chose a group composed of one subject per pair in the MZ and the (such as the A term) can be assessed by comparing the χ2 goodness- DZ groups, taken randomly. of-fit values of the two competing models also has a χ2 distribution, and its significance can be reported in the conventional way, with Results pb0.05 denoting that the added term provides a better overall fit to the data). Intraclass correlations and Falconer's heritability estimates A similar path analysis and structural equation model can be used to estimate the A, D and E parameters (relative contributions of Fig. 1 shows the intraclass correlation computed for local additive genetic factors, genetic dominance effects and random volumes in both identical and fraternal twins (top left: rMZ and environmental components, respectively on the observed volumes). top right: rDZ). Red colors indicate a high correlation (r close to 1), The ADE model differentiates two genetic effects: the sum of all the whereas blue colors indicate no detectable correlation (r=0). The effects of individual genetic loci (A) and the result of any interactions significance of the intraclass correlations was assessed by computing between alleles at the same genetic locus (D)(Maes, 2005). When p-values corrected for multiple comparisons (bottom left: pICC(MZ), the genetic dominance term fits, the DZ correlation tends to be less pcorrected =0.034; bottom right: pICC(DZ), pcorrected =0.025). A com- than half of the MZ correlation (here we take γ=1/4 to fit D and parison of these two intraclass correlations is given by the maps of γ=1/2 to fit A). Falconer's heritability statistics (h2, middle right panel). Red colors

Fig. 2. Variance component maps for additive genetic (a2 — top left), common (c2 — top right), and unique environmental (e2 — bottom left) factors for the unscaled data. Bottom right: Color-coded maps representing the model choice at each voxel — light blue (yellow and red, respectively) indicates that the best fitting model is obtained with ACE (AE and CE, respectively). The corresponding anatomical sections (a–g) are shown in Fig. 1 – middle left. C.C. Brun et al. / NeuroImage 48 (2009) 37–49 43 indicate greater heritability. The left panel in the middle row shows limbic (cingulate gyrus), occipital and anterior temporal regions. the common anatomical template and the colored lines indicate the While DZ twins resemble each other to a greater degree than different sections exhibited in the color maps. Three features are randomly chosen individuals of the same age and sex. evident: first, for most subcortical regions, MZ twin volumes are correlated between members of the pair at around r=0.5, with Genetic and environmental influences on brain structure variability values much closer to zero for DZ twins. Second, a correction for multiple comparisons reveals that the overall pattern of correlations The influence of additive genetic (A), as well as shared (C) and in the DZ twins is significantly greater than zero; strictly speaking, a unique (E) environmental factors on brain structure volumes are higher proportion of the brain has correlations exceeding the mapped in Figs. 2 and 3 for the unscaled and scaled data, res- p=0.05 threshold than would be expected by chance if the null pectively. The corresponding values are also reported for eight ROIs hypothesis of no correlation were true. Third, the high values of in Table 1 (unscaled data) and Table 2 (scaled data). heritability (right panel in the middle row), with values over 0.5 for Figs. 2 and 3 display voxelwise maps of the ACE variance the majority of the subcortical regions, are based on twice the components. In each map, the proportion of the overall variance is difference in the intraclass correlations for the MZ and DZ twins. expressed on a scale of 0 (dark blue) to 75% (red). The variance These give an estimate of the proportion of the variance in those components, a2, c2, e2 are proportions, and vary from 0 to 1, but their regions that is genetically mediated. As is also implied by the contribution to the overall variance is often stated as a percentage. In structural equation models below, the heritability maps suggest that the bottom left panel, color-coded maps show the model that the anatomy of the MZ twins resembles each other to a greater provided the best fit. Light blue corresponds to the ACE model, yellow degree than the anatomy of the DZ twins in ventricular, callosal, to the AE model and red to the CE model.

Fig. 3. Variance component maps for additive genetic (a2 — top left), common (c2 — top right), and unique environmental (e2 — bottom left) factors for the scaled data. Bottom right: Color-coded maps representing the model choice at each voxel — light blue (yellow and red, respectively) indicates that the best fitting model fit is obtained with ACE (AE and CE, respectively). The corresponding anatomical sections (a–g) are shown in Fig. 1 – middle left. 44 C.C. Brun et al. / NeuroImage 48 (2009) 37–49

Table 1 Measures of the intraclass correlation coefficients (ICC) for the MZ and DZ groups, Falconer's heritability estimate (h2), the additive genetic (a2), dominant genetic (d2), shared (c2) and unique environmental (e2) variance components, their confidence interval, the probability p (computed from the structural equation models; these exceed 0.05 when the model fits) and the model that provided the best fit when explaining variance in the volumes of these brain regions across subjects for the unscaled data.

Regions WB Fr Par Temp Occ Lmbc Vent BG Th

ICCMZ 0.87 0.86 0.87 0.88 0.87 0.85 0.71 0.86 0.84

ICCDZ 0.74 0.74 0.74 0.74 0.75 0.77 0 0.71 0.76 h2 0.26 0.25 0.27 0.29 0.24 0.17 1.42 0.30 0.17 a2 (%) 34.82 35.28 33.18 36.58 33.26 27.81 0(0–49.64) 39.58 24.08 (0.47–65.66) (0.32–54.18) (3.07–55.81) (31.89–96.97) (30.69–91.19) (1.85–36.86) (0.22–42.57) (0.05–68.59) c2/d2 (%) 55.03 54.44 56.57 54.98 56.71 62.03 63.51 49.43 63.67 (1.52–56.59) (28.16–73.67) (0–57.26) (0.15–55.90) (4.9–56.93) (30.17–78.73) (0.79–63.91) (17.02–95.13) (9.43–98.97) e2 (%) 10.14 10.28 10.25 8.44 10.03 10.15 36.49 10.99 12.26 (7.87–53.03) (7.51–41.24) (5.65–85.93) (1.90–38.22) (3.23–40.38) (10.13–51.53) (0.80–63.91) (0.35–70.10) (0.26–41.98) Probability 0.74 0.74 0.77 0.58 0.56 0.41 0.09 0.91 0.74 Best fitting model ACE ACE ACE ACE ACE ACE ADE ACE ACE

Falconer's heritability estimate is usually considered to be 1 if 2(rMZ-rDZ) exceeds 1. As noted in the main text, the a2 coefficient is a more reliable estimate of heritability than Falconer's estimate. ROIs: WB — Whole brain; Fr — Frontal lobes; Par — Parietal lobes; Temp — Temporal lobes; Occ — Occipital lobes; Lmbc — Limbic lobes; Vent — Ventricles; BG — Basal Ganglia; Th — Thalamus.

