Abstract

There are numerous reports of sexual dimorphism in brain structure in children and adults, but data on sex differences in infancy are extremely limited. Our primary goal was to identify sex differences in neonatal brain structure. Our secondary goal was to explore whether brain structure was related to androgen exposure or sensitivity. Two hundred and ninety-three neonates (149 males) received high-resolution structural magnetic resonance imaging scans. Sensitivity to androgen was measured using the number of cytosine, adenine, guanine (CAG) triplets in the androgen receptor gene and the ratio of the second to fourth digit, provided a proxy measure of prenatal androgen exposure. There was a significant sex difference in intracranial volume of 5.87%, which was not related to CAG triplets or digit ratios. Tensor-based morphometry identified extensive areas of local sexual dimorphism. Males had larger volumes in medial temporal cortex and rolandic operculum, and females had larger volumes in dorsolateral prefrontal, motor, and visual cortices. Androgen exposure and sensitivity had minor sex-specific effects on local gray matter volume, but did not appear to be the primary determinant of sexual dimorphism at this age. Comparing our study with the existing literature suggests that sex differences in cortical structure vary in a complex and highly dynamic way across the human lifespan.

Introduction

Relative risk levels for many psychiatric disorders are dramatically different in males and females (Rutter et al. 2003). Many early onset neurodevelopmental disorders are male-biased, including autism spectrum disorders (Baird et al. 2000; Chakrabarti and Fombonne 2001), attention-deficit hyperactivity disorder (ADHD; Szatmari et al. 1989; Moffitt 1990), early onset persistent antisocial behavior (Moffitt and Caspi 2001), Tourette's syndrome (Wang and Kuo 2003), and early onset schizophrenia (Remschmidt et al. 1994). In contrast, many adolescent onset disorders are female-biased, including depression (Bebbington et al. 1998), anxiety (McLean et al. 2011), and eating disorders (Lucas et al. 1991). It has been hypothesized that the prevalence and expression of these disorders are related to sex differences in brain development.

There is a great deal of evidence for sexual dimorphism in the human central nervous system during both adulthood and childhood, although there is some debate as to whether this is independent of body, head, and total brain size. The best-replicated findings are greater volume of the cerebrum in males (Dekaban and Sadowsky 1978; Caviness et al. 1996; Giedd et al. 1996, 1997; Reiss et al. 1996; Nopoulos et al. 1997; Filipek 1999; De Bellis et al. 2001; Goldstein et al. 2001), relatively greater volume of the amygdala (Caviness et al. 1996; Good et al. 2001), putamen, and pallidum (Rijpkema et al. 2012) in males, and relatively greater volume of the caudate (Filipek et al. 1994; Caviness et al. 1996; Giedd et al. 1996, 1997) and hippocampus (Filipek et al. 1994; Caviness et al. 1996) in females. The largest study of children and adolescents carried out to date reports that only occipital gray matter (GM), putamen, and cerebellum all differed significantly by sex after adjusting for total brain volume; all being relatively larger in males (but note that neither the hippocampus nor the amygdala was included in this report; BDCG 2012). Voxel-based morphometry (VBM) studies also suggest that adult males and females show localized differences in cortical GM volume. Females have been reported to show increased GM volume adjacent to the depths of both central sulci and the left superior temporal sulcus, in right Heschl's gyrus and planum temporale, right inferior frontal and frontomarginal gyri, bilateral cingulate gyrus, precentral gyrus, and right inferior parietal lobule. Males have been reported to show increased GM volume in the bilateral medial temporal lobes (including hippocampus, amygdala, and entorhinal and perirhinal cortices), left inferior temporal gyrus, right middle temporal gyrus, right occipital lingual gyrus, the midbrain and both cerebellar hemispheres (Good et al. 2001; Chen et al. 2007; Lentini et al. 2012). A study of peripubertal children reported some similar findings: females had increased GM in planum temporale/parietal operculum, posterior lateral orbitofrontal cortex, and anterior cingulate, and males had larger volumes in amygdala, putamen, and ventral tegmental area (midbrain). This study also reported sexual dimorphisms that were not present in the adult: females had larger volumes in the medial prefrontal cortex, precuneus, and superior parietal lobule. Males had larger volumes in the hypothalamus, dorsolateral prefrontal cortex, and visual cortex (Lombardo, Ashwin, Auyeung, Chakrabarti, Taylor, et al. 2012).

