Elsevier

NeuroImage

Volume 81, 1 November 2013, Pages 306-316
NeuroImage

Genetic and environmental contributions to brain activation during calculation

https://doi.org/10.1016/j.neuroimage.2013.04.118Get rights and content

Highlights

  • A first description of the genetic influence onto the calculation cerebral circuit

  • Plausible importance of the right intraparietal sulcus in number coding at early age

  • Importance of the environment in the maturation of the hemispheric asymmetry

  • Shared cortical network between calculation and eye movements

Abstract

Twin studies have long suggested a genetic influence on inter-individual variations in mathematical abilities, and candidate genes have been identified by genome-wide association studies. However, the localization of the brain regions under genetic influence during number manipulation is still unexplored. Here we investigated fMRI data from a group of 19 MZ (monozygotic) and 13 DZ (dizygotic) adult twin pairs, scanned during a mental calculation task. We examined both the activation and the degree of functional lateralization in regions of interest (ROIs) centered on the main activated peaks. Heritability was first investigated by comparing the respective MZ and DZ correlations. Then, genetic and environmental contributions were jointly estimated by fitting a ACE model classically used in twin studies. We found that a subset of the activated network was under genetic influence, encompassing the bilateral posterior superior parietal lobules (PSPL), the right intraparietal sulcus (IPS) and a left superior frontal region. An additional region of the left inferior parietal cortex (IPC), whose deactivation correlated with a behavioral calculation score, also presented higher similarity between MZ than between DZ twins, thus offering a plausible physiological basis for the observable inheritance of math scores. Finally, the main impact of the shared environment was found in the lateralization of activation within the intraparietal sulcus. These maps of genetic and environmental contributions provide precise candidate phenotypes for further genetic association analyses, and illuminate how genetics and education shape the development of number processing networks.

Introduction

Mastering the ability to solve mathematical problems, even as simple as addition or subtraction, requires a long period of teaching and training and is reached only through several developmental stages (Butterworth, 2005, Von Aster and Shalev, 2007). Behavioral studies of young infants emphasized the importance of early numerical competences such as approximation, comparison or matching of numerical magnitude that exist prior to education (McCrink and Wynn, 2004, Xu and Spelke, 2000). This initial preverbal system is later refined by education and serves as a starting point for the subsequent acquisition of numerical symbols and emergence of exact calculation (Ansari and Dhital, 2006, Verguts and Fias, 2004). Neuroimaging studies of adults and infants converge to localize this preverbal numerical system within the parietal lobe (Cantlon et al., 2009, Castelli et al., 2006, Dehaene et al., 2003, Piazza et al., 2004, Pinel et al., 2001, Temple and Posner, 1998, Venkatraman et al., 2005). An event-related potential study showed that this parietal representation is present even in newborns (Izard et al., 2009) noticeably in the right hemisphere. These results are coherent with the existence of a core system for numerosities that maybe inherited and shared with other animal species (Cantlon and Brannon, 2007, Nieder and Miller, 2004), and forms a promising context for the search of genetic determinant of numerical skills.

Indeed, several experimental approaches consistently demonstrated the importance of genetic factors in explaining individual variability in arithmetic, both in normal and in pathological ranges. Familial studies of developmental dyscalculia, an impairment in the acquisition of arithmetical skills, showed a high prevalence of this developmental disease among siblings (Alarcón et al., 1997, Shalev et al., 2001). Quantitative genetic studies suggested a heritability estimate ranging from 0.2 to 0.9 (Oliver et al., 2004). These results were recently comforted by a genome-wide association analysis, performed on two distinct samples, which isolated ten genetic polymorphisms that may partially explain variation of individual performance in mathematics (Docherty et al., 2010). However, it is unclear whether this genetic contribution is directly related to the core numerical system or whether it reflects variations in parts of the arithmetical network related to language. Indeed, the same authors reported, based on a large twin study, that two-thirds of the genetic factors that contribute to variation in mathematic also affect reading performance (Plomin and Kovas, 2005).

No study to date directly explored the localization of the cerebral regions underlying this genetic contribution. The only available data come from clinical populations characterized by genetic anomalies and which also present impairments in numerical abilities. The most documented pathologies are the VeloCardioFacial Syndrome (VCF) caused by a micro-deletion in the chromosome 22q11, the Turner syndrome caused by the deletion of one X chromosome, and the fragile X syndrome, caused by a mutation of the FMR1 gene. Extensive investigation of VCF patients showed both functional (Eliez et al., 2001) and anatomical (Barnea-Goraly et al., 2005, Eliez et al., 2000) abnormalities in the left parietal region, nearby the supramarginal gyrus. A functional magnetic resonance imaging (fMRI) study of female subjects with fragile X syndrome also demonstrated a correlation between FMR1 protein expression and activation of the left fronto-parietal network during a mental calculation task, in particular around the supramarginal/angular gyrus region (Rivera et al., 2002). Finally, a neuroimaging study of Turner syndrome showed that patients presented an abnormal recruitment of the bilateral intraparietal sulci (IPS) during exact and approximate calculation task, as well as an abnormal anatomical organization of the right intraparietal sulcus (Molko et al., 2003). All of these studies point to an atypical development of the parietal lobe. However, the severity of these diseases, usually accompanied by other cognitive impairments, does not allow evaluating the specificity of the genetic impact onto numerical cognition. It remains unclear how genetic determinants interact with education at the cerebral level in a typically developed brain.