If we assume that all the regions of the brain have a partially shared also noticeable in the occipital lobes (20–30% – top left – d). The genetic influence related to overall scale of the brain, then after adjusting comparison of the top right panels in Figs. 2 and 3 shows that overall, for individual differences in brain scale across subjects, a lesser residual genetic influences (a2) are relatively high in the subcortical areas, as well effect of genetic factors should remain (this is based on the fact that as in the occipital areas, which are the earliest to mature in infancy. overall brain volume is heritable). Therefore, we hypothesized that all To summarize the effect of the three factors on global structure brain regions would show a higher heritability prior to the adjustment volumes, ICC, h2 and proportion of variance factors were computed for and we also expected the proportion of variance due to environmental the five lobes, the ventricles, the thalamus, the basal ganglia and the factors to be greater in the unscaled data. whole brain (Tables 1 and 2). In the unscaled data, the proportion of Fig. 3 shows that the influenceofgeneticfactorsisdetectable genetic, shared and unique environmental variance was approxi- throughout the brain in the unscaled data (top left): from 20% of the mately the same for all the lobes (see Table 1), with around 30–40% of genetic variance in the white matter to 75% in subcortical structures such the variance being attributable to genetic differences in the cohort. as the corpus callosum and the ventricles, 20–40% in the basal ganglia and The shared environment also accounted for around half of the the thalamus and 50% in the occipital lobes (d). The effects of the shared variance in these volumes, with the rest of the differences being environment, as shown by the c2 values (Fig. 2, top right panel), are more attributable either to unique environment or measurement errors. prominent than their genetic counterparts in the frontal lobes and in the Volumes for the basal ganglia and the thalamus were shown to be white matter, such as the internal capsule, the uncinate fasciculus and the influenced by genetic factors (A), as well as shared (C) and unique (E) superior longitudinal fasciculus. The unique environment variance (e2) environmental factors (a2 =40%, and c2 =50%, for the basal ganglia — maps demonstrate high variance in the gray matter. As this term accounts a2 =25% and c2 =63% for the thalamus). Between 60% and 70% of the for the individual environmental influence and measurement errors from variance in ventricular volumes was attributable to dominant genetic all sources, it is not possible to distinguish unique environmental effects factors (the ADE model resulted in a better fit than the ACE model, from sources of measurement errors that are uncorrelated between the which may be related to the undetectable DZ correlation (ICC=0) and twins. These maps should therefore be interpreted cautiously. to the increased difference between MZ and DZ correlations (N1/2). As hypothesized, the scaled maps showed less genetic effects This value was still high (50% to 70%) in the scaled data (Table 2), throughout the brain (see Fig. 3 — top right). While scaling the data where the ADE model was also proved to be the best fit. The strong depleted the effects of the common environment on brain structure influence of genetics was also seen in the thalamus and basal ganglia (only the white matter partly exhibits c2 values equal to 20–30%), the (where a2 =58% and d2 =57%, respectively). Even so, the genetic genetic influence is still very strong in the limbic lobe, and the influence on the whole brain volume was considerably smaller after subcortical structures, the ventricles in particular (60%). Effects are scaling, whereas the effect of the common environment decreased

Table 2 Measures of the intraclass correlation coefficients (ICC) for the MZ and DZ groups, Falconer's heritability estimate (h2), the additive genetic (a2), dominant genetic (d2), shared (c2) and unique environmental (e2) variance components, their confidence interval, the probability p (computed from the structural equation models; these exceed 0.05 when the model fits) and the model that provided the best fit when explaining variance in the volumes of these brain regions across subjects for the scaled data.

Regions WB Fr Par Temp Occ Lmbc Vent BG Th

ICCMZ 0.21 0.38 0.09 0.12 0.55 0.57 0.59 0.49 0.61

ICCDZ 0.33 0.46 0.26 0.21 0.26 0.12 0 0.35 0 h2 0 0 0 0 0.58 0.91 1.17 0.27 1.21 a2 (%) 0 0 – 14.91 46.84 17.27 0 56.97 0 (0–25.23) (0–34.76) – (0–38.41) (1.46–65.11) (0.02–79.76) (0–34.92) (0–99.93) (0–68.10) c2/d2 (%) 27.13 42.29 – 0 7.01 34.54 53.12 0 58.32 (3.27–61.47) (0–45.53) – 0 (5.72–54.36) (0.01–41.67) (9.40–53.62) 0 (1.66–58.67) e2 (%) 73.87 57.05 – 85.08 46.13 48.18 46.88 43.03 41.68 (24.43–89.48) (19.70–83.03) – (61.58–100) (28.53–72.13) (28.75–73.95) (26.14–73.95) (0–99.99) (0–79.60) Probability 0.26 0.54 – 0.55 0.38 0.06 0.23 0.61 0.25 Best fitting model ACE ACE No fit AE ADE ADE ADE AE ADE

Falconer's heritability estimate is usually considered to be 1 if 2(rMZ-rDZ) exceeds 1. As noted in the main text, the a2 coefficient is a more reliable estimate of heritability than Falconer's estimate. ROIs: WB — Whole brain; Fr — Frontal lobes; Par — Parietal lobes; Temp — Temporal lobes; Occ — Occipital lobes; Lmbc — Limbic lobes; Vent — Ventricles; BG — Basal Ganglia; Th — Thalamus. C.C. Brun et al. / NeuroImage 48 (2009) 37–49 45

