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Sex interaction of white matter microstructure and verbal IQ in corpus callosum in typically developing children and adolescents

  • Susumu Yokota
    Correspondence
    Corresponding author at: Faculty of Arts and Science, Kyushu University, 744 Motooka Nishi ku, Fukuoka 819-0395, Japan.
    Affiliations
    Faculty of Arts and Science, Kyushu University, Fukuoka, Japan
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  • Hikaru Takeuchi
    Affiliations
    Division of Developmental Cognitive Neuroscience, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
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  • Kohei Asano
    Affiliations
    Kokoro Research Center, Kyoto University, Kyoto, Japan
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  • Michiko Asano
    Affiliations
    Department of Child and Adolescent Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
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  • Yuko Sassa
    Affiliations
    Division of Developmental Cognitive Neuroscience, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
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  • Yasuyuki Taki
    Affiliations
    Division of Developmental Cognitive Neuroscience, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan

    Division of Medical Neuroimaging Analysis, Department of Community Medical Supports, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan

    Department of Nuclear Medicine & Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
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  • Ryuta Kawashima
    Affiliations
    Division of Developmental Cognitive Neuroscience, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan

    Smart Ageing International Research Centre, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
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Open AccessPublished:April 27, 2022DOI:https://doi.org/10.1016/j.braindev.2022.04.003

      Abstract

      Background

      Childhood is an extremely important time for neural development that has a critical role in human intelligence. Efficient information processing is crucial for higher intelligence, so the intra- or inter-hemispheric interaction is vital. However, the relationship between neuroanatomical connections and intelligence in typically developing children, as well as sex differences in this relationship, remains unknown.

      Methods

      Participants were 253 typically developing children (121 boys and 132 girls) aged 5–18. We acquired diffusion tensor imaging data and intelligence using an age-appropriate version of the IQ test; Wechsler Intelligence Scale for Children (WISC) or Wechsler Adult Intelligence Scale (WAIS). We conducted whole-brain multiple regression analysis to investigate the association between fractional anisotropy (FA), which reflects white matter microstructural properties, and each composite score of IQ test (full-scale IQ, performance IQ, and verbal IQ).

      Results

      FA was positively correlated with full-scale IQ in bilateral inferior occipitofrontal fasciculus, genu, and splenium of corpus callosum (CC). FA in the right superior longitudinal fasciculus, bilateral inferior longitudinal fasciculus, and splenium of CC were also positively correlated with performance IQ. Furthermore, we found significant sex interaction between FA in the CC and verbal IQ. FA was positively correlated in boys, and negatively correlated in girls.

      Conclusion

      Results suggest that efficient anatomical connectivity between parietal and frontal regions is crucial for children’s intelligence. Moreover, inter-hemispheric connections play a critical role in verbal abilities in boys.

