A twin study of intracerebral volumetric relationships - PubMed (original) (raw)

A twin study of intracerebral volumetric relationships

J Eric Schmitt et al. Behav Genet. 2010 Mar.

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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Figures

Fig. 1

Confirmatory factor models. Panel A is a sample path diagram of the MTMM model factor pattern. Two classes of latent variables are defined, those pertaining to tissue or spatial location. Though not shown, each path corresponds to a unique, freely estimated parameter. The first letter of the observed variable name corresponds to spatial location (F, frontal; P, parietal; O, occipital; T temporal) and the second to tissue (G, gray; W, white). Given data from twins and family members, the variance can be decomposed into genetic and nongenetic sources (Panel B). For simplicity, the model from only one twin is shown

Fig. 2

Maximum likelihood parameter estimates for the best-fit MTMM model. For simplicity, genetic (panel A) and unique environmental (panel B) factor loadings are shown separately

Fig. 3

Residual variance for variance components with and without a global covariate, organized by cerebral region of interest. Variance components (A, C, and E) are labeled and shown as broken lines. Total variance (V) is shown as a solid line

Fig. 4

Maximum likelihood parameter estimates for the best-fit MTMM model after covarying for total brain volume. For clarity, genetic (panel A) and unique environmental (panel B) factor loadings are shown separately

References

1. Akaike H. Factor analysis and AIC. Psychometrika. 1987;52:317–332.
1. Baaré WF, Hulshoff Pol HE, Boomsma DI, Posthuma D, De Geus EJ, Schnack HG, Van Haren NE, van Oel CJ, Kahn RS. Quantitative genetic modeling of variation in human brain morphology. Cereb Cortex. 2001;11:816–824. -PubMed
1. Bechger TM, Maris G. Structural equation modeling of multiple facet data: extending models for multitrait-multimethod data. Psicologica. 2004;25:253–274.
1. Campbell DT, Fiske DW. Convergent and discriminant validation by the multitrait-multimethod matrix. Psychol Bull. 1959;56:81–105. -PubMed
1. Carmelli D, Swan GE, DeCarli C, Reed T. Quantitative genetic modeling of regional brain volumes and cognitive performance in older male twins. Biol Psychol. 2002;61:139–155. -PubMed

A twin study of intracerebral volumetric relationships - PubMed (original) (raw)