Fig. 4. Variance of the Jacobian across the population for the unscaled (left) and the scaled data (right) displayed as the percentage of the mean at each voxel. Here, blue indicates a small variance in the trait (0%), whereas red indicates a higher variance (5%). It is worth noting that values in the unscaled white matter are higher than values in the scaled white matter except for the subcortical regions (1% versus 0.3%). from 10%. This trend was found for all lobar structures except for the Phenotypic variability in the population 2 2 occipital lobes (ascaled =47%) and the temporal lobes (ascaled =15%). In the scaled and unscaled data, the A, C, E or A, D, E terms fitted in Fig. 4 shows the voxelwise variance of the phenotype in our all cases except for the parietal lobes (where the best p-value=0.01 sample. Whether the brains are scaled or unscaled, variance remains was found for the ACE model in the scaled data, and indicates a lack of relatively high in the subcortical regions (around 5%), where the fit). The full ACE model gave the best fit for all the structures except for genetic influence is the highest (see the two previous paragraphs). the lateral ventricles (where pace =0.048 and pade =0.091) in the Second, although not so clearly evident, values measured in the lobar unscaled data. In comparison, the best fit with ACE was found in the white matter (Figs. 4 f and g) show a difference in variance between scaled whole brain and frontal lobes only (p=0.26 and p=0.54, the unscaled and the scaled data (1% and 0.3%, respectively). This respectively). The ADE model was the best fit for the most genetically effect is also shown in Table 3, where a similar measurement is influenced structures, such as the occipital lobes, the ventricles, and presented for the ROIs. While the variance is higher in the unscaled the thalamus, whereas the AE model performed better in the basal data (8% versus 0.6% for the scaled data) for the whole brain volume, ganglia (pace =0.44, pade =0.40 and pae =0.61) and in the temporal frontal, parietal, temporal lobar and basal ganglia volumes, the scaling lobes (pace =0.41, pade =0.36 and pae =0.55). has little influence on the other structures that are the most gene- Overall, when the scaling effect was removed, the explanatory tically determined. value of the genetic term (A) decreased in all lobar regions. This is in line with expectation, because the variance in substructure volumes Discussion obeys an approximate power law relative to the overall size of the brain (Thompson et al., 2003); in other words, the logarithms of the Findings substructure volumes and overall brain volumes are tightly correlated in normal populations. Because of this dependency, some of the In this study, we combined Tensor-Based Morphometry, a method variance in substructure volumes is correlated with variations in that analyzes morphological brain differences, with models tradi- overall brain volume, which is also highly heritable (see Table 1 and tionally used in genetic studies, including structural equation Introduction). If some of the same genes that influence substructure models, which were computed using a new and efficient method volumes also influence the overall brain volume (which is likely), then (Chiang et al., 2008; Lee et al., 2009). The study had three main adjusting for overall brain volume is likely to decrease the remaining findings. First, we computed correlation maps to visualize the level of genetic proportion of variation in substructure volumes. However, if anatomical similarity for identical and fraternal twin groups, from different genes mediate overall brain volume and substructure which we derived a commonly used measure of heritability. This volumes, adjusting for overall brain volume may (at least in theory) voxelwise method indicated a genetically mediated component of increase the proportion of the remaining variation in substructure variance in subcortical regions (≈50%). The maps revealed the volumes that is genetically mediated. In our data, even after adjusting for brain volume (see Fig. 2), the adjusted occipital, limbic lobar fl volumes were still genetically in uenced. Temporal lobe volumes were Table 3 also controlled by genetics before and after adjustment, but to a lesser Standard deviation of the ROI volumes across the population reported as a percentage of extent, which may be explained by a high c2 value in the inferior the mean of the whole region. 2 temporal lobes, and a high a value in the anterior temporal area, that Regions WB Fr Par Temp Occ Lmbc Vent BG Th – – persisted after scaling (see Fig. 2 top right and left f and g and Fig. 3 Unscaled 8.99 8.94 8.93 8.71 8.81 8.69 17.31 8.93 8.91 – top left – f and g). The environmental (C) component remained high Scaled 0.41 0.40 0.67 0.71 10.37 17.11 11.84 0.77 11.41 in frontal regions. This effect was not seen for subcortical structures, ROIs: WB — Whole brain; Fr — Frontal lobes; Par — Parietal lobes; Temp — Temporal where the genetic term was still dominant in the lateral ventricles, lobes; Occ — Occipital lobes; Lmbc — Limbic lobes; Vent — Ventricles; BG — Basal basal ganglia and thalamus after adjusting for brain scale. Ganglia; Th — Thalamus. 46 C.C. Brun et al. / NeuroImage 48 (2009) 37–49 expected pattern of genetic influence for a heritable trait, in which Besides, generally speaking the more genetically influenced traits identical twins resembled each other the most, and fraternal twins were also the more variable phenotypes in the sample. less so. The resemblance for each type of twin was significant overall and was statistically confirmed to be greater than that likely to be Genetics and the developmental sequence observed by chance in random pairings of individuals of the same age and sex. The extent to which different brain structures are genetically Subcortical regions, in particular the lateral ventricles and corpus influenced is likely to vary spatially across the brain. One plausible callosum, and the occipital lobes that mature the earliest in infancy, developmental hypothesis, based on the developmental sequence showed strong evidence of hereditary influences even after adjust- during childhood, is that the earliest-maturing brain regions have ment for whole brain scale. Our voxel-based maps of the heritability structural volumes that are more genetically influenced. Environ- indicated genetically influenced regions previously implicated in mental influences may be greater for the later-developing brain independent studies by Hulshoff Pol et al. (2006a), such as limbic regions (e.g., the frontal lobes). As we found in our cortical mapping regions (such as the cingulate gyrus), anterior temporal and study of brain development from childhood to early adulthood, occipital lobe regions. In Hulshoff Pol et al. (2006a), the authors (Gogtay et al., 2004), brain regions that mature first in childhood tend used VBM and structural equation models. The additive genetic to support more basic cognitive processes, such as movement (motor components of variance, a2, were slightly higher in their study than and primary sensory cortices), taste (insula), vision (occipital lobes), the ones in our study for similar regions (e.g., ≈80% versus ≈40% and hearing (anterior temporal lobes). The regions with the most for unscaled data). This may be attributable to a larger e2 com- protracted maturational time-course include the prefrontal lobes ponent in our study (which contains any measurement errors). (involved in complex reasoning), temporal lobes and hippocampus Differences may be due to differences in the definitions of regions of (Gogtay et al., 2004). White matter continues to mature throughout interest. In addition, in sample sizes around 100 subjects (as were life (Bartzokis et al., 2008), but most of the white matter myelination used in this study and the one by Hulshoff Pol et al. (2006a)), there that affects regional brain volumes is completed by