      Keywords

      1. Introduction

      Intelligence has played a significant role for human development. MRI studies have helped elucidate the relationship between human intelligence versus brain structure and function in children and adolescents. The development of brain structures, including both gray and white matter, is an essential process of neural development in childhood and plays an important role in cognitive function. Several previous studies have revealed that level of intelligence was correlated with brain structures, such as total brain volume, cortical thickness, or gray matter volume [
      • Shaw P.
      • Greenstein D.
      • Lerch J.
      • Clasen L.
      • Lenroot R.
      • Gogtay N.
      • et al.
      Intellectual ability and cortical development in children and adolescents.
      ,
      • Wilke M.
      • Sohn J.-H.
      • Byars A.W.
      • Holland S.K.
      Bright spots: correlations of gray matter volume with IQ in a normal pediatric population.
      ], as well as white matter structural development [
      • Lebel C.
      • Deoni S.
      The development of brain white matter microstructure.
      ,
      • Lebel C.
      • Treit S.
      • Beaulieu C.
      A review of diffusion MRI of typical white matter development from early childhood to young adulthood.
      ].
      Diffusion tensor imaging (DTI) is a powerful modality for investigating white matter microstructure in vivo. This technique makes it possible to track brain white matter development. Previous studies have revealed white matter microstructural changes in development using metrics like fractional anisotropy (FA) or mean diffusivity (MD). Brain development during late childhood and adolescence is characterized by decreasing gray matter and increasing white matter volume [
      • Courchesne E.
      • Chisum H.J.
      • Townsend J.
      • Cowles A.
      • Covington J.
      • Egaas B.
      • et al.
      Normal brain development and aging: quantitative analysis at in vivo MR imaging in healthy volunteers 1.
      ]. The development of white matter microstructure has been delineated from early infants to adults using DTI [
      • Morriss M.C.
      • Zimmerman R.A.
      • Bilaniuk L.T.
      • Hunter J.V.
      • Haselgrove J.C.
      Changes in brain water diffusion during childhood.
      ,
      • Mukherjee P.
      • Miller J.H.
      • Shimony J.S.
      • Conturo T.E.
      • Lee B.C.P.
      • Almli C.R.
      • et al.
      Normal brain maturation during childhood: developmental trends characterized with diffusion-tensor MR Imaging 1.
      ,
      • Mukherjee P.
      • Miller J.H.
      • Shimony J.S.
      • Philip J.V.
      • Nehra D.
      • Snyder A.Z.
      • et al.
      Diffusion-tensor MR imaging of gray and white matter development during normal human brain maturation.
      ,
      • Lebel C.
      • Walker L.
      • Leemans A.
      • Phillips L.
      • Beaulieu C.
      Microstructural maturation of the human brain from childhood to adulthood.
      ]. For example, the corpus callosum (CC) and fornix develop earlier than association fibers such as the arcuate fasciculus or superior longitudinal fasciculus [
      • Lebel C.
      • Gee M.
      • Camicioli R.
      • Wieler M.
      • Martin W.
      • Beaulieu C.
      Diffusion tensor imaging of white matter tract evolution over the lifespan.
      ,
      • Westlye L.T.
      • Walhovd K.B.
      • Dale A.M.
      • Bjornerud A.
      • Due-Tonnessen P.
      • Engvig A.
      • et al.
      Life-span changes of the human brain white matter: diffusion tensor imaging (DTI) and volumetry.
      ]. Moreover, sex differences in white matter microstructure have also been observed. Girls appear to reach mature levels earlier than boys [
      • Wang Y.
      • Adamson C.
      • Yuan W.
      • Altaye M.
      • Rajagopal A.
      • Byars A.W.
      • et al.
      Sex differences in white matter development during adolescence: a DTI study.
      ], which could explain subtle cognitive differences.
      Whilst the relationship between white matter microstructure and cognitive functions are understudied, previous research has revealed that gray matter metrics such as volume or cortical thickness in frontal and parietal or temporal regions are commonly correlated with intelligence [
      • Menary K.
      • Collins P.F.
      • Porter J.N.
      • Muetzel R.
      • Olson E.A.
      • Kumar V.
      • et al.
      Associations between cortical thickness and general intelligence in children, adolescents and young adults.
      ,
      • Pangelinan M.M.
      • Zhang G.
      • VanMeter J.W.
      • Clark J.E.
      • Hatfield B.D.
      • Haufler A.J.
      Beyond age and gender: relationships between cortical and subcortical brain volume and cognitive-motor abilities in school-age children.
      ,
      • Burgaleta M.
      • Johnson W.
      • Waber D.P.
      • Colom R.
      • Karama S.
      Cognitive ability changes and dynamics of cortical thickness development in healthy children and adolescents.
      ,
      • Yokota S.
      • Takeuchi H.
      • Hashimoto T.
      • Hashizume H.
      • Asano K.
      • Asano M.
      • et al.
      Individual differences in cognitive performance and brain structure in typically developing children.
      ]. As for the correlation between white matter microstructure and cognitive function, Schmithorst, Wilke [
      • Schmithorst V.J.
      • Wilke M.
      • Dardzinski B.J.
      • Holland S.K.
      Cognitive functions correlate with white matter architecture in a normal pediatric population: a diffusion tensor MRI study.
      ] found a positive correlation between FA in frontal and occipito-parietal areas and full-scale IQ (FSIQ) in children aged 5–18. More recently, Muetzel, Mous [
      • Muetzel R.L.
      • Mous S.E.
      • van der Ende J.
      • Blanken L.M.E.
      • van der Lugt A.
      • Jaddoe V.W.V.
      • et al.
      White matter integrity and cognitive performance in school-age children: A population-based neuroimaging study.
      ] also found a positive correlation between white matter microstructure and FSIQ in the right uncinate fasciculus in a larger cohort of 6- to 10-year-old children. Chiang, McMahon [
      • Chiang M.-C.
      • McMahon K.L.
      • de Zubicaray G.I.
      • Martin N.G.
      • Hickie I.
      • Toga A.W.
      • et al.
      Genetics of white matter development: a DTI study of 705 twins and their siblings aged 12 to 29.
      ] investigated genetic influence over white matter structure (FA) in 705 children and adolescence twins and their siblings. They found significant association between heritability and intellectual performance in the thalamus, genu and posterior limb of internal capsule, and corona radiata. Regarding sex differences in the relationship between cognitive function and white matter microstructure there was a positive correlation between verbal IQ, which reflects verbal abilities such as verbal reasoning, vocabulary, or verbal working memory, and FA in girls in the superior longitudinal fasciculus (SLF), inferior fronto-occipital fasciculus, and cortico-spinal tract, though the sample size for this study was relatively small [
      • Wang Y.
      • Adamson C.
      • Yuan W.
      • Altaye M.
      • Rajagopal A.
      • Byars A.W.
      • et al.
      Sex differences in white matter development during adolescence: a DTI study.
      ].
      These studies have clarified the relationship between FA and general cognitive ability. However, there are few previous studies examining the relationship between FA and more specific cognitive abilities such as non-verbal visuospatial abilities, or verbal abilities, and the interaction with sex using larger samples.
      This study investigated the relationship between white matter microstructure and cognitive abilities, also considering sex difference in those relationships in a large number of typically developing children. Considering parieto-frontal integration theory of intelligence [
      • Jung R.E.
      • Haier R.J.
      The Parieto-Frontal Integration Theory (P-FIT) of intelligence: converging neuroimaging evidence.
      ] and the association between white matter properties and cognitive abilities [
      • Muetzel R.L.
      • Mous S.E.
      • van der Ende J.
      • Blanken L.M.E.
      • van der Lugt A.
      • Jaddoe V.W.V.
      • et al.
      White matter integrity and cognitive performance in school-age children: A population-based neuroimaging study.
      ], we hypothesized that FA in the SLF, which connects frontal and parietal regions, would correlate with cognitive functions. We also predicted that there would be sex differences in these associations.

      2. Method

      2.1 Participants

      We acquired brain MRI images from 298 typically developing Japanese children (152 boys and 146 girls; age range, 5.6–18.4 years). The details relating to their initial recruitment were described in our previous study [
      • Taki Y.
      • Hashizume H.
      • Sassa Y.
      • Takeuchi H.
      • Asano M.
      • Asano K.
      • et al.
      Breakfast staple types affect brain gray matter volume and cognitive function in healthy children.
      ]. In brief, we recruited only right-handed children who did not have any neurodevelopmental disorders, history of malignant tumors or head traumas involving loss of consciousness using an advertisement in local schools. We also confirmed that all subjects were right-handed using the self-report questionnaire, the “Edinburgh Handedness Inventory” [
      • Oldfield R.C.
      The assessment and analysis of handedness: the Edinburgh inventory.
      ]. After we had explained the purpose and procedures of the study, written informed consent was obtained from each subject and a parent prior to participation in this study. Because DTI data were obtained from only parts of the entire subjects, the imaging analyses were performed with 253 subjects. Final sample comprised 121 boys (mean age = 11.2 ± 2.8 years old) and 132 girls (mean age = 11.9 ± 3.3 years old). This study was approved by the Institutional Review Board of Tohoku University and conducted in accordance to the Declaration of Helsinki (1991).

      2.2 Cognitive measures

      On the same day as the MRI scan, we measured intelligence using an age-appropriate version of an IQ test (Wechsler Intelligence Scale for Children (WISC)-III for participants whose age was under 16 years old, Wechsler Adult Intelligence Scale (WAIS)-III for participants whose age was over 16 years). Trained examiners administered these tests. Three IQ scores: FSIQ, Verbal IQ (VIQ), and Performance IQ (PIQ), were computed according to the standard formulae. See Table 1 for a summary of these data.
      Table 1Sample characteristics.
      NBoysGirlsp (uncorrected)p (FDR
      Statistical threshold was set at P < 0.05 with false discovery rate correction.
      )
      121132
      Age11.2011.880.1
      Sex difference was tested by Mann-Whitney U test.
      0.2
      SD2.763.29
      FSIQ104.00100.770.031
      Sex difference was tested by T test.
      0.124
      SD12.8110.94
      VIQ105.67102.050.031
      Sex difference was tested by T test.
      0.124
      SD13.3713.12
      PIQ101.2898.860.04
      Sex difference was tested by Mann-Whitney U test.
      0.053
      SD13.3010.57
      FDR: false discovery rate, FSIQ: full-scale IQ, PIQ: performance IQ, SD: standard deviation, VIQ: verbal IQ.
      1 Statistical threshold was set at P < 0.05 with false discovery rate correction.
      a Sex difference was tested by Mann-Whitney U test.
      b Sex difference was tested by T test.