To shed light on this question we collected behavioral and fMRI data on monozygotic (MZ) and dizygotic (DZ) twin adults performing mental calculation tasks. Because MZ twins share 100% of their genetic polymorphism while DZ twins share only 50%, any greater similarity in MZ compared to DZ should reflect a genetic contribution, given that both MZ and DZ pairs are supposed to share equally similar environments. We then aimed here to separate, within the calculation network, activations under genetic contribution from those under environmental contribution. This mapping should help us to understand if the numerical core system is effectively influenced by genetic factors. We also examined the correlations between individual fMRI activations and behavioral scores in arithmetic, an approach which may help isolate the components of the arithmetical network that contribute to the large heritability reported for mathematical skills. Finally, because it has been hypothesized that learning arithmetical rules is accompanied by the recycling of evolutionary older parietal functions, including the nearby parietal regions involved in eye movements (Knops et al., 2009, Simon et al., 2002), we also collected activations related to saccade to test whether they share any genetic contribution with the arithmetical network.

Section snippets

Subjects

19 pairs of monozygotic (MZ) twins (mean age = 23.2 years old) and 13 pairs of dizygotic (DZ) twins (mean age = 22.3 years old) participated to our study. They were all healthy right handed male adults. Zygosity was determined by genetic analysis of single nucleotide polymorphisms (SNPs) extracted from subjects' saliva (DNA collection kit from DNA Genotek/OG-250, DNA Genotek). DNA was collected in a small volume of 200 μL of TE10:1 and transferred to the French Centre National de Génotypage for

Results

Scores from the behavioral calculation task show that MZ and DZ performed equally well on average (mean MZ score = 0.73, standard deviation (sd) = 0.21; mean DZ score = 0.73, sd = 0.21). In agreement with previous behavioral studies using larger numbers of subjects (Kovas et al., 2007b, Loehlin and Nichols, 1976, Oliver et al., 2004, Wadsworth et al., 1995), calculation scores were highly correlated within MZ siblings (ICCMZ = 0.53, p = 0.007; ICCDZ = 0.12, p > 0.1), and both estimates of heritability (h2F = 

Discussion

Comparing the functional correlation in the brain activation of MZ and DZ twins during a subtraction task, we decomposed the cerebral network activated during mental arithmetic into regions showing either a genetic or an environmental contribution. Although the activity evoked by the subtraction task covers a large set of cortical and subcortical areas, functional similarity in brain activation between twins was essentially restricted to the posterior part of the parietal lobe, while no

Conclusion

In conclusion, our results show that genetic determinants and shared environment have distinct and complementary impacts on the cerebral circuits for arithmetic. Both a superior frontoparietal set of areas, which also support eye movements, and the left angular gyrus, are subject to a genetic contribution. Meanwhile, the shared environment (most likely including education practices) primarily affects the functional lateralization of activation in the intraparietal sulcus. Future experiments,

Acknowledgments

We thank C. Lalanne, T. Bourgeron, F. Faucherau, E. Artiges, D. LeBihan, G. Dehaene-Lambertz and A. Jobert for their contribution to data acquisition or analysis. We gratefully acknowledge the CC-IN2P3 (Centre de Calcul de l'Institut National de Physique Nucléaire et de Physique des Particules) for providing a significant amount of the computing resources and services needed for this work, as well as P Calvat and S. Daligault and C. Delpuech for their help. This study was supported financially

References (99)

  • S. Eliez et al.

    Functional brain imaging study of mathematical reasoning abilities in velocardiofacial syndrome (del22q11.2)

    Genet. Med.

    (2001)
  • L. Feigenson et al.

    Core systems of number

    Trends Cogn. Sci.

    (2004)
  • C.K. Gilmore et al.

    Non-symbolic arithmetic abilities and mathematics achievement in the first year of formal schooling

    Cognition

    (2010)
  • R.H. Grabner et al.

    Individual differences in mathematical competence predict parietal brain activation during mental calculation

    NeuroImage

    (2007)
  • R.H. Grabner et al.

    To retrieve or to calculate? Left angular gyrus mediates the retrieval of arithmetic facts during problem solving

    Neuropsychologia

    (2009)
  • D.C. Hyde et al.