adolescence, with is a certain margin of error in estimating a2, and these confidence heavy myelination of the corticospinal tracts and occipital lobes intervals have been reported to allow comparisons with other occurring in early infancy (Aubert-Broche et al., 2008). This develop- reports and with future studies. mental sequence may relate to the profile of genetic influences on Also, as hypothesized, when unscaled data was assessed (i.e., brain structure; our initial data are consistent with the hypothesis that before accounting for differences in overall brain scale), the propor- structures subserving basic processes may be under stronger genetic tion of genetic variance was higher for all the unscaled lobar volumes control than those that mature over a more extended period. compared to the unscaled volumes. This is reasonable given that brain volume is itself heritable and is correlated with regional volumes TBM: advantages and limitations (Giedd et al., 2007). This finding is also supported by prior twin studies (Pfefferbaum et al., 2000; Bartley et al., 1997). Prior research has found that key measures of cognitive per- Third, our ACE variance component models largely confirmed the formance, such as IQ, are highly heritable and correlate with frontal results obtained with the statistical maps, namely that genetic lobe gray and white matter volumes (Peper et al., 2007). Here we influences were relatively strong subcortically. Very similar results found moderate genetic influences on frontal lobe volumes, in the were found for the voxelwise maps as for the ROIs. Even so, voxelwise range 30–40% before scaling; values that are comparable with, but maps, derived from the SEMs, may be used to define a more detailed not quite as high as prior studies. The method used to measure pattern for the different influences. For example, the superior/ structures is likely to affect the values for the genetic variance anterior regions of the temporal lobes were found to be under genetic components, because all methods are likely to vary in their control, whereas their inferior parts were more influenced by measurement error variance, which is part of the e2 component of common environment, at least in the unscaled data. We also observed variance, and all variance components are by definition proportions strong shared environmental effects on all the unscaled structure of the overall variance and sum to one. For more specific analysis of volumes except the ventricles, which was not observed in other cortical gray matter variable specialized processing methods may be studies. This finding may be due to the mean age and small variance used to analyze the cortical surface (see Thompson et al. (2004) for a of the sample population (young adults). As the brain changes review), as in our prior genetic studies (Thompson et al., 2001) and throughout life, one might propose that the common environmental in the cortical surface modeling studies by Schmitt et al. (2008). effects are at their strongest immediately after the teenage years (the Although these analyses are more time consuming to perform, a age range studied here) and that this effect may decrease with age, as precise estimate of cortical thickness or gray matter density (and the twin subjects no longer live in the same environment. A similar other features such as cortical curvature and complexity) can be explanation has been proposed in prior studies of heritability of IQ,in derived for each subject. In particular, registration methods devel- which effects of the childhood rearing environment were detected oped for more precise cortical pattern matching (e.g., Thompson et al. early in life but dissipated later in life when the twin subjects lived (2004)) can be used to integrate and compare data from subjects apart. After scaling effects were removed, subcortical structures were whose gyral patterns are different, by fluidly transforming entire generally the most highly genetically influenced, while frontal lobe networks of gyral landmarks into correspondence. Registration error volumes were mostly influenced by shared and unique environment. is reduced, and measurement precision is increased. In general, one We were unable to detect any environmental effect on lateral would expect the genetic and common environmental components ventricular volume. Reveley et al. (1982), Pfefferbaum et al. (2000) to be higher, as a proportion of the overall variance for methods that and Styner et al. (2005) showed that ventricular volume is genetically further reduce the measurement error variance, even if these influenced, and others (Baaré et al., 2001; Wright et al., 2002) found methods are more labor-intensive to perform. As such, heritability that ventricular volumes are equally influenced by genetics (58%) and estimates may be slightly lower for high-throughput voxel-based environment (42%). Inspection of our data revealed that the ven- methods such as TBM or VBM (Hulshoff Pol et al., 2006a) than for tricular volume correlation for DZ twins was relatively low. Choosing methods that explicitly model and register cortical anatomy the ADE model rather than the ACE helped to explain these findings: (Thompson et al., 2001; Schmitt et al., 2008). As twin studies begin the genetics effect on ventricles before and after scaling was shown to to routinely assess hundreds or even thousands of subjects, there will be dominant more than additive, leading to a correlation in MZ that is ultimately be a trade-off between methods that are most efficient to more than double the correlation in DZ twins. use on large cohorts versus those that offer the greatest power to C.C. Brun et al. / NeuroImage 48 (2009) 37–49 47 detect gene effects per subject analyzed, given the resources avail- Acknowledgments able. A related problem pertains to the subcortical structures as registration accuracy could potentially be influenced by the degree of This work was generously supported by NIH grant R01 spatial regularization involved in the warping approach. A highly HD050735 and the National Health and Medical Research Council, regularized method, in which the deformation fields are extremely Australia grant 496682. smooth, may increase the genetic proportion of variance (a2) locally 2 if a was high for global measures. This issue can be further inves- References tigated in the future by studying how the genetic effects on brain structures depend on the smoothness and accuracy of the registra- Arsigny, V., Fillard, P., Pennec, X., Ayache, N., 2006. Log-Euclidean metrics for fast and – tion vector fields. In the future, we plan to further examine genetic simple calculus on diffusion tensors. Mag. Res. Med. 56 (2), 411 421. Ashburner, J., Friston, K.J., 2000. Voxel-based morphometry: the methods. NeuroImage influences on cortical gray matter differences using surface-based 11 (6), 805–821. cortical thickness maps (Thompson et al., 2004), which will take Aubert-Broche, B., Fonov, V., Leppert, I., Pike, G.B., Collins, L.D., 2008. Human brain advantage of the cortical features and explicit surface models to allow myelination from birth to 4.5 years. International conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), New-York, USA, higher order spatial normalization and matching of cortical anatomy. September 7–9. A second more conceptual issue relates to the disentanglement of Baaré, W.F., Hulshoff Pol, H.E., Boomsma, D.I., Posthuma, D., de Geus, E.J., Schnack, H.G., genetic effects on overall brain volume from regional heritability van Haren, N.E., van Oel, C.J., Kahn, R.S., 2001. Quantitative genetic modeling of variation in human brain morphology. Cereb. Cortex 11 (9), 816–824. estimates. In imaging studies designed to identify genes that regulate Bartley, A.J., Jones, D.W., Weinberger, D.R., 1997. Genetic variability of human brain size overall brain volume, there may be no need to adjust the heritability and cortical gyral patterns. Brain 120 (2), 257–269. maps for global volume effects, as potentially important genetic Bartzokis, G., Lu, P.H., Tingus, K., Mendez, M.F., Richard, A., Peters, D.G., Oluwadara, B., Barrall, K.A., Finn, J.P., Villablanca, P., Thompson, P.M., Mintz, effects would be discounted. In the future, however, there will be a J., 2008. Lifespan trajectory of myelin integrity and maximum motor speed. growing interest in genes that regulate specific functional systems and Neurobiol. Aging Oct 15. [Electronic publication ahead of print]. not others, or distributed cortical networks that operate as a Blokland, G.A., McMahon, K.L., Hoffman, J., Zhu, G., Meredith, M., Martin, N.G., Thompson, P.M., de Zubicaray, G.I., Wright, M.J., 2008. Quantifying the heritability functional unit. A first step towards understanding regionally-specific of task-related brain activation and performance during the N-back working genetic influences has been pioneered by Wright et al. (2002) and memory task: a twin fMRI study. Biol. Psychol. 