      2.3 Image acquisition

      MRI data acquisition was conducted using a 3T Philips Achieva scanner (Royal Philips, Amsterdam, Nederland). Using a spin-echo echo planer imaging (EPI) sequence (repetition time = 10,293 ms, echo time = 55 ms, big delta (Δ) = 26.3 ms, little delta (δ) = 12.2 ms, field of view = 22.4 cm, 2 × 2 × 2 mm3 voxels, 60 slices, SENSE reduction factor = 2, number of acquisitions = 1), diffusion-weighted data were collected. The diffusion weighting was isotropically distributed along 32 directions (b value = 1000 s/mm2). Additionally, a single image with no diffusion weighting (b value = 0 s/mm2; b0 image) was acquired. The total scan time was 7 min 17 s. Acquisitions for phase correction and signal stabilization were not used as reconstructed images. FA maps were calculated from the collected images using a commercially available diffusion tensor analysis package on the MR console as has been described previously [
      • Takeuchi H.
      • Taki Y.
      • Thyreau B.
      • Sassa Y.
      • Hashizume H.
      • Sekiguchi A.
      • et al.
      White matter structures associated with empathizing and systemizing in young adults.
      ,
      • Takeuchi H.
      • Taki Y.
      • Sassa Y.
      • Hashizume H.
      • Sekiguchi A.
      • Fukushima A.i.
      • et al.
      Verbal working memory performance correlates with regional white matter structures in the frontoparietal regions.
      ,
      • Takeuchi H.
      • Taki Y.
      • Sassa Y.
      • Hashizume H.
      • Sekiguchi A.
      • Fukushima A.i.
      • et al.
      White matter structures associated with creativity: evidence from diffusion tensor imaging.
      ,
      • Takeuchi H.
      • Sekiguchi A.
      • Taki Y.
      • Yokoyama S.
      • Yomogida Y.
      • Komuro N.
      • et al.
      Training of working memory impacts structural connectivity.
      ]. These procedures involved correction for motion and distortion caused by eddy currents. Calculations were performed according to a previously proposed method [
      • Le Bihan D.
      • Mangin J.-F.
      • Poupon C.
      • Clark C.A.
      • Pappata S.
      • Molko N.
      • et al.
      Diffusion tensor imaging: concepts and applications.
      ].
      Three-dimensional (3D) high-resolution T1-weighted images (T1WI) were also collected using a magnetization-prepared rapid gradient-echo (MPRAGE) sequence. The parameters are as follows: 240 × 240 matrix, repetition time = 6.5 ms, echo time = 3 ms, inversion time = 711 ms, field of view = 24 cm, 162 slices, 1.0 mm slice thickness, and scan duration of 8 min and 3 s. Although we did not collect any clinical brain MRI sequences due to scan time constraints for young children, a radiologist (YT) had checked potential brain lesions using T1WIs.

      2.4 Image preprocessing

      Preprocessing and analysis of MRI data were performed using statistical parametric mapping 8 (SPM8; Wellcome Department of Cognitive Neurology, London, UK) implemented in Matlab (Mathworks Inc., Natick, MA, USA). The skull of the unsmoothed b = 0 images of all the subjects in this study was stripped by masking the images using the threshold of a given signal intensity from spatially smoothed (using 8 mm Full Width at Half Maxim; FWHM) b = 0 images of each participant. Then, using the coregister option, this skull-stripped unsmoothed b = 0 image was coregistered to a skull-stripped b = 0 image template that was created previously [
      • Takeuchi H.
      • Taki Y.
      • Sassa Y.
      • Hashizume H.
      • Sekiguchi A.
      • Fukushima A.i.
      • et al.
      White matter structures associated with creativity: evidence from diffusion tensor imaging.
      ]. Using this parameter, other diffusion imaging data were aligned to the template, too.
      Subsequently, using a previously validated two-step new segmentation algorithm of diffusion images and the previously validated diffeomorphic anatomical registration through exponentiated lie algebra (DARTEL)-based registration process [
      • Takeuchi H.
      • Taki Y.
      • Thyreau B.
      • Sassa Y.
      • Hashizume H.
      • Sekiguchi A.
      • et al.
      White matter structures associated with empathizing and systemizing in young adults.
      ], all images, including gray matter segments [regional gray matter density (rGMD) map], white matter segments [regional white matter density (rWMD) map], and cerebrospinal fluid (CSF) segments [regional CSF density (rCSFD) map] of diffusion images, were normalized. The voxel size of these normalized images was 1.5 × 1.5 × 1.5 mm3.
      Next, from the average image of the normalized white matter segmentation (rWMD) images of all the subjects, we created a mask image consisting of voxels with a white matter signal intensity > 0.99. We then applied this mask image to the normalized FA image, therefore retaining only areas that were highly likely to contain white matter from the normalized FA images. These images were smoothed (6 mm FWHM) and carried through to second-level analyses of FA. The description in this subsection has been mostly reproduced from our previous study using an identical methodology [
      • Takeuchi H.
      • Taki Y.
      • Hashizume H.
      • Asano K.
      • Asano M.
      • Sassa Y.
      • et al.
      Impact of reading habit on white matter structure: Cross-sectional and longitudinal analyses.
      ]. By taking FA signal variabilities within the white matter areas into account in the DARTEL procedures, the misalignment of the tracts was prevented using stringent masking. Signal contamination from other tissues was prevented thereby effectively solving or alleviating the major problems of voxel based analyses of FA images [
      • Smith S.M.
      • Jenkinson M.
      • Johansen-Berg H.
      • Rueckert D.
      • Nichols T.E.
      • Mackay C.E.
      • et al.
      Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data.
      ,
      • Vos S.B.
      • Jones D.K.
      • Viergever M.A.
      • Leemans A.
      Partial volume effect as a hidden covariate in DTI analyses.
      ,
      • Westerhausen R.
      • Kompus K.
      • Dramsdahl M.
      • Falkenberg L.E.
      • Grüner R.
      • Hjelmervik H.
      • et al.
      A critical re-examination of sexual dimorphism in the corpus callosum microstructure.
      ].
      T1WIs were used to calculate total intracranial volume. Using the new segmentation algorithm implemented in SPM8, T1WIs of each individual were segmented into six tissues. Then we summed segmented gray matter, white matter, and cerebral spinal fluid volume as total intracranial volume which was treated as covariate in following analyses.