    Near-infrared spectroscopy shows right parietal specialization for number in pre-verbal infants

    NeuroImage

    (2010)
  • A. Ischebeck et al.

    How specifically do we learn? Imaging the learning of multiplication and subtraction

    Neuroimage

    (2006)
  • A. Ischebeck et al.

    Flexible transfer of knowledge in mental arithmetic—an fMRI study

    NeuroImage

    (2009)
  • Y. Kovas et al.

    ‘Generalist genes’ and mathematics in 7-year-old twins

    Intelligence

    (2005)
  • K. Landerl et al.

    Dyslexia and dyscalculia: two learning disorders with different cognitive profiles

    J. Exp. Child Psychol.

    (2009)
  • N. Molko et al.

    Functional and structural alterations of the intraparietal sulcus in a developmental dyscalculia of genetic origin

    Neuron

    (2003)
  • T. Nichols et al.

    Valid conjunction inference with the minimum statistic

    NeuroImage

    (2005)
  • M.V. Peelen et al.

    Patterns of fMRI activity dissociate overlapping functional brain areas that respond to biological motion

    Neuron

    (2006)
  • M. Piazza et al.

    Tuning curves for approximate numerosity in the human intraparietal sulcus

    Neuron

    (2004)
  • M. Piazza et al.

    A magnitude code common to numerosities and number symbols in human intraparietal cortex

    Neuron

    (2007)
  • M. Piazza et al.

    Developmental trajectory of number acuity reveals a severe impairment in developmental dyscalculia

    Cognition

    (2010)
  • P. Pinel et al.

    Modulation of parietal activation by semantic distance in a number comparison task

    NeuroImage

    (2001)
  • G.R. Price et al.

    Impaired parietal magnitude processing in developmental dyscalculia

    Curr. Biol.

    (2007)
  • T. Rickard et al.

    The calculating brain: an fMRI study

    Neuropsychologia

    (2000)
  • O. Simon et al.

    Topographical layout of hand, eye, calculation, and language-related areas in the human parietal lobe

    Neuron

    (2002)
  • O. Simon et al.

    Automatized clustering and functional geometry of human parietofrontal networks for language, space, and number

    NeuroImage

    (2004)
  • D. Szűcs et al.

    The parietal distance effect appears in both the congenitally blind and matched sighted controls in an acoustic number comparison task

    Neurosci. Lett.

    (2005)
  • V. Venkatraman et al.

    Neural correlates of symbolic and non-symbolic arithmetic

    Neuropsychologia

    (2005)
  • S.J. Wadsworth et al.

    Covariation among measures of cognitive ability and academic achievement in the Colorado Adoption Project: sibling analysis

    Personal. Individ. Differ.

    (1995)
  • G. Winterer et al.

    Genetics of human prefrontal function

    Brain Res. Rev.

    (2003)
  • F. Xu et al.

    Large number discrimination in 6-month-old infants

    Cognition

    (2000)
  • L. Zago et al.

    Neural correlates of simple and complex mental calculation

    NeuroImage

    (2001)
  • M. Alarcón et al.

    A twin study of mathematics disability

    J. Learn. Disabil.

    (1997)
  • D. Ansari et al.

    Age-related changes in the activation of the intraparietal sulcus during nonsymbolic magnitude processing: an event-related functional magnetic resonance imaging study

    J. Cogn. Neurosci.

    (2006)
  • D. Ansari et al.

    Neural correlates of symbolic number processing in children and adults

    NeuroReport

    (2005)
  • A.A. Brewer et al.

    Functional plasticity in human parietal visual field map clusters: adapting to reversed visual input

    J. Vis.

    (2012)
  • B. Butterworth

    The development of arithmetical abilities

    J. Child Psychol. Psychiatry

    (2005)
  • J.F. Cantlon et al.

    Basic math in monkeys and college students

    PLoS Biol.

    (2007)
  • J.F. Cantlon et al.

    Functional imaging of numerical processing in adults and 4-y-old children

    PLoS Biol.

    (2006)
  • J.F. Cantlon et al.

    The neural development of an abstract concept of number

    J. Cogn. Neurosci.

    (2009)
  • L. Capano et al.

    Mathematical learning disorder in school-age children with attention-deficit hyperactivity disorder

    Can. J. Psychiatry

    (2008)
  • F. Castelli et al.

    Discrete and analogue quantity processing in the parietal lobe: a functional MRI study

    Proc. Natl. Acad. Sci. U. S. A.

    (2006)
  • J. Castronovo et al.

    Numerical estimation in blind subjects: evidence of the impact of blindness and its following experience

    J. Exp. Psychol. Hum. Percept. Perform.

    (2007)
  • F. Chochon et al.

    Differential contributions of the left and right inferior parietal lobules to number processing

    J. Cogn. Neurosci.

    (1999)
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