79 (1), 70–79. Schmitt et al. (2007), who proposed a multivariate spatial decom- Bollen, K.A., Stein, R.A., 1992. Bootstrapping goodness-of-fit measures in structural – position of the genetic variance in a dataset. These methods hold equation models. Sociol. Methods Res. 21 (2), 205 229. Bro-Nielsen, M., Gramkow, C., 1996. Fast fluid registration of medical images. promise for identifying genetic effects that affect the development of Proceedings of the 4th International Conference on Visualization in Biomedical an entire system, or overlapping sets of genes involved in the Computing, Hamburg, Germany, September 22–25, pp. 272–276. maturation of different brain regions. These methods often require Brun, C., Leporé, N., Pennec, X., Chou, Y.-Y., Lopez, O.L., Aizenstein, O.J., Becker, J.T., Toga, A.W., Thompson, P.M., 2007. Comparison of standard and Riemannian fluid very large cohorts to rigorously establish these subtle, second-order registration for tensor-based morphometry in HIV/AIDS. Workshop on Statistical effects on heritability. These include the examination of differential Registration: Pair-wise and Group-wise Alignment and Atlas Formation, 10th heritability between different measures or different regions and the International conference on Medical Image Computing and Computer Assisted – fi fl Intervention (MICCAI), Brisbane, Australia, October 29 November 2. detection of common versus speci c genetic in uences on different Brun, C., Leporé, N., Pennec, X., Chou, Y.-Y, Lee, A.D., de Zubicaray, G., McMahon, K., measures, which will be a key target of future studies. Wright, M., Barysheva, M., Toga, A.W., Thompson, P.M., 2008. A new registration Third, there may be age effects on heritability, which means that method based on log-Euclidean tensor metrics and its application to genetic fi studies. Proceedings of the 5th IEEE International Symposium on Biomedical any study of a speci c cohort must be interpreted as applying to the Imaging (ISBI), Paris, France, May 14–17. age range specifically examined (here, early adulthood). In Lenroot et Cannon, T.D., Thompson, P.M., Van Erp, T.G., Toga, A.W., Poutanen, V.P., Huttunen, M., al. (2007), who studied children, the authors argued that there is an Lonnqvist, J., Standerskjold-Nordenstam, C.G., Narr, K.L., Khaledy, M., Zoumalan, C.I., Dail, R., Kaprio, J., 2002. Cortex mapping reveals regionally specific patterns age-related difference in the heritability of cortical thickness. The of genetic and disease-specific gray-matter deficits in twins discordant for primary sensory and motor cortices were found to be predominantly schizophrenia. Proc. Natl. Acad. Sci. 99 (5), 3228–3233. determined by genes in childhood, while later-developing regions, Cannon, T.D., Hennah, W., van Erp, T.G., Thompson, P.M., Lönnqvist, J., Huttunen, M., such as the dorsolateral prefrontal cortex and the temporal lobes may Gasperoni, T., Tuulio-Henriksson, A., Pirkola, T., Toga, A.W., Kaprio, J., Mazziotta, J., Peltonen, L., 2005. Association of DISC1/TRAX haplotypes with schizophrenia, experience greater genetic influence later in life. reduced prefrontal gray matter, and impaired short- and longterm memory. Arch. Lenroot et al. (2009) suggested that brain regions related to higher Gen. Psychiatry, 62 (11), 1205–1213. cognitive functions may have morphometric variance that is more Chiang, M.-C., Dutton, R.A., Hayashi, K.M., Toga, A.W., Lopez, O.L., Aizenstein, H.J., Becker, J.T., Thompson, P.M., 2006. Fluid registration of medical images using heritable in adolescence than childhood, as that is when they are most Jensen–Rényi divergence reveals 3D-profile of brain atrophy in HIV/AIDS. rapidly developing. Gene–environment interaction effects may also Proceedings of the 3rd IEEE International Symposium on Biomedical Imaging, complicate and further enhance this increase in heritability during the Arlington, Virginia, USA, 6–9 April. Chiang, M.-C., Dutton, R.A., Hayashi, K.M., Toga, A.W., Lopez, O.L., Aizenstein, H.J., Becker, most rapid period of development. As such, longitudinal studies will J.T., Thompson, P.M., 2007. 3D pattern of brain abnormalities in Williams syndrome be needed on large datasets (Wright et al., 2001) to account for age visualized using tensor-based morphometry. Neuroimage 36 (4), 1096–1109. effects, or even temporary effects on heritability that may occur Chiang, M.-C., Barysheva, M., Lee, A.D., Madsen, S.K., Klunder, A.D., Toga, A.W., McMahon, K.L., de Zubicaray, G.I., Meredith, M., Wright, M.J., Srivastava, A., Balov, during an active growth spurt and may not persist later when growth N., Thompson, P.M., 2008. Brain fiber architecture, genetics, and intelligence: a High rates are lower and population variance is lower (Giedd et al., 2007). Angular Resolution Diffusion Imaging (HARDI) study. Workshop on Computational An additional fruitful direction involves the genetics of diffusion Diffusion MRI, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), New-York, USA, September 6. tensor imaging (DTI). Hulshoff Pol et al. (2006a) showed that the Chou, Y.-Y., Leporé, N., de Zubicaray, G.I., Carmichael, O.T., Becker, J.T., Toga, A.W., cross-sectional area and fiber integrity (fractional anisotropy) of the Thompson, P.M., 2008. Automated ventricular mapping with multi-atlas fluid image corpus callosum was mostly determined by genes, a finding that has alignment reveals genetic effects in Alzheimer's Disease. Neuroimage 40 (2), 615–630. been verified at different ages across the human lifespan. More recent Chou, Y.-Y., Leporé, N., Chiang, M.-C., Avedissian, C., Barysheva, M., McMahon, K.L., de Zubicaray, G.I., Meredith, M., Wright, M.J., Toga, A.W., Thompson, P.M., 2009a. bivariate genetic modeling studies using high-angular resolution Mapping genetic influences on ventricular structure in twins. NeuroImage 44 diffusion imaging (HARDI) in twins suggest that DTI-derived mea- (4), 1312–1323. sures of fiber architecture and IQ may be both highly heritable and Chou, Y.-Y., Leporé, N., Brun, C.C., Barysheva, M., McMahon, K., de Zubicaray, G.I., Wright, fl M.J., Toga, A.W., Thompson, P.M., 2009b. Can tissue segmentation improve in uenced by overlapping sets of genes (Chiang et al., 2008; Lee et al., registration? A study of 92 twins. 15th Annual Meeting of the Organization for 2009). Future studies combining DTI, functional, and cognitive Human Brain Mapping, June 18–23, 2009, San Francisco, California. measurements with neuroanatomy (Glahn et al., 2007b) are needed Christensen, E.G., Rabbitt, R.D., Miller, M.I., 1996. Deformable templates using large deformation kinematics. IEEE Trans. Image Process. 5 (10), 1435–1447. to better establish links between genetics, functional MRI signals and Collins, L., Holmes, C.J., Peters, T.M., Evans, A.C., 1995. Automatic 3D model-based cognition (see Blokland et al. (2008) and De Zubicaray et al. (2008)). neuroanatomical segmentation. Hum. Brain Mapp. 3 (3), 190–208. 48 C.C. Brun et al. / NeuroImage 48 (2009) 37–49