In contrast to the above, data on sex differences in brain development during infancy are extremely limited, due to the challenges of carrying out and analyzing magnetic resonance imaging (MRI) data in this age group. An earlier study by our group demonstrated significant sex differences in intracranial volume (ICV), cortical GM, and cortical white matter (WM) in a sample of 74 neonates, but did not investigate localized effects on cortical GM due to power limitations (Gilmore et al. 2007). This is a critical gap in our knowledge as the perinatal period is an extremely dynamic stage of brain development, characterized by rapid elaboration of new synapses (Huttenlocher and Dabholkar 1997), exuberant dendritic (Petanjek et al. 2008) and axonal growth (Kasprian et al. 2008), and extensive myelination (Brody et al. 1987). These changes are reflected in dramatic increases in GM and WM volumes as indexed by MRI (Gilmore et al. 2007, 2012; Knickmeyer et al. 2008) and presumably underpin the rapid maturation of motor skills, cognition, social abilities, and language that characterize the first 2 years of life (Kagan and Herschkowitz 2005). This is a critical period for childhood-onset illnesses, such as autism (Hazlett et al. 2005, 2011; Wolff et al. 2012). There is also extensive evidence that adult-onset diseases, such as schizophrenia, originate in early brain development (Rapoport et al. 2005; Fatemi and Folsom 2009). Early aberrations in neurodevelopment, relevant to adult-onset disorders, can be captured via MRI as evidenced by a recent study, showing that male neonates at high genetic risk for schizophrenia have larger intracranial, cerebrospinal fluid (CSF), total GM, and lateral ventricle volumes than controls (Gilmore et al. 2010).

Assuming that sexual dimorphism is present in the neonate brain, the question arises as to what biological mechanisms produce these differences. For many years, sexual differentiation of the brain was considered to be analogous to that of the reproductive tracts, such that in the presence of testicular hormones (in particular testosterone) discrete male neural circuits would develop in an otherwise sexually monomorphic brain, while in the absence of testosterone discrete female neural circuits would develop. However, recent evidence suggests that a more complex and nuanced model is necessary (McCarthy and Arnold 2011). First, in contrast to the reproductive tracts, sexual dimorphism in the brain is generally a difference of degree with a substantial overlap in the distributions of males and females (Breedlove 1994; McCarthy and Arnold 2011). This suggests that, instead of distinct male and female neural circuits, there are shared networks that are differentially weighted toward sex-typical responses (McCarthy and Arnold 2011). Thus, the relative amount of testosterone (rather than the presence or absence of testosterone) exposure may produce an average difference between the sexes and contribute to individual differences within each sex. This model is supported by a recent study, which reported that individual differences in fetal testosterone levels measured in amniotic fluid are associated with sexually dimorphic GM volumes in peripubertal males, including the right temporoparietal junction/posterior temporal sulcus, planum temporale/parietal operculum, and posterior lateral orbitofrontal cortex (Lombardo, Ashwin, Auyeung, Chakrabarti, Taylor, et al. 2012). Secondly, there is a growing recognition that nonhormonal mechanisms such as direct sex-chromosome effects and sex-specific environments play an important role in sexual differentiation of the brain.

The primary goal of the current study was to identify sex differences in brain development in neonates using automated region of interest (ROI) volumetry and tensor-based morphometry (TBM). We chose to use TBM rather than classic VBM as false-positive findings due to systematic group differences in registration errors are less likely (Hua et al. 2008; Lepore et al. 2008). TBM analyses were restricted to GM. Localized WM changes are also of interest, but we felt that these would be best addressed through diffusion tensor imaging. The secondary goal of the current study was to explore whether individual differences in brain volumes within sexually dimorphic areas were related to androgen exposure or sensitivity. A genetic predisposition for high sensitivity to T was measured using the number of cytosine, adenine, guanine (CAG) triplets in the androgen receptor gene, low numbers of CAG triplets are associated with greater receptor sensitivity/efficiency (Chang 2002), and the ratio of the second to fourth digit (2D:4D) was taken as a proxy measure of prenatal testosterone exposure (FT) (Manning et al. 1998). We note that a longitudinal study of 2D:4D ratios at birth, 1 year, and 2 years of age carried out by our group showed a lack of stability across age. Therefore, in this analysis, we only included digit ratios collected in a narrow age band (around 2 weeks of age), an age point which showed significant sex differences in our previous work (Knickmeyer et al. 2011). We also note that our analysis is based on the previously discussed model in which sexual dimorphism arises from differences in the relative amount of testosterone. A study of typically developing children such as this cannot rule out the possibility that sexual differentiation of the neonate brain is determined by the presence or absence of testosterone in a completely binary fashion.

Materials and Methods

Subjects

Two hundred and ninety-three neonates including 149 males and 144 females, and 143 singletons and 150 twins. Mothers were recruited during the second trimester of pregnancy from the outpatient obstetrics and gynecology clinics at UNC hospitals. Exclusion criteria at enrollment were the presence of abnormalities on fetal ultrasound or major medical illness in the mother. Demographic data are found in Table 1. Note that with the exception of maternal ethnicity (METHNIC), males and females did not differ significantly on any of the demographic variables examined. This study was approved by the Institutional Review Board of the University of North Carolina (UNC) School of Medicine. Written informed consent was obtained from the participants' mothers before study procedures were carried out.