      2.5 Data analysis

      2.5.1 Behavioral data analyses

      Behavioral data analyses were performed using statistical analysis software package, SPSS version 20 (IBM, Japan). We examined sex differences across age, FSIQ, VIQ and PIQ. Because age and PIQ displayed non-parametric distributions, we tested for sex differences using Mann-Whitney U tests. T-tests were performed for the FSIQ and VIQ as these displayed parametric distributions. The statistical threshold was set at P < 0.05, with false discovery rate (FDR) correction. The effect size for each statistical tests was calculated using Cohen’s d for T-tests and r for Mann-Whitney U tests.

      2.5.2 Brain imaging data analyses

      We conducted three separate whole-brain multiple regression analyses to investigate the association between FA and each composite score of IQ test (FSIQ, VIQ, or PIQ). Sex and age (days after birth) were assigned as covariates. Next, we investigated the interaction between sex and FA and IQ composite scores. To examine the effect, age, and one of three composite scores were modeled so that each covariate had a unique relationship with FA for each sex (using the interaction option in SPM), which enabled investigation of the effects of interactions between sex and each covariate. Due to there is a known association between brain volume and IQ or diffusion parameters [
      • Deary I.J.
      • Penke L.
      • Johnson W.
      The neuroscience of human intelligence differences.
      ,
      • Tamnes C.K.
      • Østby Y.
      • Fjell A.M.
      • Westlye L.T.
      • Due-Tønnessen P.
      • Walhovd K.B.
      Brain maturation in adolescence and young adulthood: regional age-related changes in cortical thickness and white matter volume and microstructure.
      ], total intracranial volume was also assigned as a covariate in all regression analyses.
      A multiple comparison correction was performed using threshold-free cluster enhancement (TFCE) with randomized (5,000 permutations) non-parametric testing using the TFCE toolbox (https://dbm.neuro.uni-jena.de/tfce/). We applied a threshold of family-wise error (FWE) corrected P < 0.05. In order to specify the tracts from the coordinates for significant clusters, we used the International Consortium for Brain Mapping (ICBM) DTI-81 Atlas (https://www.loni.usc.edu/).

      3. Results

      3.1 Sample characteristics

      The sample characteristic of age is illustrated in Fig. 1. The means and standard deviations for age and IQ scores (FSIQ, VIQ, and PIQ) are presented in Table 1. We did not find any significant sex differences in these values (age: U = 7034.5, p (uncorrected) = 0.04, p (FDR) = 0.2, r = 0.1; FSIQ; t (251) = 2.17, p (uncorrected) = 0.031, p (FDR) = 0.124, d = 0.27; VIQ: t (251) = 2.18, p (uncorrected) = 0.031, p (FDR) = 0.124, d = 0.27; PIQ: U = 6794, p (uncorrected) = 0.1, p (FDR) = 0.053, r = 0.13).
      Figure thumbnail gr1
      Fig. 1Distribution of age in years in (a) boys and (b) girls.

      3.2 Brain imaging results

      We found a significant positive correlation between FSIQ and FA in bilateral inferior occipitofrontal fasciculus, genu and splenium of the CC (Table 2, Fig. 2a). Moreover, PIQ was positively correlated with FA in the right SLF, bilateral inferior longitudinal fasciculus (ILF), and splenium of the CC (Table 2, Fig. 2b). We did not find any correlation between VIQ and FA in whole group analyses.
      Table 2Clusters with positive correlations of FA with Full-Scale IQ and Performance IQ over the whole cohort.
      White matter locationvoxelsMNI coordinatesTFCE valuep
      x (mm)y (mm)z(mm)
      FSIQ
      Genu of corpus callosum15424308490.890.018
      Splenium of corpus callosum38−21−5114435.720.033
      Right Inferior occipitofrontal fasciculus3630−688417.350.049
      Left inferior occipitofrontal fasciculus79−38−46−6434.930.033
      PIQ
      Right superior longitudinal fasciculus119338−826553.680.011
      Right Inferior longitudinal fasciculus40332−21−8475.860.023
      Splenium of corpus callosum819−20−5112480.250.022
      Left inferior longitudinal fasciculus78−34−323466.20.025
      376−38−46−8460.750.026
      p: TFCE-corrected p value.
      FSIQ: full-scale IQ, MNI: Montreal Neurological Institute, PIQ: performance IQ, TFCE: Threshold Free Cluster Enhancement.
      Figure thumbnail gr2
      Fig. 2Positive FA correlates of Full-Scale IQ and performance IQ in the whole-group analysis. (a) FA values in the genu and splenium of the corpus callosum and bilateral occipito-frontal fasciculus were positively correlated with Full-Scale IQ. (b) FA values in right superior longitudinal fasciculus, bilateral inferior longitudinal fasciculus, and splenium of the corpus callosum were positively correlated with Performance IQ. The result was obtained using Threshold Free Cluster Enhancement, P < 0.05 based on 5,000 permutations.
      We found a significant interaction between sex and VIQ in the CC (TFCE value = 483.9, TFCE corrected P value = 0.02, cluster size = 105 voxels, Montreal Neurological Institute (MNI) coordinate; x = −9 mm, y = 0 mm, z = 27 mm, Fig. 3). FA in this cluster was positively correlated with VIQ score in boys and negatively correlated in girls. We did not find any significant sex interaction between other composite scores (FSIQ and PIQ) and FA.
      Figure thumbnail gr3
      Fig. 3Location of brain region where a sex interaction was seen between verbal IQ and FA. (a) There was a significant positive correlation in boys and negative correlation in girls in the corpus callosum. This result was obtained using Threshold Free Cluster Enhancement, P < 0.05 based on 5,000 permutations. (b, c) Scatter plots depicting the correlation between verbal IQ score and parameter estimates of the significant cluster in the corpus callosum in boys (b) and girls (c).
      In order to check the possibility that both of the VIQ and FA correlated with the age, we calculated Pearson’s correlation coefficients. We did not find any significant correlations (VIQ and age: r = 0.075, p = 0.23, FA and age: r = 0.007, p = 0.91, Fig. 4).
      Figure thumbnail gr4
      Fig. 4Scatter plots of (a)verbal IQ and age, (b)parameter estimates of significant cluster in corpus callosum and age in whole group.