De Geus, E.J.C., Van't Ent, D., Wolfensberger, S.P.A., Heutink, P., Hoogendijk, W.J.G., Leporé, N., Brun, C.C., Pennec, X., Chou, Y.Y., Lopez, O.L., Aizenstein, H.J., Becker, J.T., Toga, Boomsma, D.I., Veltman, D.J., 2006. Intrapair differences in hippocampal volume in A.W., Thompson, P.M., 2007. Mean template for tensor-based morphometry using monozygotic twins discordant for the risk for anxiety and depression. Biol. deformation tensors. Proceedings, 10th International conference on Medical Image Psychiatry 61 (9), 1062–1071. Computing and Computer Assisted Intervention (MICCAI), Brisbane, Australia, De Zubicaray, G.I., Chiang, M.-C., McMahon, K.L., Shattuck, D.W., Toga, A.W., Martin, N.G., October 29–November 2 10 (2), 826–833. Wright, M.J., Thompson, P.M., 2008. Meeting the challenges of neuroimaging Leporé,N.,Brun,C.,Chou,Y.-Y,Chiang,M.-C.,Dutton,R.A.,Hayashi,K.M., genetics. Brain Imaging Behav. 2 (4), 258–263. Lueders, E., Lopez, O.L., Aizenstein, H.J., Toga, A.W., Becker, J.T., Thompson, Edgington, E.S., 1995. Randomization Tests3rd Edition. Marcel Dekker. P.M., 2008a. Generalized tensor-based morphometry of HIV/AIDS using Falconer, D.S., 1989. Introduction to Quantitative Genetics3rd Ed. Longman, Essex, UK. multivariate statistics on deformation tensors. IEEE Trans. Med. Imaging 27 Fornell, C., Larcker, D.F., 1981. Evaluating structural equation models with unobservable (1), 129–141. variables and measurement error. J. Marketing Res. 18 (3), 375–381. Leporé, N., Chou, Y.-Y., Lopez, O.L., Aizenstein, H.J., Becker, J.T., Toga, A.W., Thompson, Giedd, J.N., Schmitt, J.E., Neale, M.C., 2007. Structural brain magnetic resonance imaging P.M., 2008b. Fast 3D fluid registration of brain magnetic resonance images. SPIE of pediatric twins. Hum. Brain Mapp. 28 (6), 474–481. Medical Imaging conference, San Diego, CA, February 17–21. Glahn, D.C., Thompson, P.M., Blangero, J., 2007a. Neuroimaging endophenotypes: Leporé, N., Brun, C., Chou, Y.-Y., Lee, A.D., Barysheva, M., Pennec, X., McMahon, K.L., strategies for finding genes influencing brain structure and function. Hum. Brain Meredith, M., de Zubicaray, G.I., Wright, M., Toga, A.W., Thompson, P.M., 2008c. Best Mapp. 28 (6), 488–501. individual template selection from deformation tensor minimization. Proceedings Glahn, D.C., Paus, T., Thompson, P.M., 2007b. Imaging genomics: mapping the influence of the 5th IEEE International Symposium on Biomedical Imaging (ISBI), Paris, of genetics on brain structure and function. Hum. Brain Mapp. 28 (6), 461–463. France, May 14–17. Gogtay, N., Giedd, J.N., Lusk, L., Hayashi, K.M., Greenstein, D., Vaituzis, A.C., Nugent 3rd, Leporé, N., Brun, C., Chou, Y.-Y., Lee, A.D., Barysheva, M., Pennec, X., McMahon, K.L., T.F., Herman, D.H., Clasen, L.S., Toga, A.W., Rapoport, J.L., Thompson, P.M., 2004. Meredith, M., de Zubicaray, G.I., Wright, M., Toga, A.W., Thompson, P.M., 2008d. Dynamic mapping of human cortical development during childhood through early Multi-atlas tensor-based morphometry and its application to a genetic study of 92 adulthood. Proc. Natl. Acad. Sci. 101 (21), 8174–8179. twins. Workshop on Mathematical Foundations of Computational Anatomy, Gogtay, N.⁎, Lu, A.⁎, Leow, A.D., Klunder, A.D., Lee, A.D., Chavez, A., Greenstein, D., Giedd, International conference on Medical Image Computing and Computer Assisted J.N., Toga, A.W., Rapoport, J.L., Thompson, P.M., 2008. Three-dimensional brain Intervention (MICCAI), New-York, USA, September 6. growth abnormalities in childhood-onset schizophrenia visualized by using tensor- Maes, H.H., 2005. ACE Model, Encyclopedia of Statistics in Behavioral Science. John based morphometry. Proc. Natl. Acad. Sci. U S A. 2008 Oct 14;105 (41),15979–15984 Wiley & Sons, LTD, Chichester. [Electronic publication ahead of print 2008 Oct 13]. [⁎ equal contribution]. Narr, K.L., Szeszko, P.R., Lencz, T., Woods, R.P., Hamilton, L.S., Phillips, O., Robinson, Gramkow, C., Registration of 2D and 3D medical images, Master's thesis, Danish D., Burdick, K.E., DeRosse, P., Kucherlapati, R., Thompson, P.M., Toga, A.W., Technical University, Copenhagen, Denmark (1996) Malhotra, A.K., Bilder, R.M., DTNBP1 is associated with imaging phenotypes in Gray, J.R., Thompson, P.M., 2004. Neurobiology of intelligence: science and ethics. Nat. schizophrenia, Hum Brain Mapp, under revision (2008) Rev. Neurosci. 4 (6), 471–482. Neale, M.C., Boker, S.M., Xie, G., Maes, H.H., 1999. Mx: Statistical Modeling, 5th Edition. Haier, R.J., Jung, R.E., Yeo, R.A., Head, K., Alkire, M.T., 2004. Structural brain variation and Richmond. general intelligence. Lancet 23 (1), 425–433. Nichols, T.E., Holmes, A.P., 2002. Non parametric permutation tests for functional Hattersley, A.T., McCarthy, M.I., 2005. What makes a good genetic association study? neuroimaging: a primer with examples. Hum. Brain Mapp. 15 (1), 1–25. Lancet 366 (9493), 1315–1323. Oppenheim, J.S., Skerry, J.E., Tramo, M.J., Gazzaniga, M.S., 1989. Magnetic resonance Heckemann, R.A., Hajnal, J.V., Aljabar, P., Rueckert, D., Hammers, A., 2006. Automatic imaging morphology of the corpus callosum in monozygotic twins. Ann. Neurol. 26 anatomical brain MRI segmentation combining label propagation and decision (1), 100–104. fusion. NeuroImage 33 (1), 115–126. Pennec, X., Stefanescu, R., Arsigny, V., Fillard, P., Ayache, N., 2005. Riemannian elasticity: Hua, X., Leow, A.D., Levitt, J.G., Caplan, R., Thompson, P.M., Toga, A.W., 2009. Detecting a statistical regularization framework for non-linear registration. Medical Image brain growth patterns in normal children using tensor-based morphometry. Hum. Computing and Computer-Assisted Intervention (MICCAI), Palm Springs, CA, USA, Brain Mapp. 30 (1), 209–219. pp. 943–950. Hua, X., Leow, A.D., Lee, S., Klunder, A.D., Toga, A.W., Leporé, N., Chou, Y.-Y., Chiang, Peper, J.S., Brouwer, R.M., Boomsma, D.I., Kahn, R.S., Hulshoff Pol, H.E., 2007. Genetic M.