Elsevier

NeuroImage

Volume 48, Issue 1, 15 October 2009, Pages 37-49
NeuroImage

Mapping the regional influence of genetics on brain structure variability — A Tensor-Based Morphometry study

https://doi.org/10.1016/j.neuroimage.2009.05.022Get rights and content

Abstract

Genetic and environmental factors influence brain structure and function profoundly. The search for heritable anatomical features and their influencing genes would be accelerated with detailed 3D maps showing the degree to which brain morphometry is genetically determined. As part of an MRI study that will scan 1150 twins, we applied Tensor-Based Morphometry to compute morphometric differences in 23 pairs of 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.

Introduction

3D maps showing the relative contribution of genetic, shared and unique environmental factors to brain structure can facilitate the understanding of the influence of genetics on anatomical variability. Twins have been studied with quantitative genetic models to estimate these different factors. This approach has detected highly heritable (i.e., genetically influenced) brain features, such as the whole brain volume and total gray and white matter volumes (Posthuma et al., 2002).

Identifying genetically influenced features is important, as genes at least partially mediate many psychiatric disorders (Van't Ent et al., 2007). In addition, many cognitive or behavioral measures in normal individuals, such as full-scale IQ, are highly influenced by genetics (Gray and Thompson, 2004) and are correlated with measures of brain structure (Reiss et al., 1996, Thompson et al., 2001, Haier et al., 2004). These image-derived measures (such as gray matter volume) are often called intermediate phenotypes when they are associated with an illness and are more amenable to quantitative genetic analysis (see Glahn et al. (2007a) for a review of the endophenotype concept). Using this approach, researchers have identified and confirmed specific genes that are associated with structural brain deficits in schizophrenia patients (Cannon et al., 2002, Cannon et al., 2005, Pietiläinen et al., 2008, Narr et al., 2008). Association studies (Sullivan, 2007, Hattersley and McCarthy, 2005) and twin studies using a cross-twin cross-trait design (bivariate genetic models) (Posthuma et al., 2002) have also found specific genes or common sets of genes influencing brain morphology and cognitive performance.

Environmental factors (e.g., cardiovascular health, nutrition, exercise, and education) may also exert protective or harmful effects on the structural integrity of the brain (Raji et al., 2008). In epidemiological studies and drug trials, accounting for genetic and environmental influences on disease progression (e.g., the ApoE4 risk allele in Alzheimer's disease; Hua et al. (2008b)), may adjust for confounds in the analysis of treatment effects (Jack et al., 2008). Twin studies can reveal whether specific neuroanatomical measures are predominantly influenced by genetics or shared or individual environments (see Peper et al. (2007) for a review), by comparing twin pairs with different degrees of genetic affinity. Identical (or monozygotic, MZ) twins share the same genetic material, whereas fraternal (or dizygotic, DZ) twins share, on average, only half of their genetic polymorphisms (random DNA sequence variations that occur among normal individuals). DZ twins are commonly studied in lieu of other siblings because they are the same age, preventing any age-related confounds. Identical and fraternal twin pairs are compared to ensure, to the greatest possible extent, comparable upbringings and family environments despite varying degrees of genetic resemblance.

The earliest neuroanatomical genetic studies used traditional volumetric measures and region of interest analyses to quantify similarity between MZ and DZ twins. Whole brain and hemispheric volumes were found to be highly heritable (> 80% for the whole brain in Pfefferbaum et al., 2000, Sullivan et al., 2001 and > 94% for the hemispheric volume in Bartley et al. (1997)). Gray matter and white matter volumes were shown to be 82% and 88% genetically determined (Baaré et al., 2001), respectively. Oppenheim et al., 1989, Pfefferbaum et al., 2000, Scamvougeras et al., 2003, and Hulshoff Pol et al. (2006a) showed that the corpus callosum is mostly controlled by genes, and this was verified at different stages in life. Findings were less consistent for ventricular volume and shape. Reveley et al., 1982, Pfefferbaum et al., 2000, Styner et al., 2005 showed these structures to be highly heritable, whereas other studies (Baaré et al., 2001, Wright et al., 2002) determined that ventricular volumes are equally influenced by genetics (58%) and environment (42%). Gyral and sulcal patterns were shown to be widely variable in MZ twins (Weinberger et al., 1992, Bartley et al., 1997), suggesting strong environmental influences independent of genetics (Steinmetz et al., 1994).