      4. Discussion

      In the present study, we explored the relationship between intelligence and white matter microstructure in typically developing children, also considering interactions with sex. Our hypothesis that FA in the SLF would correlate with cognitive functions was partially accepted. PIQ score correlated with FA mainly in the right SLF. Additionally, we found that in the whole sample, FSIQ score correlated with FA in the bilateral occipitofrontal fasciculus, genu and splenium of the CC. Moreover, consistent with our hypothesis, we found a significant sex interaction between FA in the CC and VIQ score.
      The findings of a positive correlation between FA in CC and bilateral occipitofrontal fasciculus and FSIQ were largely consistent with previous researches. The CC is the most important structure for inter-hemispheric connectivity [
      • Hofer S.
      • Frahm J.
      Topography of the human corpus callosum revisited—comprehensive fiber tractography using diffusion tensor magnetic resonance imaging.
      ] and the microstructure of the CC is associated with higher order processing, such as visuospatial abilities or language [
      • Fryer S.L.
      • Frank L.R.
      • Spadoni A.D.
      • Theilmann R.J.
      • Nagel B.J.
      • Schweinsburg A.D.
      • et al.
      Microstructural integrity of the corpus callosum linked with neuropsychological performance in adolescents.
      ], and intelligence [
      • Navas-Sánchez F.J.
      • Alemán-Gómez Y.
      • Sánchez-Gonzalez J.
      • Guzmán-De-Villoria J.A.
      • Franco C.
      • Robles O.
      • et al.
      White matter microstructure correlates of mathematical giftedness and intelligence quotient.
      ,
      • Yu C.
      • Li J.
      • Liu Y.
      • Qin W.
      • Li Y.
      • Shu N.
      • et al.
      White matter tract integrity and intelligence in patients with mental retardation and healthy adults.
      ]. The genu and splenium of the CC conduct inter-hemispheric connections with frontal and parietal/occipital regions, respectively [
      • Hofer S.
      • Frahm J.
      Topography of the human corpus callosum revisited—comprehensive fiber tractography using diffusion tensor magnetic resonance imaging.
      ]. The occipitofrontal fasciculus connects inferolateral and dorsolateral frontal cortex and posterior temporal and occipital lobes [
      • Catani M.
      • Howard R.J.
      • Pajevic S.
      • Jones D.K.
      Virtual in vivo interactive dissection of white matter fasciculi in the human brain.
      ]. As we found a positive correlation between these fibers and FSIQ score, it suggests that efficient anatomical inter-hemispheric connections and associations between frontal and occipital regions play a great role in human intelligence. In line with this interpretation, previous studies on brain structure and cognitive function have indicated that gray or white matter metrics in specifically inferior frontal gyrus, ventro- and dorsolateral prefrontal cortex, and parietal regions have been associated with higher intelligence [
      • Lerch J.P.
      • Worsley K.
      • Shaw W.P.
      • Greenstein D.K.
      • Lenroot R.K.
      • Giedd J.
      • et al.
      Mapping anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MRI.
      ]. Furthermore, our results are consistent with the parieto-frontal integration theory of intelligence [
      • Jung R.E.
      • Haier R.J.
      The Parieto-Frontal Integration Theory (P-FIT) of intelligence: converging neuroimaging evidence.
      ]. According to this theory, sensory information is processed by temporal and occipital regions and integrated within parietal regions. The frontal regions such as anterior cingulate cortex and dorsolateral prefrontal regions are associated with higher order processing. Previous studies on cortical thickness [
      • Menary K.
      • Collins P.F.
      • Porter J.N.
      • Muetzel R.
      • Olson E.A.
      • Kumar V.
      • et al.
      Associations between cortical thickness and general intelligence in children, adolescents and young adults.
      ,
      • Karama S.
      • Ad-Dab'bagh Y.
      • Haier R.
      • Deary I.
      • Lyttelton O.
      • Lepage C.
      • et al.
      Positive association between cognitive ability and cortical thickness in a representative US sample of healthy 6 to 18 year-olds.
      ] or brain structural and functional correlation [
      • Lu L.H.
      • Dapretto M.
      • O'Hare E.D.
      • Kan E.
      • McCourt S.T.
      • Thompson P.M.
      • et al.
      Relationships between brain activation and brain structure in normally developing children.
      ] support this theory.
      Additionally, previous studies that investigated the association of brain and cognitive development using multiple imaging modalities, including cortical thickness, white matter volume and microstructure, have shown that cortical thickness and mean diffusivity (MD) (which reflects average magnitude of water diffusion) showed negative correlations, and FA and white matter volume showed positive correlations with intellectual abilities [
      • Tamnes C.K.
      • Fjell A.M.
      • Østby Y.
      • Westlye L.T.
      • Due-Tønnessen P.
      • Bjørnerud A.
      • et al.
      The brain dynamics of intellectual development: waxing and waning white and gray matter.
      ]. From these previous findings, regional gray matter metrics (volume or cortical thickness) and anatomical connectivity (which is measured by FA) seem to be closely related. In line with these findings, our results indicate that it is not simply regional gray matter volume in posterior and frontal regions that are crucial for intelligence, but also efficient anatomical connections between the two.
      With regard to the positive correlation between FA and PIQ, previous study found that visuospatial ability was associated with global white matter microstructure, but they did not find any significant associations between certain fiber tracts and cognitive abilities [
      • Muetzel R.L.
      • Mous S.E.
      • van der Ende J.
      • Blanken L.M.E.
      • van der Lugt A.
      • Jaddoe V.W.V.
      • et al.
      White matter integrity and cognitive performance in school-age children: A population-based neuroimaging study.
      ]. Extending this result, we found that the white matter properties of certain fibers were associated with cognitive function. In this study, we found a significant positive correlation between PIQ and FA mainly in the right SLF. PIQ reflects visuospatial processing ability, response speed, or visuomotor association [
      • Donders J.
      • Warschausky S.
      WISC-III factor index score patterns after traumatic head injury in children.
      ,
      • Wechsler D.
      WISC-III: Wechsler intelligence scale for children: Manual.
      ]. SLF is a major association fiber pathway that connects postrolandic regions and frontal lobe. The SLF is separated into a dorsal SLF I, middle SLF II, ventral SLF III, and arcuate fascicle [
      • Makris N.
      • Kennedy D.N.
      • McInerney S.