-C., Barysheva, M., Jack Jr, C.R., Bernstein, M.A., Britson, P.J., Gunter, J.L., Ward, influences on human brain structure: a review of brain imaging studies in twins. C.P., Whitwell, J.L., Borowski, B., Fleisher, A.S., Fox, N.C., Boyes, R., Barnes, J., Hum Brain Mapp, 28 (6), 464–473. Harvey, D., Kornak, J., Schuff, N., Boreta, L., Studholme, C., Alexander, G.E., Pfefferbaum, A., Sullivan, E.V., Swan, G.E., 2000. Brain structure in men remains Weiner, M.W., Thompson, P.M., 2008a. 3D characterization of brain atrophy in highly heritable in the seventh and eighth decades of life. Neurobiol. Aging 21 Alzheimer's disease and mild cognitive impairment using tensor-based (1), 63–74. morphometry. NeuroImage 41 (1), 19–34. Pietiläinen, O.P., Paunio, T., Loukola, A., Tuulio-Henriksson, A., Kieseppä, T., Hua, X., Leow, A.D., Parikshak, N., Lee, S., Chiang, M.C., Toga, A.W., Jack Jr., C.R., Weiner, Thompson, P., Toga, A.W., van Erp, T.G., Silventoinen, K., Soronen, P., Hennah, M.W., Thompson, P.M., Alzheimer's Disease Neuroimaging Initiative, 2008b. Tensor- W.,Turunen,J.A.,Wedenoja,J.,Palo,O.M.,Silander,K.,Lönnqvist,J.,Kaprio,J., based morphometry as a neuroimaging biomarker for Alzheimer's disease: an MRI Cannon, T.D., Peltonen, L., 2008. Association of AKT1 with verbal learning, study of 676 AD, MCI, and normal subjects. NeuroImage 43 (3), 458–469. verbal memory, and regional cortical gray matter density in twins. Am. J. Med. Hulshoff Pol, H.E., Schnack, H.G., Posthuma, D., Mandl, R.C.W., Baaré, W.F., Van Oel, C.J., Genet. B Neuropsychiatr. Genet. 2008 Dec 2. [Electronic publication ahead of Van Haren, N.E., Collins, D.L., Evans, A.C., Amunts, K., Bürgel, U., Zilles, K., de Geus, E., print]. Boomsma, D.I., Kahn, R.S., 2006a. Genetic contributions to human brain Posthuma, D., De Geus, E.J., Baaré, W.F., Hulshoff Pol, H.E., Kahn, R.S., Boomsma, D.I., morphology and intelligence. J. Neurosci. 26 (40), 10235–10242. 2002. The association between brain volume and intelligence is of genetic origin. Hulshoff Pol, H.E., Schnack, H.G., Mandl, R.C.W., Brans, R.G., Van Haren, N.E., Baaré, Nat. Neurosci. 5 (2), 83–84. W.F., Van Oel, C.J., Collins, D.L., Evans, A.C., Kahns, R.S., 2006b. Gray and white Raji, C.A.⁎, Ho, A.J.⁎, Parikshak, N., Becker, J.T., Lopez, O.L., Kuller, L.H., Hua, X., Leow, A.D., matter density changes in monozygotic and same-sex dizygotic twins discordant Toga, A.W., Thompson, P.M., 2008. Mapping effects of body mass index, insulinemia, for schizophrenia using voxel-based morphometry. Neuroimage 31 (2), and diabetes mellitus on brain structure in cognitively normal elders. American 482–488. Academy of Neurology (AAN), 2009. [⁎ equal contribution]. Jack Jr., C.R., Petersen, R.C., Grundman, M., Jin, S., Gamst, A., Ward, C.P., Sencakova, D., Reiss, A.L., Abrams, M.T., Singer, H.S., Ross, J.L., Denckla, M.B., 1996. Brain development, Doody, R.S., Thal, L.J., Members of the Alzheimer's Disease Cooperative Study gender and IQ in children. A volumetric imaging study. Brain 119 (5), 1763–1774. (ADCS), 2008. Longitudinal MRI findings from the vitamin E and donepezil Reveley, A.M., Reveley, M.A., Clifford, C.A., Murray, R.M., 1982. Cerebral ventricular size treatment study for MCI. Neurobiol. Aging 29 (9), 1285–1295. in twins discordant for schizophrenia. Lancet, 1 (8271), 540–541. Jenkinson, M., Bannister, P.R., Brady, J.M., Smith, S.M., 2002. Improvised optimization for Scamvougeras, A., Kigar, D.L., Jones, D., Weinberger, D.R., Witelson, S.F., 2003. Size of the the robust and accurate linear registration and motion correction of brain images. human corpus callosum is genetically determined: an MRI study in mono and Neuroimage 17 (2), 825–841. dizygotic twins. Neurosci. Lett. 338 (2), 91–94. Kochunov, P., Lancaster, J.L., Thompson, P.M., Woods, R., Mazziotta, J., Hardies, J., Fox, P., Shattuck, D.W., Leahy, R.M., 2002. BrainSuite: an automated cortical surface identifica- 2001. Regional spatial normalization: toward an optimal target. J. Comput. Assist. tion tool. Med. Image Anal. 6 (2),129–142. Tomogr. 25 (5), 805–816. Schmitt, J.E., Wallace, G.L., Rosenthal, M.A., Molloy, E.A., Ordaz, S., Lenroot, R.K., Clasen, Kochunov, P., Lancaster, J.L., Thompson, P.M., Toga, A.W., Brewer, P., Hardies, J., Fox, P., L.S., Blumenthal, J.D., Kendler, K.S., Neale, M.C., Giedd, J.N., 2007. A multivariate 2002. An optimized individual target brain in the Talairach coordinate system. analysis of neuroanatomic relationships in a genetically informative pediatric NeuroImage 17 (2), 922–927. sample. Neuroimage 35 (1), 70–82. Lee, A.D., Leow, A.D., Lu, A., Reiss, A.L., Hall, S., Chiang, M.-C., Toga, A.W., Thompson, P.M., Schmitt, J.E., Lenroot, R.K., Wallace, G.L., Ordaz, S., Taylor, K.N., Kabani, N., Greenstein, D., 2007. 3D pattern of brian abnormalities in fragile X Syndrome visualized using Lerch, J.P., Neale, M.C., Giedd, J.N., 2008. Identification of genetically mediated tensor-based morphometry. Neuroimage 34 (3), 924–938. cortical networks: a multivariate study of pediatric twins and siblings. Cereb. Lee, A.D., Leporé, N., Brun, C., Barysheva, M., Chou, Y.-Y., Chiang, M.-C., Madsen, S.K., Cortex 18 (8), 1737–1747. McMahon, K.L., de Zubicaray, G.I., Wright, M., Toga, A.W., Thompson, P.M., 2009. The Scout, P.E., Fleiss, J.L., 1979. Intraclass correlations: uses in assessing rater reliability. multivariate A/C/E model and the genetics of fiber architecture. Proceedings of the Psychol. Bull. 86 (2), 420–428. 6th IEEE International Symposium on Biomedical Imaging (ISBI), Boston, MA, USA, Steinmetz, H., Herzog, A., Huang, Y., Hacklander, T., 1994. Discordant brain-surface June 28–July 1rst. anatomy in monozygotic twin. N. Engl. J. Med. 331 (14), 952–953. Lenroot, R.K., Schmitt, J.E., Ordaz, S.J., Wallace, G.L., Neale, M.C., Lerch, J.P., Kendler, K.S., Styner, M., Lieberman, J.A., McClure, R.K., Weinberger, D.R., Jones, D.W., Gerig, G., 2005. Evans, A.C., Giedd, J.N., 2009. Differences in genetic and environmental influences Morphometric analysis of lateral ventricles in schizophrenia and healthy controls on the human cerebral cortex associated with development during childhood and regarding genetic and disease-specific factors. Proc. Natl. Acad. Sci. USA 102 (13), adolescence. Hum. Brain Mapp. 30 (1), 163–174. 4872–4877. C.C. Brun et al. / NeuroImage 48 (2009) 37–49 49