Computational mapping methods allow the mapping of genetic influences on structure volumes throughout the brain, without requiring a priori specification of regions of interest. Among them, voxel-based methods, such as voxel-based morphometry (VBM) (Ashburner and Friston, 2000), have revealed genetically mediated deficits in attention deficit hyperactivity disorder (Van't Ent et al., 2007), anxiety disorders (De Geus et al., 2006) and schizophrenia (Hulshoff Pol et al., 2006b). Tensor-Based Morphometry (TBM) is another voxel-based method that has been used successfully to detect morphometric differences associated with aging and Alzheimer's disease (Hua et al., 2008a, Hua et al., 2008b), HIV/AIDS (Brun et al., 2007, Chiang et al., 2006, Leporé et al., 2008a), Williams syndrome (Chiang et al., 2007), Fragile X syndrome (Lee et al., 2007), schizophrenia (Gogtay et al., 2008), and normal brain development (Hua et al., 2009).

As TBM has been extensively used in past studies, we chose this method to analyze our dataset of 23 pairs of MZ and 23 pairs of same-sex DZ twins. TBM combines a warping step and a statistical step to determine local volume changes. Here, we detected local similarities between MZ and between DZ twins and then compared these two groups, to determine the genetic and environmental effects on brain structures. We first hypothesized that brain structure volumes would be more genetically influenced when the data is not adjusted for the overall brain size. We also predicted that the volumes of brain regions that mature earliest in infancy (e.g., occipital lobes) would be the most highly heritable, while environmental effects would be more readily detected in structures that have a more protracted maturational time-course, such as the frontal lobes.

Section snippets

Overview

In TBM, a population of images is linearly aligned to a common space, then nonrigidly registered (i.e., warped) to a common target brain, chosen either as one of the subjects in the study or as a specially constructed template with the mean geometry for the group of subjects being studied. The local expansion or compression factor applied during the warping process (also called the Jacobian determinant) is a useful index of volumetric differences between each subject and the template.

Intraclass correlations and Falconer's heritability estimates

Fig. 1 shows the intraclass correlation computed for local volumes in both identical and fraternal twins (top left: rMZ and top right: rDZ). Red colors indicate a high correlation (r close to 1), whereas blue colors indicate no detectable correlation (r = 0). The significance of the intraclass correlations was assessed by computing p-values corrected for multiple comparisons (bottom left: pICC(MZ), pcorrected = 0.034; bottom right: pICC(DZ), pcorrected = 0.025). A comparison of these two intraclass

Findings

In this study, we combined Tensor-Based Morphometry, a method that analyzes morphological brain differences, with models traditionally used in genetic studies, including structural equation models, which were computed using a new and efficient method (Chiang et al., 2008, Lee et al., 2009). The study had three main findings. First, we computed correlation maps to visualize the level of anatomical similarity for identical and fraternal twin groups, from which we derived a commonly used measure

Acknowledgments

This work was generously supported by NIH grant R01 HD050735 and the National Health and Medical Research Council, Australia grant 496682.

References (83)

  • Hulshoff PolH.E. et al.

    Gray and white matter density changes in monozygotic and same-sex dizygotic twins discordant for schizophrenia using voxel-based morphometry

    Neuroimage

    (2006)
  • JackC.R. et al.

    Longitudinal MRI findings from the vitamin E and donepezil treatment study for MCI

    Neurobiol. Aging

    (2008)
  • JenkinsonM. et al.

    Improvised optimization for the robust and accurate linear registration and motion correction of brain images

    Neuroimage

    (2002)
  • KochunovP. et al.