Table 1

Demographic data

Variable Male Female 
Gestational age at birth (days)
mean (SD), range 
264.9 (14.78), 224–292 264.7 (15.86), 224–295 
Birthweight (g)
mean (SD), range 
2973 (664.1), 1553–4650 2895 (537.9), 1781–4295 
Gestational age at MRI (days)
mean (SD), range 
292.5 (12.92), 261–324 295.4 (20.09), 263–401 
Maternal age at birth (years)
mean (SD), range 
30.24 (5.68), 17–48 30.47 (5.58), 18–44 
Paternal age at birth (years)
mean (SD), range 
31.24 (5.48), 20–41 31.94 (6.14), 19–47 
Maternal education level (years)
mean (SD), range 
15.32 (3.13), 8–24 15.46 (2.96), 8–24 
Total household income (dollars)
mean (SD), range 
$68 029 ($46 452), 0–$237 000 $72 147 ($52 347), 0–$280 000 
Twin status 
 Singleton no. (%) 72 (48.32) 71 (49.31) 
 Twin no. (%) 77 (51.68) 73 (50.69) 
Maternal ethnicity* 
 White no. (%) 119 (79.87) 107 (74.31) 
 Black or African American no. (%) 21 (14.09) 35 (24.31) 
 Asian no. (%) 8 (5.37) 2 (1.39) 
 American Indian or Alaskan Native no. (%) 1 (0.67) 0 (0) 
Variable Male Female 
Gestational age at birth (days)
mean (SD), range 
264.9 (14.78), 224–292 264.7 (15.86), 224–295 
Birthweight (g)
mean (SD), range 
2973 (664.1), 1553–4650 2895 (537.9), 1781–4295 
Gestational age at MRI (days)
mean (SD), range 
292.5 (12.92), 261–324 295.4 (20.09), 263–401 
Maternal age at birth (years)
mean (SD), range 
30.24 (5.68), 17–48 30.47 (5.58), 18–44 
Paternal age at birth (years)
mean (SD), range 
31.24 (5.48), 20–41 31.94 (6.14), 19–47 
Maternal education level (years)
mean (SD), range 
15.32 (3.13), 8–24 15.46 (2.96), 8–24 
Total household income (dollars)
mean (SD), range 
$68 029 ($46 452), 0–$237 000 $72 147 ($52 347), 0–$280 000 
Twin status 
 Singleton no. (%) 72 (48.32) 71 (49.31) 
 Twin no. (%) 77 (51.68) 73 (50.69) 
Maternal ethnicity* 
 White no. (%) 119 (79.87) 107 (74.31) 
 Black or African American no. (%) 21 (14.09) 35 (24.31) 
 Asian no. (%) 8 (5.37) 2 (1.39) 
 American Indian or Alaskan Native no. (%) 1 (0.67) 0 (0) 

*P = 0.02 (different between males and females).

Image Acquisition

MRI was carried out at the UNC MRI Research Center on a Siemens head-only 3-T scanner (Allegra, Siemens Medical System, Inc., Erlangen, Germany) as previously described (Gilmore et al. 2007). All subjects were studied without sedation. Once a child was asleep, he/she was fitted with earplugs and placed in the MRI scanner with a head in a Vac-Fix immobilization device, and additional foam padding to diminish the sounds of the scanner. Scans were carried out with a neonatal nurse present, and a pulse oximeter was used to monitor heart rate and oxygen saturation. T1-weighted images were obtained using a 3-dimensional (3D) spoiled gradient (FLASH repetition time [TR]/echo time [TE]/flip angle 15/7 ms/25°). Proton density and T2-weighted images were obtained with a turbo spin echo sequence (TSE TR/TE1/TE2/flip angle 6200/20/119 ms/150°). Spatial resolution was 1 × 1 × 1 mm3 voxel size for T1-weighted images and 1.25 × 1.25 × 1.5 mm3 voxel size with a 0.5-mm interslice gap for proton density/T2-weighted images.