      • Sorensen A.G.
      • Wang R.
      • Caviness V.S.
      • et al.
      Segmentation of subcomponents within the superior longitudinal fascicle in humans: a quantitative, in vivo, DT-MRI study.
      ]. The significant regions, with which we observed a positive correlation with PIQ in this study, mainly overlapped the ventral SLF III. This pathway originates from the supramarginal gyrus and terminates in ventral premotor and prefrontal areas [
      • Makris N.
      • Kennedy D.N.
      • McInerney S.
      • Sorensen A.G.
      • Wang R.
      • Caviness V.S.
      • et al.
      Segmentation of subcomponents within the superior longitudinal fascicle in humans: a quantitative, in vivo, DT-MRI study.
      ]. As for the functional role of SLF, since this pathway connects the rostral part of the inferior parietal lobule with lateral inferior frontal lobe in a bidirectional way [
      • Preuss T.M.
      • Goldman-Rakic P.S.
      Connections of the ventral granular frontal cortex of macaques with perisylvian premotor and somatosensory areas: anatomical evidence for somatic representation in primate frontal association cortex.
      ], this fiber pathway may be critical for visuomotor association or visuospatial attention [
      • de Schotten M.T.
      • Dell'Acqua F.
      • Forkel S.J.
      • Simmons A.
      • Vergani F.
      • Murphy D.G.M.
      • et al.
      A lateralized brain network for visuospatial attention.
      ,
      • Nagy Z.
      • Westerberg H.
      • Klingberg T.
      Maturation of white matter is associated with the development of cognitive functions during childhood.
      ]. Clinical studies in patients with brain lesions suggest that visuospatial attention is a dominant function of the right hemisphere [
      • Mesulam M.-M.
      A cortical network for directed attention and unilateral neglect.
      ]. Therefore, it is quite plausible that the larger FA value in the right ventral SFL was associated with higher PIQ score.
      Our second finding was the interaction between sex and VIQ and FA. We observed a significant positive correlation in boys and a negative correlation in girls between VIQ and FA in the CC. These findings indicate that efficient information transfer between hemispheres plays a critical role in higher cognitive abilities. The CC shows a protracted development pattern that continues into adolescence [
      • Westlye L.T.
      • Walhovd K.B.
      • Dale A.M.
      • Bjornerud A.
      • Due-Tonnessen P.
      • Engvig A.
      • et al.
      Life-span changes of the human brain white matter: diffusion tensor imaging (DTI) and volumetry.
      ]. Previous studies on sexual dimorphism of the CC have revealed that males showed a higher callosal anisotropy than females in anterior and posterior genu, as well as in the truncus region [
      • Westerhausen R.
      • Kompus K.
      • Dramsdahl M.
      • Falkenberg L.E.
      • Grüner R.
      • Hjelmervik H.
      • et al.
      A critical re-examination of sexual dimorphism in the corpus callosum microstructure.
      ,
      • Menzler K.
      • Belke M.
      • Wehrmann E.
      • Krakow K.
      • Lengler U.
      • Jansen A.
      • et al.
      Men and women are different: diffusion tensor imaging reveals sexual dimorphism in the microstructure of the thalamus, corpus callosum and cingulum.
      ,
      • Oh J.S.
      • Song I.C.
      • Lee J.S.
      • Kang H.
      • Park K.S.
      • Kang E.
      • et al.
      Tractography-guided statistics (TGIS) in diffusion tensor imaging for the detection of gender difference of fiber integrity in the midsagittal and parasagittal corpora callosa.
      ]. Due to the larger brain size of male [
      • Lenroot R.K.
      • Giedd J.N.
      Sex differences in the adolescent brain.
      ], the faster and stronger connectivity in males might have seen as the results of an adjustment for the longer distance of inter-hemisphere connectivity [
      • Westerhausen R.
      • Kompus K.
      • Dramsdahl M.
      • Falkenberg L.E.
      • Grüner R.
      • Hjelmervik H.
      • et al.
      A critical re-examination of sexual dimorphism in the corpus callosum microstructure.
      ]. Moreover, the maturation of white matter microstructure is faster in girls than in boys [
      • Schmithorst V.J.
      • Holland S.K.
      • Dardzinski B.J.
      Developmental differences in white matter architecture between boys and girls.
      ,
      • Simmonds D.J.
      • Hallquist M.N.
      • Asato M.
      • Luna B.
      Developmental stages and sex differences of white matter and behavioral development through adolescence: a longitudinal diffusion tensor imaging (DTI) study.
      ]. Boys develop slower than girls in terms of verbal abilities [
      • Hyde J.S.
      • Linn M.C.
      Gender differences in verbal ability: A meta-analysis.
      ], and this could go some way to explaining the positive correlation with FA of CC in boys.
      There are some limitations in this study. First, inconsistent with previous study on sex differences [
      • Wang Y.
      • Adamson C.
      • Yuan W.
      • Altaye M.
      • Rajagopal A.
      • Byars A.W.
      • et al.
      Sex differences in white matter development during adolescence: a DTI study.
      ], we did not find any positive correlation between FA and IQ indices in girls, which may be attributable to differences in the subjects’ age range, sample size or methods of the DTI analysis. The second limitation regards the DTI technique itself. Since the specific neurobiological meaning of DTI parameters is unclear, differences in FA are mostly thought to be the result of differences in fiber organization, but they could also be related to myelination, fiber density, axonal diameter, and ratio of intracellular/extracellular space. Multiple imaging modalities combined could help to delineate the association between brain development and cognitive abilities. Third, the present study was cross-sectional. To capture development of brain structure and cognitive ability, longitudinal data are needed to validate the results. Fourth, we did not consider the positive (such as studying, reading, or exercising) and negative (maltreatment from parents, such as neglect or verbal abuse) factors associated with brain development. Finally, due to the DTI imaging acquisition protocol, we only collected a single image with no diffusion weighting. Therefore, the EPI distortion artifact may not be completely corrected.
      In conclusion, we found that white matter microstructure in (1) CC and bilateral occipitofrontal fasciculus and FSIQ score, and (2) the right SLF and PIQ score were positively correlated in typically developing children. These results suggest that efficient anatomical connectivity between parietal and frontal regions is crucial for children’s intelligence. Moreover, FA in the left CC and VIQ were positively correlated in boys, suggesting that efficient inter-hemispheric connections play a role for verbal abilities in boys. The present results should be validated using longitudinal data.