Sullivan, E.V., Pfefferbaum, A., Swan, G.E., Carmelli, D., 2001. Heritability of hippocampal Erp, T.G., Cannon, T.D., Toga, A.W., 2004. Mapping cortical change in Alzheimer's size in elderly twin men: equivalent influence from genes and environment. disease, brain development, and schizophrenia. Neuroimage 23 (1), 2–18. Hippocampus 11 (6), 754–762. Van't Ent, D., Lehn, H., Derks, E.M., Hudziak, J.J., Van Strien, N.M., Veltman, D.J., Sullivan, P.F., 2007. Spurious genetic associations. Biol. Psychiatry 61 (10), 1121–1126. de Geus, E.J.C., Todd, R.D., Boomsma, D.I., 2007. A structural MRI study in Thompson, P.M., Cannon, T.D., Narr, K.L., Van Erp, T., Poutanen, V.P., Huttunen, M., monozygotic twins concordant or discordant for attention/hyperactivity Lönnqvist, J., Standertskjöld-Nordenstam, C.G., Kaprio, J., Khaledy, M., Dail, R., problems: evidence for genetic and environmental heterogeneity in the Zoumalan, C.I., Toga, A.W., 2001. Genetic influences on brain structure. Nat. developing brain. Neuroimage 35 (3), 1004–1020. Neuroscience 4 (12), 1253–1258. Weinberger, D.R., Bartley, A.J., Jones, D.W., Zigun, J.R., 1992. Regional variations in Thompson, P.M., Cannon, T.D., Toga, A.W., 2002. Mapping genetic influences on human human monozygotic twins. Soc. Neurosci. Abstr. 18, 595. brain structure. Ann. Med. 34 (7–8), 523–536. Wright, M., de Geus, E., Ando, J., Luciano, M., Posthuma, D., Onon, Y., Hansell, N., Van Thompson, P.M., Narr, K.L., Blanton, R.E., Toga, A.W., 2003. Mapping structural Baal, C., Hiraishi, K., Hasegawa, T., Smith, G., Geffen, G., Geffen, L., Kanba, S., alterations of the corpus callosum during brain development and degeneration, Miyake, A., Martin, N., Boomsma, D., 2001. Genetic of cognition: outline of a Book Chapter. In: Iacoboni, M., Zaidel, E. (Eds.), The Parallel Brain, MIT press, collaborative twin study. Twin Res. 4 (1), 48–56. Cambridge. Wright, I.C., Sham, P., Murray, R.M., Weinberger, D.R., Bullmore, E.T., 2002. Genetic Thompson, P.M., Hayashi, K.M., Sowell, E.R., Gogtay, N., Giedd, J.N., Rapoport, J.L., de contributions to regional variability in human brain structure: methods and Zubicaray, G.I., Janke, A.L., Rose, S.E., Semple, J., Doddrell, D.M., Wang, Y., van preliminary results. Neuroimage 17 (1), 256–271.