Automated Region of Interest Volumetry

Brain tissue was classified as GM, unmyelinated WM, myelinated WM (mWM), and CSF using an automatic, atlas-moderated expectation maximization segmentation tool as previously described (Prastawa et al. 2005; Gilmore et al. 2007). Parcellation of each subject's brain into regions was achieved by nonlinear warping of a parcellation atlas template as previously described (Hazlett et al. 2005; Gilmore et al. 2007; Knickmeyer et al. 2008). Left and right hemispheres were subdivided into 4 regions along the anterior–posterior axis (roughly corresponding to prefrontal, frontal, parietal, and occipital regions). Note that portions of the temporal lobe are included in each section (primarily frontal and parietal). The cerebellum, brainstem, and subcortical structures are represented separately. Note that subcortical structures are combined into a single “exclusion” area; individual subcortical structures cannot be reliably defined at this age. After deformation, the parcellation template is combined with the tissue classification maps and results in estimates of GM, WM, mWM, and CSF for each region. The volume of mWM in the cortex was very small and likely represented partial-volume effects; therefore, we did not perform statistical tests on cortical mWM. The neonatal lateral ventricles are segmented using ITK-SNAP (Insight Toolkit SNAP) a semi-automated 3D segmentation tool, which uses a level-set evolution method (Yushkevich et al. 2006). SNAP is controlled by both a user-defined initialization and by data-specific segmentation protocols with region-growing parameters that operate in conjunction with the probabilistic CSF map generated during tissue segmentation. Analysis was restricted to the following variables: ICV, total GM, total WM, total CSF, lateral ventricle volume, cerebellar volume, and lobar GM and WM (14 volumes total).

Tensor-Based Morphometry: Image Preprocessing

Brain tissue was extracted from the original T2-weighted images and corrected for intensity inhomogeneity using an expectation maximization segmentation algorithm (Prastawa et al. 2005). T2-weighted images were used as these had better signal-to-noise ratio in our neonatal sample. The skull stripped images were then rigidly aligned to match the center and orientation, and the average image was calculated afterwards, serving as the template of the following affine alignment. The rigid and affine registrations were employed with the AFNI software (Cox 1996). Intensity histogram matching was then applied on the affine aligned images to prepare for the nonrigid registration. The unbiased large deformation nonrigid group-wise registration method (Joshi et al. 2004) was used to construct the atlas and estimate deformation fields mapping each input images to the atlas. To get the final transformation from the original image space to the atlas space, we composed the affine transformation matrix and the nonrigid deformation field.

CAG Repeats

DNA was extracted from buccal samples using standard methods as described in the Puregene® DNA Purification Kit (Gentra Systems) using supplies from Qiagen. After extraction, samples were aliquoted into 2 (23 µL) tubes and stored at −80°C. Prior to freezing, DNA quantity and quality (indexed by the 260/280 nm ratio) were assessed by spectrophotometer (Beckman DU640, Beckman-Coulter, Brea, CA, USA). To genotype the CAG repeat polymorphism in the human androgen receptor gene, we adapted a protocol from Allen et al. (1992). First, a polymerase chain reaction (PCR) spanning the CAG repeat polymorphism was performed. The forward primer was 1, 5′-FAM-GCTGTGAAGGTTGCTGTTCCTCAT-3′, and the reverse primer was 2, 5′-TCCAGAATCTGTTCCAGAGCGTGC-3′. The PCR was carried out on a MJ Research PTC-2000 Thermocycler. The PCR product was purified using a Promega Wizard SV 96 PCR clean-up system using the standard protocol. The size of the PCR fragments for each sample were measured using a 3730 DNA Analyzer (Applied Biosystems, Foster City, CA) using the 600LIZ size standard for comparison. Only PCR products with a carboxyfluorescein-labeled end are detected by the analyzer. The data were viewed using the GeneMapper software (Applied Biosystems) to determine the peak fragment length for each sample. Genotypes were called by visual observation of the peak fragment length within the GeneMapper software. Peak fragment lengths were then converted to the CAG repeat number. The overall no-call percentage for genotypes was 2%. Ninety-eight samples were run in duplicate to check for genotyping disagreements. Two samples did not replicate (a rate of 2%), probably due to a pipetting error. Additional genotyping was performed to resolve the genotype in these individuals. In total, CAG repeat length was available for 262 children. For females, who have 2 copies of the androgen receptor gene, mean CAG length was used in subsequent statistical analyses.

2D:4D Ratio

We collected black and white photocopies of both the left and right hands of participating children at the neonatal MRI visit. Measurements of second and fourth digit length were taken from photocopies using vernier calipers with an accuracy of ±0.02 mm and repeatability of 0.01 mm. The 2D:4D ratio was calculated from digit length measured from the basal crease of the digit proximal to the palm to the tip of the digit. Each digit was measured twice with a minimum of 1 day between measurements. A single rater performed all 2D:4D measurements. Intraclass correlation coefficients (Shrout-Fleiss fixed set) for this rater are 0.86 and 0.90 (for left and right hands, respectively). For all subsequent analyses, 2D:4D ratios were calculated using the average of the 2 measurements. The rater was blind to subject's sex. While the rater was not given explicit information as regards subject age or ethnicity, the photocopies themselves provide some limited information relevant to these parameters (e.g. size and shape of the hand, skin tone). Right versus left hand could be determined from the photocopies themselves. The sequence of images was randomized. Left 2D:4D was available for 210 subjects, and right 2D:4D was available for 209 subjects (see Table 2 for descriptive statistics on CAG repeat and digit ratios).

