Elsevier

NeuroImage

Volume 53, Issue 3, 15 November 2010, Pages 926-934
NeuroImage

COMT genotype affects prefrontal white matter pathways in children and adolescents

https://doi.org/10.1016/j.neuroimage.2010.01.033Get rights and content

Abstract

Diffusion tensor imaging is widely used to evaluate the development of white matter. Information about how alterations in major neurotransmitter systems, such as the dopamine (DA) system, influence this development in healthy children, however, is lacking. Catechol-O-metyltransferase (COMT) is the major enzyme responsible for DA degradation in prefrontal brain structures, for which there is a corresponding genetic polymorphism (val158met) that confers either a more or less efficient version of this enzyme. The result of this common genetic variation is that children may have more or less available synaptic DA in prefrontal brain regions. In the present study we examined the relation between diffusion properties of frontal white matter structures and the COMT val158met polymorphism in 40 children ages 9–15. We found that the val allele was associated with significantly elevated fractional anisotropy values and reduced axial and radial diffusivities. These results indicate that the development of white matter in healthy children is related to COMT genotype and that alterations in white matter may be related to the differential availability of prefrontal DA. This investigation paves the way for further studies of how common functional variants in the genome might influence the development of brain white matter.

Introduction

By increasing action potential conduction speed for axons, the white matter (or myelin) of the brain facilitates the rapid exchange of signals among different brain regions. In normal development, the volume of white matter increases linearly through the second decade of life (Barnea-Goraly et al., 2005b, Eluvathingal et al., 2007, Hasan et al., 2007, Lenroot and Giedd, 2006, Sowell et al., 2003, Toga et al., 2006), a period during which there are also significant gains in cognitive capacity. The proliferation and differentiation of developing oligiodendrocytes that extend myelinating projections that encircle axons are controlled, in part, by neurotransmitters (Belachew et al., 1999, Bongarzone et al., 1998, Karadottir and Attwell, 2007). Little is known, however, about how neurochemical effects might modulate human brain structural development. Studies of genes that affect neurotransmitter systems, such as catechol-O-methyltransferase (COMT), can provide insight into the neurochemical modulation of the development of brain white matter. The present investigation applied diffusion tensor imaging (DTI) to examine for the first time the effects of the COMT polymorphism on white matter structure in 40 healthy children between 9 and 15 years of age.

COMT is a gene that encodes a key enzyme in the metabolism of dopamine (DA). A single nucleotide polymorphism (SNP; G→A transition at codon 158) leading to a valine (val) to methionine (met) substitution in a coding region of COMT has been found to be associated with a greater than two-fold decrease in COMT enzyme activity and DA catabolism (Chen et al., 2004, Lachman et al., 1996, Lotta et al., 1995). Consequently, the met allele of this polymorphism confers reduced enzymatic activity and subsequently increased DA availability (Chen et al., 2004, Tenhunen et al., 1994, Tunbridge et al., 2004), especially in the prefrontal cortex (PFC), in which COMT enzyme activity is the primary factor that determines synaptic levels of DA (Garris and Wightman, 1994, Karoum et al., 1994).

A useful but simplistic description of the COMT behavioral phenotype is that met allelic loading confers a cognitive processing advantage but concomitant difficulties in affective processing. Moreover, there appears to be specificity to the met-allele cognitive advantage: individuals who carry the met allele perform better on higher-order cognitive processing (i.e., tasks requiring mental manipulation) (Bilder et al., 2002, Bruder et al., 2005, Diaz-Asper et al., 2008, Egan et al., 2001, Goldberg et al., 2003, Joober et al., 2002, Malhotra et al., 2002, Rosa et al., 2004), but do not outperform their val-allele homozygous counterparts in the foundations of these operations (i.e., storage, updating, temporal order, maintenance, planning) (Bilder et al., 2002, Bruder et al., 2005, Goldberg et al., 2003, Williams-Gray et al., 2007). Met-allele carriers have also been found to be characterized by a form of cognitive inflexibility (Drabant et al., 2006, Nolan et al., 2004); consequently, investigators have begun to question the cognitive advantage of carrying the met allele (Barnett et al., 2008, Ho et al., 2005). In the domain of emotional functioning, individuals with a met allele have been found to show higher endocrine and subjective responses to stress (Jabbi et al., 2007), higher harm avoidance (Enoch et al., 2003), increased neuroticism (Enoch et al., 2003, Stein et al., 2005), higher trait anxiety (Woo et al., 2004), lower extraversion (Stein et al., 2005), higher pain sensitivity along with reduced μ-opioid receptor response (Zubieta et al., 2003), potentiated startle reflex (Montag et al., 2008), and increased aggression/hostility (Han et al., 2006, Lachman et al., 1998, Rujescu et al., 2003, Volavka et al., 2004). This pattern of findings suggests that met-allele carriers are more emotionally reactive than are their val-allele homozygous counterparts. Given the extensive descriptions of the behavioral phenotype associated with the COMT gene, investigators have suggested that the COMT gene plays a critical role in an apparent evolutionary trade-off between cognitive and affective functions (Papaleo et al., 2008). Thus, it appears that neither polymorphism is clearly advantaged; instead, we may need to rely on brain endophenotypes, like those measured by DTI, to better delineate the function and role of this gene.