    An optimized individual target brain in the Talairach coordinate system

    NeuroImage

    (2002)
  • LeeA.D. et al.

    3D pattern of brian abnormalities in fragile X Syndrome visualized using tensor-based morphometry

    Neuroimage

    (2007)
  • PfefferbaumA. et al.

    Brain structure in men remains highly heritable in the seventh and eighth decades of life

    Neurobiol. Aging

    (2000)
  • ReveleyA.M. et al.

    Cerebral ventricular size in twins discordant for schizophrenia

    Lancet,

    (1982)
  • ScamvougerasA. et al.

    Size of the human corpus callosum is genetically determined: an MRI study in mono and dizygotic twins

    Neurosci. Lett.

    (2003)
  • SchmittJ.E. et al.

    A multivariate analysis of neuroanatomic relationships in a genetically informative pediatric sample

    Neuroimage

    (2007)
  • SullivanP.F.

    Spurious genetic associations

    Biol. Psychiatry

    (2007)
  • Van't EntD. et al.

    A structural MRI study in monozygotic twins concordant or discordant for attention/hyperactivity problems: evidence for genetic and environmental heterogeneity in the developing brain

    Neuroimage

    (2007)
  • WrightI.C. et al.

    Genetic contributions to regional variability in human brain structure: methods and preliminary results

    Neuroimage

    (2002)
  • ArsignyV. et al.

    Log-Euclidean metrics for fast and simple calculus on diffusion tensors

    Mag. Res. Med.

    (2006)
  • Aubert-BrocheB. et al.

    Human brain myelination from birth to 4.5 years

  • BaaréW.F. et al.

    Quantitative genetic modeling of variation in human brain morphology

    Cereb. Cortex

    (2001)
  • BartleyA.J. et al.

    Genetic variability of human brain size and cortical gyral patterns

    Brain

    (1997)
  • BartzokisG. et al.

    Lifespan trajectory of myelin integrity and maximum motor speed

    Neurobiol. Aging

    (2008)
  • BollenK.A. et al.

    Bootstrapping goodness-of-fit measures in structural equation models

    Sociol. Methods Res.

    (1992)
  • Bro-NielsenM. et al.

    Fast fluid registration of medical images

  • BrunC. et al.

    Comparison of standard and Riemannian fluid registration for tensor-based morphometry in HIV/AIDS

  • BrunC. et al.

    A new registration method based on log-Euclidean tensor metrics and its application to genetic studies

  • CannonT.D. et al.

    Cortex mapping reveals regionally specific patterns of genetic and disease-specific gray-matter deficits in twins discordant for schizophrenia

    Proc. Natl. Acad. Sci.

    (2002)
  • CannonT.D. et al.

    Association of DISC1/TRAX haplotypes with schizophrenia, reduced prefrontal gray matter, and impaired short- and longterm memory

    Arch. Gen. Psychiatry,

    (2005)
  • ChiangM.-C. et al.

    Fluid registration of medical images using Jensen–Rényi divergence reveals 3D-profile of brain atrophy in HIV/AIDS

  • ChiangM.-C. et al.

    Brain fiber architecture, genetics, and intelligence: a High Angular Resolution Diffusion Imaging (HARDI) study

  • ChouY.-Y. et al.

    Can tissue segmentation improve registration? A study of 92 twins

    15th Annual Meeting of the Organization for Human Brain Mapping

    (2009)
  • ChristensenE.G. et al.

    Deformable templates using large deformation kinematics

    IEEE Trans. Image Process.

    (1996)
  • CollinsL. et al.

    Automatic 3D model-based neuroanatomical segmentation

    Hum. Brain Mapp.

    (1995)
  • De GeusE.J.C. et al.

    Intrapair differences in hippocampal volume in monozygotic twins discordant for the risk for anxiety and depression

    Biol. Psychiatry

    (2006)
  • De ZubicarayG.I. et al.

    Meeting the challenges of neuroimaging genetics

    Brain Imaging Behav.

    (2008)
  • EdgingtonE.S.

    Randomization Tests

    (1995)
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