Table 2

Descriptive data for testosterone exposure variables

Variable Male Female P-value 
2D:4D ratio right
mean (SD), range 
0.92 (0.04), 0.84–1.02 0.93 (0.05), 0.82–1.03 0.08 
2D:4D ratio left
mean (SD), range 
0.92 (0.04), 0.80–1.01 0.93 (0.04), 0.85–1.03 0.04 
CAG repeats
mean (SD), range 
19.77 (2.71), 12.00–28.00 19.58 (2.29), 14.50–25.50 0.60 
Variable Male Female P-value 
2D:4D ratio right
mean (SD), range 
0.92 (0.04), 0.84–1.02 0.93 (0.05), 0.82–1.03 0.08 
2D:4D ratio left
mean (SD), range 
0.92 (0.04), 0.80–1.01 0.93 (0.04), 0.85–1.03 0.04 
CAG repeats
mean (SD), range 
19.77 (2.71), 12.00–28.00 19.58 (2.29), 14.50–25.50 0.60 

Data Analysis

For demographic and descriptive data as well as the results of automated ROI volumetry, statistical analyses were performed using the SAS statistical software, version 9.2. Mixed models were used to study the relationship between brain volumes, sex, CAG repeat length, and digit ratios. Mixed models methodology was used to treat twins as repeated measures, whereas singletons had no repeated measures. In other words, twin pairs were treated as a single subject. The compound symmetry covariance structure was used to capture the correlation between twins in a pair (with only 2 twins in a pair there is only 1 off-diagonal correlation; Munoz et al. 1986). All models were adjusted for gestational age at MRI and METHNIC. We used METHNIC as we expected it to be more reliable than paternal ethnicity. When testing for sex differences, we ran analyses both with and without adjusting for ICV. Models testing the effect of CAG repeat and digit ratios were run separately in males and females. All statistical hypothesis tests were 2-tailed. Tests were conducted at a significance level of 0.05. We also ran 2 sensitivity analyses: one with birthweight as a covariate and another including only 1 individual from each MZ pair.

For TBM, the associations between local GM volumes, sex, CAG repeat length, and digit ratios were examined by fitting a multiscale adaptive generalized estimation equation (MAGEE) model to the Jacobian determinant of the deformation matrix at each voxel of the template (note that WM was masked and only GM was examined). The MAGEE method extends the multiscale adaptive regression model (MARM) method (Zhu et al. 2009; Li et al. 2011, 2013) to the correlated sample setting. MAGEE methodology treated the twins as repeated measures, whereas the singletons had no repeated measures. It integrates a commonly used approach for analyzing the correlated data called generalized estimation equation (GEE) with adaptive smoothing methods. GEE models account for within-twin correlation among repeated measures via the specification of a “working” correlation structure in parameter estimation. Specifically, the compound symmetry covariance structure was used to capture the correlation between twins in a pair. An attractive feature of GEE models is that the estimated regression coefficients and their associated standard deviations are valid even if the correlation structure assumed for modeling the within-twin correlation are not precisely correct (Liang and Zeger 1986).

This was a 2-step analysis. In the first step, our primary scientific interest was to localize regions which show a significant difference between males and females. In the second step, the primary interest was to localize regions which were related to CAG repeat length and the digit ratio within sexually dimorphic regions in a congruent direction (male > female sexual dimorphism and FT-positive correlation; female > male sexual dimorphism and FT-negative correlation). These models were run separately in males and females.

The final form in the first step of estimating equations in MAGEE included gender while adjusting for METHNIC, ICV, and gestational age at MRI. The final form in the second step for all male or female subjects included the variable indexing testosterone exposure (left 2D:4D, right 2D:4D, or CAG repeat) while adjusting for METHNIC, ICV, and gestational age at MRI. To clarify the details, we interpret these comparisons in statistics context. For the comparison of males versus females, we will set up a linear model: 

y(d)=β01+β1gender+β2age+β31(METHNIC=White)+β41(METHNIC=Asian)+β51(METHNIC=Black)+β6ICV

y(d) is the imaging measure at the dth voxel and 1(·) is an indicator function for METHNIC. We are interested in the estimation of the coefficient β1 and testing whether β1=0 is statistically significant or not. The value of β1 can be negative or positive across the whole brain. At a specific voxel, if it is positive, it indicates that the imaging measure in females is statistically higher than that in males, denoted as female > male; otherwise, female is statistically lower than male, denoted as female < male. Models for the second step are similar. For example, to examine the influence of CAG repeat, we will set up the linear model: 

y(d)=β01+β1CAGrepeat+β2age+β31(METHNIC=White)+β41(METHNIC=Asian)+β51(METHNIC=Black)+β6ICV