DTI is a non-invasive in vivo method of measuring the diffusion of water as it probes tissue microstructure (Basser et al., 1994b, Basser and Pierpaoli, 1996, Moseley et al., 1990). In areas of densely packed neural fibers, the many axonal membranes will restrict diffusion perpendicular to the fiber orientation, resulting in anisotropic diffusion (Beaulieu, 2002, Le Bihan, 2003, Sen and Basser, 2005). Similarly, in areas with small cell composition, reduced intracellular space will cause restriction of diffusion (Sehy et al., 2002). DTI has been used effectively to obtain information about white matter structure, even in compromised populations such as infants (Berman et al., 2005, Gao et al., 2009, McGraw et al., 2002, Morriss et al., 1999, Mukherjee et al., 2002, Neil et al., 1998, Partridge et al., 2005, Sakuma et al., 1991, Schneider et al., 2004), and children with psychiatric disorders (Ashtari et al., 2005, Barnea-Goraly et al., 2005a, Barnea-Goraly et al., 2004, Eluvathingal et al., 2006, Engelbrecht et al., 2002, Ewing-Cobbs et al., 2006, Hermoye et al., 2006, Lebel et al., 2008a, Nagy et al., 2003, Ono et al., 1997, Zimmerman et al., 1998). Moreover, DTI allows investigators to characterize neural endophenotypes in developmental disorders, particularly those that may involve different brain networks (Muller, 2007). For example, in studies of autism researchers have used DTI to document anomalous values in diffusion and anisotropy (e.g., Barnea-Goraly et al., 2004, Bashat et al., 2007, Sundaram et al., 2008).

DTI yields measures of anisotropy (a measurement of the directionality of water motion) and diffusivity (a measurement of the magnitude of random water diffusion) (Basser, 1995, Basser et al., 1994b, Pierpaoli et al., 1996). The following parameters can be used to quantify the pattern of diffusion: (i) fractional anisotropy (FA), a measure of the intravoxel preferred directionality of water translational random motion, expressed as a ratio ranging from 0 to 1 (0 = isotropic and 1 = unidirectional); (ii) axial diffusivity (AD), the magnitude of water diffusion along the long axis of the axons, equivalent to the primary eigenvalue of diffusion tensor, λ1; and (iii) radial diffusivity (RD), the magnitude of water diffusion perpendicular to the long axis of the axons, equivalent to the average of the 2nd and 3rd eigenvalues of diffusion tensor, λ2 and λ3. These parameters can be obtained for each voxel within the brain and, combined with diffusion tensor tractography, for each major white matter pathway. Recent work indicates that considering FA in conjunction with directional diffusivity information (e.g., AD and RD) is superior to using FA alone for interpreting white matter features (Dougherty et al., 2007, Gao et al., 2009, Hasan, 2006, Song et al., 2005). A fourth possible parameter, mean diffusivity (MD), was not reported in the present study because it is a simple linear combination of AD and RD [MD = (AD + 2RD)/3].

In the present study we examined COMT gene-related differences in four major white matter fiber tracts in a group of children and adolescents. Because the PFC exhibits the greatest alteration in DA function as a result of this SNP (Garris and Wightman, 1994, Gogos et al., 1998, Karoum et al., 1994), we selected four pathways with significant prefrontal terminations as ROIs (Mori et al., 1999). Specifically, we used tractography to delineate genu of the corpus callosum (GCC), which connects the left and right frontal lobes; the anterior thalamic radiation (ATR), which is formed by fibers interconnecting thalamic nuclei and the cerebral cortex of the frontal lobe; the inferior fronto-occipital fasciculus (IFO), which connects anterior frontal regions to the parietal and occipital lobes; and the uncinate fasciculus (UNC), which connects the frontal and temporal lobes. These tracts were traced in each participant and saved as ROIs within which the three DTI parameters of interest (FA, AD, RD) were calculated. We tested these parameters as indicators of differences in white matter microstructure in the three COMT genotype groups (met/met, met/val, and val/val). We hypothesized that altered brain DA levels, influenced by children's COMT genotype, would be related to altered white matter diffusion properties in these four major prefrontal fiber bundles. In addition, to test the specificity of COMT effects on prefrontal white matter, we also examined a control fiber pathway, the splenium of the corpus callosum (SCC), that does not have prefrontal terminations, and that was recently used as a control region in a study examining localized group differences in white matter (Pacheco et al., 2009).

Section snippets

Participants

Participants were 40 children and adolescents (26 females) between the ages of 9 and 15 years (M = 11.06, SD = 1.4). They were recruited through their mothers by online advertisements (posted free on the classified-style website: www.craigslist.com) and parent networks (two www.yahoo.com California Bay Area parent groups comprising more than 4000 members). They responded to notices of Stanford University research studies seeking community participants. Each mother–child pair was compensated $25/h.

Participants

Participants were 25 Caucasians (62.5%), 3 Asian Americans (7.5%), 2 Hispanic Americans (5%), 8 participants of multi- or bi-racial descent (20%), and 2 who were not classified (5%). Participants were 9 to 15 years of age, covering a range in which the dynamic processes of brain myelination are still occurring. Genotyping yielded three groups of children: homozygous met (n = 6); homozygous val (n = 13); and met/val (n = 21). These allelic frequencies were in Hardy–Weinberg equilibrium, χ2(2) = 0.63, p = 

Discussion

The present study was designed to examine COMT gene-related differences in major white matter fiber tracts with prefrontal terminations in children and adolescents. Our results indicate that the COMT genotype is associated with altered diffusion parameters in subcortical white matter in a sample of children and adolescents. These findings support the formulation that variants in COMT genotype affect brain development in major prefrontal pathways. White matter microstructural variations may be

Acknowledgments

This project was supported by awards from the National Institute of Mental Health [MH081583 to MET, RR P41-009874 to GHG, MH074849 to IHG, and NIH EY-15000 to RFD], and by a NARSAD Young Investigator Award to MET. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors thank Melissa L. Henry, Sarah Victor, Emily Dennis, and Rebecca Johnson for their assistance in acquiring the scan data, and

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