Abstract
Using high resolution magnetic resonance imaging data, we examined the interrelationships between eight cerebral lobar volumetric measures via both exploratory and confirmatory factor analyses in a large sample (N = 484) of pediatric twins and singletons. These analyses suggest the presence of strong genetic correlations between cerebral structures, particularly between regions of like tissue type or in spatial proximity. Structural modeling estimated that most of the variance in all structures is associated with highly correlated lobar latent factors, with differences in genetic covariance and heritability driven by a common genetic factor that influenced gray and white matter differently. Reanalysis including total brain volume as a covariate dramatically reduced the total residual variance and disproportionately influenced the additive genetic variance in all regions of interest.
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Akaike H (1987) Factor analysis and AIC. Psychometrika 52:317–332
Baaré WF, Hulshoff Pol HE, Boomsma DI, Posthuma D, De Geus EJ, Schnack HG, Van Haren NE, van Oel CJ, Kahn RS (2001) Quantitative genetic modeling of variation in human brain morphology. Cereb Cortex 11:816–824
Bechger TM, Maris G (2004) Structural equation modeling of multiple facet data: extending models for multitrait-multimethod data. Psicologica 25:253–274
Campbell DT, Fiske DW (1959) Convergent and discriminant validation by the multitrait-multimethod matrix. Psychol Bull 56:81–105
Carmelli D, Swan GE, DeCarli C, Reed T (2002) Quantitative genetic modeling of regional brain volumes and cognitive performance in older male twins. Biol Psychol 61:139–155
Carroll SB (2003) Genetics and the making of Homo sapiens. Nature 422:849–857
Chae TH, Walsh CA (2007) Genes that control the size of the cerebral cortex. In: Bock G, Goode J (eds) Cortical development: genes and genetic abnormalities. Wiley, Chichester, pp 79–90
Collins DL, Neelin P, Peters TM, Evans AC (1994) Automatic 3D intersubject registration of MR volumetric data in Standardized Talairach Space. J Comput Assist Tomogr 18:192–205
Coromina L, Coenders G (2006) Reliability and validity of egocentered network data collected via web—a meta-analysis of multilevel multitrait, multimethod studies. Soc Netw 28:209–231
Corten IW, Saris WE, Coenders G, van der Veld W, Aalberts CE, Kornelis C (2002) Fit of different models for multitrait-multimethod experiments. Struct Equ Model 9:213–232
Darlington RB, Dunlop SA, Finlay BL (1999) Neural development in metatherian and eutherian mammals: variation and constraint. J Comp Neurol 411:359–368
Edwards AWF (1972) Likelihood. Cambridge, London
Evans PD, Gilbert SL, Mekel-Bobrov N, Vallender EJ, Anderson JR, Vaez-Azizi LM, Tishkoff SA, Hudson RR, Lahn BT (2005) Microcephalin, a gene regulating brain size, continues to evolve adaptively in humans. Science 309:1717–1720
Finlay BL, Darlington RB (1995) Linked regularities in the development and evolution of mammalian brains. Science 268:1578–1584
Gaitanis JN, Walsh CA (2004) Genetics of disorders of cortical development. Neuroimaging Clin N Am 14:219–229
Geschwind DH, Miller BL, DeCarli C, Carmelli D (2002) Heritability of lobar brain volumes in twins supports genetic models of cerebral laterality and handedness. Proc Natl Acad Sci USA 99:3176–3181
Guerrini R, Marini C (2006) Genetic malformations of cortical development. Exp Brain Res 173:322–333
Hill RS, Walsh CA (2005) Molecular insights into human brain evolution. Nature 437:64–67
Ihaka R, Gentleman R (1996) R: a language for data analysis and graphics. J Comput Graph Statist 299–314
Jenkins TW, Truex RC (1963) Dissection of the human brain as a method for its fractionation by weight. Anat Rec 147:359–366
Jöreskog KG (1971) Statistical analysis of sets of congeneric tests. Psychometrika 52:101
Kendler KS, Heath AC, Martin NG, Eaves LJ (1987) Symptoms of anxiety and symptoms of depression—same genes, different environments. Arch Gen Psychiatry 44:451–457
Mcardle JJ, Goldsmith HH (1990) Alternative common factor models for multivariate biometric analyses. Behav Genet 20:569–608