Analyses for the second step were restricted to relevant regions identified in the first step (Fig. 1). In total, the secondary analyses included the following comparisons:

  1. Regions where males > females and negative correlation with CAG repeat in males.

  2. Regions where males > females and negative correlation with CAG repeat in females.

  3. Regions where males > females and negative correlation with left 2D:4D in males.

  4. Regions where males > females and negative correlation with left 2D:4D in females

  5. Regions where males > females and negative correlation with right 2D:4D in males.

  6. Regions where males > females and negative correlation with right 2D:4D in females

  7. Regions where females > males and positive correlation with CAG repeat in males.

  8. Regions where females > males and positive correlation with CAG repeat in females.

  9. Regions where females > males and positive correlation with left 2D:4D repeat in males.

  10. Regions where females > males and positive correlation with left 2D:4D repeat in females.

  11. Regions where females > males and positive correlation with right 2D:4D repeat in males.

  12. Regions where females > males and positive correlation with right 2D:4D repeat in females.

Figure 1.

Sex effects on local GM volume (TBM). Upper images show significant sex differences in local GM volumes projected onto surface-rendered views of the left and right hemispheres, lateral view (top), and medial view (middle). Clusters where females > males are in red, and clusters where males > females are in blue. Bottom images are selected 2-dimensional axial slices with significant clusters displayed on the atlas of the neonate brain. Color bar gives the t-value at each voxel. Red/yellow indicates females > males. Blue/green indicates males > females.

Figure 1.

Sex effects on local GM volume (TBM). Upper images show significant sex differences in local GM volumes projected onto surface-rendered views of the left and right hemispheres, lateral view (top), and medial view (middle). Clusters where females > males are in red, and clusters where males > females are in blue. Bottom images are selected 2-dimensional axial slices with significant clusters displayed on the atlas of the neonate brain. Color bar gives the t-value at each voxel. Red/yellow indicates females > males. Blue/green indicates males > females.

For all hypothesis tests, cluster-based thresholding was used to identify extended areas of signal (Cao 1999; Worsley et al. 1999; Hayasaka et al. 2004). First, we detected clusters of contiguous suprathreshold voxels at a significance level of 0.025 with cluster size threshold at K = 100. We then calculated a P-value for each cluster on the basis of its size/mass to test whether a ROI size is significant or not at the level of 0.001. Cluster-based inference is based on random field theory, a widely used multiple testing method for determining corrected significances while accounting for the high level of spatial dependencies between adjacent voxels (Worsley et al. 1996). The manual and the code of the nonstationarity correction toolbox for cluster size P-values are freely available at http://www.fmri.wfubmc.edu/. We also ran 2 sensitivity analyses, one with birthweight as a covariate and another including only 1 individual from each MZ pair.

Results

Sex Differences

Automated Region of Interest Volumetry

With the exception of the lateral ventricles, all absolute brain volumes were significantly greater in males than in females (Table 3). The same pattern of results was seen when including only 1 individual from each MZ twin pair and when including birthweight as a covariate, with the exception that the cerebellum was not significantly larger in males when adjusting for birthweight. There were no significant sex differences in brain volumes when adjusting for ICV (Table 4). This pattern held when including only 1 individual from each MZ twin pair and when including birthweight as a covariate.

Table 3

Sex differences in absolute brain volume (ROI volumetry)