      Acknowledgements

      We would like to thank Y. Yamada, Y. Kotozaki, and R. Nouchi for operating the MRI scanner and for administering the psychological tests.

      Conflicts of Interest Disclosures

      The authors declare no competing interests.

      Funding

      This study was funded by JSPS KAKENHI Grant Number JP15K17418 and JP18K13221 .

      References

        • Shaw P.
        • Greenstein D.
        • Lerch J.
        • Clasen L.
        • Lenroot R.
        • Gogtay N.
        • et al.
        Intellectual ability and cortical development in children and adolescents.
        Nature. 2006; 440: 676-679
        • Wilke M.
        • Sohn J.-H.
        • Byars A.W.
        • Holland S.K.
        Bright spots: correlations of gray matter volume with IQ in a normal pediatric population.
        Neuroimage. 2003; 20: 202-215
        • Lebel C.
        • Deoni S.
        The development of brain white matter microstructure.
        Neuroimage. 2018; 182: 207-218
        • Lebel C.
        • Treit S.
        • Beaulieu C.
        A review of diffusion MRI of typical white matter development from early childhood to young adulthood.
        NMR Biomed. 2019; 32
        • Courchesne E.
        • Chisum H.J.
        • Townsend J.
        • Cowles A.
        • Covington J.
        • Egaas B.
        • et al.
        Normal brain development and aging: quantitative analysis at in vivo MR imaging in healthy volunteers 1.
        Radiology. 2000; 216: 672-682
        • Morriss M.C.
        • Zimmerman R.A.
        • Bilaniuk L.T.
        • Hunter J.V.
        • Haselgrove J.C.
        Changes in brain water diffusion during childhood.
        Neuroradiology. 1999; 41: 929-934
        • Mukherjee P.
        • Miller J.H.
        • Shimony J.S.
        • Conturo T.E.
        • Lee B.C.P.
        • Almli C.R.
        • et al.
        Normal brain maturation during childhood: developmental trends characterized with diffusion-tensor MR Imaging 1.
        Radiology. 2001; 221: 349-358
        • Mukherjee P.
        • Miller J.H.
        • Shimony J.S.
        • Philip J.V.
        • Nehra D.
        • Snyder A.Z.
        • et al.
        Diffusion-tensor MR imaging of gray and white matter development during normal human brain maturation.
        Am J Neuroradiol. 2002; 23: 1445-1456
        • Lebel C.
        • Walker L.
        • Leemans A.
        • Phillips L.
        • Beaulieu C.
        Microstructural maturation of the human brain from childhood to adulthood.
        Neuroimage. 2008; 40: 1044-1055
        • Lebel C.
        • Gee M.
        • Camicioli R.
        • Wieler M.
        • Martin W.
        • Beaulieu C.
        Diffusion tensor imaging of white matter tract evolution over the lifespan.
        Neuroimage. 2012; 60: 340-352
        • Westlye L.T.
        • Walhovd K.B.
        • Dale A.M.
        • Bjornerud A.
        • Due-Tonnessen P.
        • Engvig A.
        • et al.
        Life-span changes of the human brain white matter: diffusion tensor imaging (DTI) and volumetry.
        Cereb Cortex. 2010; 20: 2055-2068
        • Wang Y.
        • Adamson C.
        • Yuan W.
        • Altaye M.
        • Rajagopal A.
        • Byars A.W.
        • et al.
        Sex differences in white matter development during adolescence: a DTI study.
        Brain Res. 2012; 1478: 1-15
        • Menary K.
        • Collins P.F.
        • Porter J.N.
        • Muetzel R.
        • Olson E.A.
        • Kumar V.
        • et al.
        Associations between cortical thickness and general intelligence in children, adolescents and young adults.
        Intelligence. 2013; 41: 597-606
        • Pangelinan M.M.
        • Zhang G.
        • VanMeter J.W.
        • Clark J.E.
        • Hatfield B.D.
        • Haufler A.J.
        Beyond age and gender: relationships between cortical and subcortical brain volume and cognitive-motor abilities in school-age children.
        Neuroimage. 2011; 54: 3093-3100
        • Burgaleta M.
        • Johnson W.
        • Waber D.P.
        • Colom R.
        • Karama S.
        Cognitive ability changes and dynamics of cortical thickness development in healthy children and adolescents.
        Neuroimage. 2014; 84: 810-819
        • Yokota S.
        • Takeuchi H.
        • Hashimoto T.
        • Hashizume H.
        • Asano K.
        • Asano M.
        • et al.
        Individual differences in cognitive performance and brain structure in typically developing children.
        Dev Cogn Neurosci. 2015; 14: 1-7
        • Schmithorst V.J.
        • Wilke M.
        • Dardzinski B.J.
        • Holland S.K.
        Cognitive functions correlate with white matter architecture in a normal pediatric population: a diffusion tensor MRI study.
        Hum Brain Mapp. 2005; 26: 139-147
        • Muetzel R.L.
        • Mous S.E.
        • van der Ende J.
        • Blanken L.M.E.
        • van der Lugt A.
        • Jaddoe V.W.V.
        • et al.
        White matter integrity and cognitive performance in school-age children: A population-based neuroimaging study.
        Neuroimage. 2015; 119: 119-128
        • Chiang M.-C.
        • McMahon K.L.
        • de Zubicaray G.I.
        • Martin N.G.
        • Hickie I.
        • Toga A.W.
        • et al.
        Genetics of white matter development: a DTI study of 705 twins and their siblings aged 12 to 29.
        Neuroimage. 2011; 54: 2308-2317
        • Jung R.E.
        • Haier R.J.
        The Parieto-Frontal Integration Theory (P-FIT) of intelligence: converging neuroimaging evidence.
        Behav Brain Sci. 2007; 30: 135-154
        • Taki Y.
        • Hashizume H.
        • Sassa Y.
        • Takeuchi H.
        • Asano M.
        • Asano K.
        • et al.
        Breakfast staple types affect brain gray matter volume and cognitive function in healthy children.
        PLoS ONE. 2010; 5
        • Oldfield R.C.
        The assessment and analysis of handedness: the Edinburgh inventory.
        Neuropsychologia. 1971; 9: 97-113
        • Takeuchi H.
        • Taki Y.
        • Thyreau B.
        • Sassa Y.
        • Hashizume H.
        • Sekiguchi A.
        • et al.
        White matter structures associated with empathizing and systemizing in young adults.
        Neuroimage. 2013; 77: 222-236
        • Takeuchi H.
        • Taki Y.
        • Sassa Y.
        • Hashizume H.
        • Sekiguchi A.
        • Fukushima A.i.
        • et al.
        Verbal working memory performance correlates with regional white matter structures in the frontoparietal regions.
        Neuropsychologia. 2011; 49: 3466-3473
        • Takeuchi H.
        • Taki Y.
        • Sassa Y.
        • Hashizume H.
        • Sekiguchi A.
        • Fukushima A.i.
        • et al.
        White matter structures associated with creativity: evidence from diffusion tensor imaging.
        Neuroimage. 2010; 51: 11-18
        • Takeuchi H.
        • Sekiguchi A.
        • Taki Y.
        • Yokoyama S.
        • Yomogida Y.
        • Komuro N.
        • et al.
        Training of working memory impacts structural connectivity.
        J Neurosci. 2010; 30: 3297-3303
        • Le Bihan D.
        • Mangin J.-F.
        • Poupon C.