Mekel-Bobrov N, Gilbert SL, Evans PD, Vallender EJ, Anderson JR, Hudson RR, Tishkoff SA, Lahn BT (2005) Ongoing adaptive evolution of ASPM, a brain size determinant in Homo sapiens. Science 309:1720–1722
Neale MC, Cardon LR (1992) Methodology for genetic studies of twins and families. Kluwer, Dordrecht
Neale MC, Boker SM, Xie G, Maes HH (2002) Mx: statistical modeling. Department of Psychiatry, Medical College of Virginia, Virginia Commonwealth University, Richmond, VA
Panizzon M, Fennema-Notestine C, Eyler LT, Jernigan TL, Prom-Wormley E, Neale M, Jacobson K, Lyons MJ, Grant MD, Franz CE, Xian H, Tsuang M, Fischl B, Seidman LJ, Dale A, Kremen WS (2009) Distinct genetic influences on cortical surface area and cortical thickness. Cereb Cortex 19:2728–2735
Pennington BF, Filipek PA, Lefly D, Chhabildas N, Kennedy DN, Simon JH, Filley CM, Galaburda A, DeFries JC (2000) A twin MRI study of size variations in human brain. J Cogn Neurosci 12:223–232
Peper J, Schnack H, Brouwer R, van Baal C, van Leeuwen M, Collins L, Evans A, Boomsma D, Kahn R, Poll HH (2009) Heritability of brain structure at the onset of puberty: an MRI study in 9-year old twin-pairs. Human Brain Map 30:2184–2196
Posthuma D, De Geus EJ, Neale MC, Hulshoff Pol HE, Baare WEC, Kahn RS, Boomsma D (2000) Multivariate genetic analysis of brain structure in an extended twin design. Behav Genet 30:311–319
Preuss TM, Caceres M, Oldham MC, Geschwind DH (2004) Human brain evolution: insights from microarrays. Nat Rev Genet 5:850–860
R Development Core Team (2005) R: a language and environment for statistical computing. Vienna, Austria
Rakic P (1995) A small step for the cell, a giant leap for mankind—a hypothesis of neocortical expansion during evolution. Trends Neurosci 18:383–388
Rakic P (2009) Evolution of the neocortex: a perspective from developmental biology. Nat Rev Neurosci 10:724–735
Schmitt JE, Wallace GL, Rosenthal MA, Molloy EA, Ordaz S, Lenroot R, Clasen LS, Blumenthal J, Kendler KS, Neale MC, Giedd JN (2007a) A multivariate analysis of neuroanatomic relationships in a genetically informative pediatric sample. Neuroimage 35:70–82
Schmitt JE, Eyler LT, Giedd JN, Kremen WS, Kendler KS, Neale MC (2007b) Review of twin and family studies on neuroanatomic phenotypes and typical neurodevelopment. Twin Res Hum Genet 10:683–694
Schmitt JE, Lenroot RK, Wallace GL, Ordaz S, Taylor KN, Kabani N, Greenstein D, Lerch JP, Kendler KS, Neale MC, Giedd JN (2008) Identification of genetically mediated cortical networks: a multivariate study of pediatric twins and siblings. Cereb Cortex 18:1737–1747
Sled JG, Zijdenbos AP, Evans AC (1998) A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 17:87–97
Tomás JM, Hontangas PM, Oliver A (2000) Linear confirmatory factor models to evaluate multitrait-multimethod matrices: the effects of number of indicators and correlation among methods. Multivar Behav Res 35:469–499
Wallace GL, Schmitt JE, Lenroot R, Viding E, Ordaz S, Rosenthal MA, Molloy EA, Clasen LS, Kendler KS, Neale MC, Giedd JN (2006) A pediatric twin study of brain morphometry. J Child Psychol Psychiatry 47:987–993
Wright IC, Sham P, Murray RM, Weinberger DR, Bullmore ET (2002) Genetic contributions to regional variability in human brain structure: methods and preliminary results. Neuroimage 17:256–271
Zijdenbos AP, Forghani R, Evans AC (2002) Automatic “pipeline” analysis of 3-D MRI data for clinical trials: application to multiple sclerosis. IEEE Trans Med Imaging 21:1280–1291
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This research was supported by the intramural research program of the NIH as well as NIH grants MH-65322, MH-20030, and DA-18673.
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Edited by Kristen Jacobson.
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Schmitt, J.E., Wallace, G.L., Lenroot, R.K. et al. A Twin Study of Intracerebral Volumetric Relationships. Behav Genet 40, 114–124 (2010). https://doi.org/10.1007/s10519-010-9332-6
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DOI: https://doi.org/10.1007/s10519-010-9332-6