Dependent variable Male LS
mean (SE) 
Female LS
mean (SE) 
Percent difference (%) P-value 
Intracranial volume 493637 (5568) 466274 (5787) 5.87 <0.0001 
Total GM 250831 (2727) 238119 (2836) 5.32 <0.0001 
Total WM 170507 (2070) 161085 (2155) 5.85 <0.0001 
Total CSF 61777 (1431) 56998 (1496) 8.38 0.0004 
Cerebellar volume 25037 (407) 24288 (425) 3.08 0.0467 
Prefrontal GM 29365 (592) 27621 (618) 6.31 0.0016 
Prefrontal WM 26516 (466) 24725 (486) 7.24 <0.0001 
Frontal GM 44900 (596) 42647 (623) 5.29 <0.0001 
Frontal WM 40387 (559) 38409 (585) 5.51 0.0002 
Parietal GM 61894 (839) 58985 (876) 4.93 0.0002 
Parietal WM 50401 (728) 48044 (761) 4.91 0.0005 
Occipital GM 72350 (980) 68701 (1021) 5.31 <0.0001 
Occipital WM 44301 (642) 41513 (670) 6.72 <0.0001 
Ventricle volume 4442 (289) 3926 (337) 13.14 0.1069 
Dependent variable Male LS
mean (SE) 
Female LS
mean (SE) 
Percent difference (%) P-value 
Intracranial volume 493637 (5568) 466274 (5787) 5.87 <0.0001 
Total GM 250831 (2727) 238119 (2836) 5.32 <0.0001 
Total WM 170507 (2070) 161085 (2155) 5.85 <0.0001 
Total CSF 61777 (1431) 56998 (1496) 8.38 0.0004 
Cerebellar volume 25037 (407) 24288 (425) 3.08 0.0467 
Prefrontal GM 29365 (592) 27621 (618) 6.31 0.0016 
Prefrontal WM 26516 (466) 24725 (486) 7.24 <0.0001 
Frontal GM 44900 (596) 42647 (623) 5.29 <0.0001 
Frontal WM 40387 (559) 38409 (585) 5.51 0.0002 
Parietal GM 61894 (839) 58985 (876) 4.93 0.0002 
Parietal WM 50401 (728) 48044 (761) 4.91 0.0005 
Occipital GM 72350 (980) 68701 (1021) 5.31 <0.0001 
Occipital WM 44301 (642) 41513 (670) 6.72 <0.0001 
Ventricle volume 4442 (289) 3926 (337) 13.14 0.1069 

Note: Brain volumes are in mm3, for all variables males had larger absolute volumes than females.

Table 4

Sex differences in brain volumes adjusted for ICV (ROI volumetry)

Dependent variable Male LS
mean (SE) 
Female LS
mean (SE) 
Percent difference (%) P-value 
Total GM 242685 (929) 242506 (959.30) 0.07 0.84 
Total WM 164583 (889) 164390 (914.80) 0.12 0.82 
Total CSF 58909 (1050) 58806 (1087.41) 0.18 0.92 
Cerebellar volume 24521 (374) 24583 (388.15) 0.25 (F > M) 0.86 
Prefrontal GM 28357 (513) 28150 (528.43) 0.74 0.68 
Prefrontal WM 25727 (393) 25192 (405.70) 2.12 0.15 
Frontal GM 43632 (418) 43373 (434.16) 0.60 0.52 
Frontal WM 38929 (324) 39214 (334.09) 0.73 (F > M) 0.37 
Parietal GM 59822 (502) 60124 (518.32) 0.50 (F > M) 0.53 
Parietal WM 48680 (463) 49028 (479.75) 0.71 (F > M) 0.44 
Occipital GM 70041 (691) 69884 (712.85) 0.22 0.81 
Occipital WM 42767 (462) 42278 (477.94) 1.16 0.28 
Ventricle Volume 4277 (284) 4088 (330.04) 4.62 0.56 
Dependent variable Male LS
mean (SE) 
Female LS
mean (SE) 
Percent difference (%) P-value 
Total GM 242685 (929) 242506 (959.30) 0.07 0.84 
Total WM 164583 (889) 164390 (914.80) 0.12 0.82 
Total CSF 58909 (1050) 58806 (1087.41) 0.18 0.92 
Cerebellar volume 24521 (374) 24583 (388.15) 0.25 (F > M) 0.86 
Prefrontal GM 28357 (513) 28150 (528.43) 0.74 0.68 
Prefrontal WM 25727 (393) 25192 (405.70) 2.12 0.15 
Frontal GM 43632 (418) 43373 (434.16) 0.60 0.52 
Frontal WM 38929 (324) 39214 (334.09) 0.73 (F > M) 0.37 
Parietal GM 59822 (502) 60124 (518.32) 0.50 (F > M) 0.53 
Parietal WM 48680 (463) 49028 (479.75) 0.71 (F > M) 0.44 
Occipital GM 70041 (691) 69884 (712.85) 0.22 0.81 
Occipital WM 42767 (462) 42278 (477.94) 1.16 0.28 
Ventricle Volume 4277 (284) 4088 (330.04) 4.62 0.56 