        • Clark C.A.
        • Pappata S.
        • Molko N.
        • et al.
        Diffusion tensor imaging: concepts and applications.
        J Magn Reson Imaging. 2001; 13: 534-546
        • Takeuchi H.
        • Taki Y.
        • Hashizume H.
        • Asano K.
        • Asano M.
        • Sassa Y.
        • et al.
        Impact of reading habit on white matter structure: Cross-sectional and longitudinal analyses.
        Neuroimage. 2016; 133: 378-389
        • Smith S.M.
        • Jenkinson M.
        • Johansen-Berg H.
        • Rueckert D.
        • Nichols T.E.
        • Mackay C.E.
        • et al.
        Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data.
        Neuroimage. 2006; 31: 1487-1505
        • Vos S.B.
        • Jones D.K.
        • Viergever M.A.
        • Leemans A.
        Partial volume effect as a hidden covariate in DTI analyses.
        Neuroimage. 2011; 55: 1566-1576
        • Westerhausen R.
        • Kompus K.
        • Dramsdahl M.
        • Falkenberg L.E.
        • Grüner R.
        • Hjelmervik H.
        • et al.
        A critical re-examination of sexual dimorphism in the corpus callosum microstructure.
        Neuroimage. 2011; 56: 874-880
        • Deary I.J.
        • Penke L.
        • Johnson W.
        The neuroscience of human intelligence differences.
        Nat Rev Neurosci. 2010; 11: 201-211
        • Tamnes C.K.
        • Østby Y.
        • Fjell A.M.
        • Westlye L.T.
        • Due-Tønnessen P.
        • Walhovd K.B.
        Brain maturation in adolescence and young adulthood: regional age-related changes in cortical thickness and white matter volume and microstructure.
        Cereb Cortex. 2010; 20: 534-548
        • Hofer S.
        • Frahm J.
        Topography of the human corpus callosum revisited—comprehensive fiber tractography using diffusion tensor magnetic resonance imaging.
        Neuroimage. 2006; 32: 989-994
        • Fryer S.L.
        • Frank L.R.
        • Spadoni A.D.
        • Theilmann R.J.
        • Nagel B.J.
        • Schweinsburg A.D.
        • et al.
        Microstructural integrity of the corpus callosum linked with neuropsychological performance in adolescents.
        Brain Cogn. 2008; 67: 225-233
        • Navas-Sánchez F.J.
        • Alemán-Gómez Y.
        • Sánchez-Gonzalez J.
        • Guzmán-De-Villoria J.A.
        • Franco C.
        • Robles O.
        • et al.
        White matter microstructure correlates of mathematical giftedness and intelligence quotient.
        Hum Brain Mapp. 2014; 35: 2619-2631
        • Yu C.
        • Li J.
        • Liu Y.
        • Qin W.
        • Li Y.
        • Shu N.
        • et al.
        White matter tract integrity and intelligence in patients with mental retardation and healthy adults.
        Neuroimage. 2008; 40: 1533-1541
        • Catani M.
        • Howard R.J.
        • Pajevic S.
        • Jones D.K.
        Virtual in vivo interactive dissection of white matter fasciculi in the human brain.
        Neuroimage. 2002; 17: 77-94
        • Lerch J.P.
        • Worsley K.
        • Shaw W.P.
        • Greenstein D.K.
        • Lenroot R.K.
        • Giedd J.
        • et al.
        Mapping anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MRI.
        Neuroimage. 2006; 31: 993-1003
        • Karama S.
        • Ad-Dab'bagh Y.
        • Haier R.
        • Deary I.
        • Lyttelton O.
        • Lepage C.
        • et al.
        Positive association between cognitive ability and cortical thickness in a representative US sample of healthy 6 to 18 year-olds.
        Intelligence. 2009; 37: 432-442
        • Lu L.H.
        • Dapretto M.
        • O'Hare E.D.
        • Kan E.
        • McCourt S.T.
        • Thompson P.M.
        • et al.
        Relationships between brain activation and brain structure in normally developing children.
        Cereb Cortex. 2009; 19: 2595-2604
        • Tamnes C.K.
        • Fjell A.M.
        • Østby Y.
        • Westlye L.T.
        • Due-Tønnessen P.
        • Bjørnerud A.
        • et al.
        The brain dynamics of intellectual development: waxing and waning white and gray matter.
        Neuropsychologia. 2011; 49: 3605-3611
        • Donders J.
        • Warschausky S.
        WISC-III factor index score patterns after traumatic head injury in children.
        Child Neuropsychol. 1997; 3: 71-78
        • Wechsler D.
        WISC-III: Wechsler intelligence scale for children: Manual.
        Psychological Corporation, San Antonio, TX1991
        • Makris N.
        • Kennedy D.N.
        • McInerney S.
        • Sorensen A.G.
        • Wang R.
        • Caviness V.S.
        • et al.
        Segmentation of subcomponents within the superior longitudinal fascicle in humans: a quantitative, in vivo, DT-MRI study.
        Cereb Cortex. 2005; 15: 854-869
        • Preuss T.M.
        • Goldman-Rakic P.S.
        Connections of the ventral granular frontal cortex of macaques with perisylvian premotor and somatosensory areas: anatomical evidence for somatic representation in primate frontal association cortex.
        J Comp Neurol. 1989; 282: 293-316
        • de Schotten M.T.
        • Dell'Acqua F.
        • Forkel S.J.
        • Simmons A.
        • Vergani F.
        • Murphy D.G.M.
        • et al.
        A lateralized brain network for visuospatial attention.
        Nat Neurosci. 2011; 14: 1245-1246
        • Nagy Z.
        • Westerberg H.
        • Klingberg T.
        Maturation of white matter is associated with the development of cognitive functions during childhood.
        J Cogn Neurosci. 2004; 16: 1227-1233
        • Mesulam M.-M.
        A cortical network for directed attention and unilateral neglect.
        Ann Neurol. 1981; 10: 309-325
        • Menzler K.
        • Belke M.
        • Wehrmann E.
        • Krakow K.
        • Lengler U.
        • Jansen A.
        • et al.
        Men and women are different: diffusion tensor imaging reveals sexual dimorphism in the microstructure of the thalamus, corpus callosum and cingulum.
        Neuroimage. 2011; 54: 2557-2562
        • Oh J.S.
        • Song I.C.
        • Lee J.S.
        • Kang H.
        • Park K.S.
        • Kang E.
        • et al.
        Tractography-guided statistics (TGIS) in diffusion tensor imaging for the detection of gender difference of fiber integrity in the midsagittal and parasagittal corpora callosa.
        Neuroimage. 2007; 36: 606-616
        • Lenroot R.K.
        • Giedd J.N.
        Sex differences in the adolescent brain.
        Brain Cogn. 2010; 72: 46-55
        • Schmithorst V.J.
        • Holland S.K.
        • Dardzinski B.J.
        Developmental differences in white matter architecture between boys and girls.
        Hum Brain Mapp. 2008; 29: 696-710
        • Simmonds D.J.
        • Hallquist M.N.
        • Asato M.
        • Luna B.
        Developmental stages and sex differences of white matter and behavioral development through adolescence: a longitudinal diffusion tensor imaging (DTI) study.
        Neuroimage. 2014; 92: 356-368
        • Hyde J.S.
        • Linn M.C.
        Gender differences in verbal ability: A meta-analysis.
        Psychol Bull. 1988; 104: 53-69