Tensor-Based Morphometry

Adjusting for ICV, males had increased GM volumes in the bilateral medial temporal cortex (including fusiform gyrus, parahippocampus, and hippocampus), superior, middle, and inferior temporal gyri (primarily anterior), rolandic operculum, Heschl's gyrus, and insular cortex, as well as the left supplementary motor area, left medial frontal gyrus (superior), left superior frontal gyrus, left precentral and postcentral gyri, left temporal pole, right putamen and pallidum, and right frontal inferior trigonal. Females had increased GM volumes in bilateral superior and middle frontal gyri, precentral gyri, occipital cortex, inferior and middle temporal gyri (posterior), paracentral lobules, supplementary motor area (posterior), anterior cingulum, and medial frontal gyri (superior), as well as the right frontal inferior trigonal, and left superior temporal gyrus (posterior; see Table 5 for details). The pattern of results was highly similar when including birthweight as a covariate and when including only 1 individual from each MZ pair. We do note that the female > male medial frontal cluster, including anterior cingulate, did not meet our significance criterion of P < 0.001 in these sensitivity analyses (P = 0.0025 and 0.0046, respectively). In addition, the male > female clusters that included right putamen and supplementary motor cortex did not meet our significance criterion when including only 1 individual from each MZ pair (P = 0.0015 and 0.0020, respectively), probably due to the lower power of this analysis (Supplementary Table 1).

Table 5

Sex differences in local GM volumes (TBM)

 Cluster size Maximum t-value Maximum P-value Cluster P-value Anatomical regions 
Male > female 2995 5.71 <0.0001 <0.0001 Fusiform_L (1119) Temporal_Mid_L (948) Temporal_Inf_L (712) Parahippocampal_L (108) Temporal_Sup_L (94) Occipital_Inf_L (12) Insula_L (1) Supramarginal_L (1) 
2562 4.41 <0.0001 <0.0001 Fusiform_R (918) Temporal_Inf_R (829) Temporal_Mid_R (669) Parahippocampal_R (101) Temporal_Sup_R (44) Hippocampus_R (1) 
812 4.11 <0.0001 0.0001 Rolandic_Oper_R (398) Temporal_Sup_R (198) Insula_R (188) Heschl_R (28) 
808 4.22 <0.0001 <0.0001 Supp_Motor_Area_L (520) Frontal_Sup_Medial_L (124) Frontal_Sup_L (114) Frontal_Mid_L (29) Supp_Motor_Area_R (21) 
644 3.44 0.0003 0.0007 Rolandic_Oper_L (298) Heschl_L (170) Insula_L (92) Temporal_Sup_L (84) 
336 3.62 0.0002 0.0004 Postcentral_L (135) Precentral_L (128) Parietal_Sup_R (72) Frontal_Sup_L (1) 
248 3.84 0.0001 0.0002 Temporal_Pole_Sup_L (158) Temporal_Pole_Mid_L (90) 
221 3.76 0.0001 0.0002 Putamen_R (133) Pallidum_R (88) 
125 3.55 0.0002 0.0004 Frontal_Inf_Tri_R (113) Insula_R (8) Frontal_Infer_Oper_R (4) 
125 4.13 <0.0001 0.0001 Temporal_Mid_R (93) Temporal_Inf_R (26) Temporal_Sup_R (6) 
101 3.71 0.0001 0.0003 Temporal_Mid_L (55) Temporal_Inf_L (46) 
Female > male 2407 5.03 <0.0001 <0.0001 Frontal_Mid_L (1451) Precentral_L (796) Frontal_Sup_L (100) Postcentral_L (26) Frontal_Inf_Tri_L (18) Frontal_Inf_Oper_L (16) 
1285 3.99 <0.0001 0.0001 Occipital_Mid_L (567) Temporal_Mid_L (293) Calcarine_L (181) Occipital_Sup_L (131) Occipital_Inf_L (77) Temporal_Inf_L (26) Lingual_L (6) Fusiform_L (3) Angular_L (1) 
1081 3.69 0.0001 0.0003 Occipital_Mid_R (563) Temporal_Mid_R (217) Occipital_Sup_R (122) Calcarine_R (122) Cuneus_R (57) 
873 3.45 0.0003 0.0006 Frontal_Mid_R (344) Precentral_R (304) Frontal_Sup_R (142) Frontal_Inf_Oper_R (74) Frontal_Sup_Medial_R (9) 
790 4.56 <0.0001 <0.0001 Frontal_Inf_Tri_R (600) Frontal_Mid_R (190) 
633 4.47 <0.0001 <0.0001 Temporal_Mid_R (413) Temporal_Inf_R (220) 
596 4.35 <0.0001 <0.0001 Supp_Motor_Area_R (319) Supp_Motor_Area_L (146) Paracentral_Lobule_L (106) Paracentral_Lobule_R (25) 
401 3.34 0.0005 0.0009 Cingulum_Ant_R (149) Frontal_Sup_Medial_R (99) Frontal_Sup_Medial_L (92) Frontal_Sup_R (35) Cingulum_Ant_L (22) Frontal_Mid_R (3) Frontal_Sup_L (1) 
335 3.82 0.0001 0.0002 Frontal_Mid_R (177) Frontal_Sup_R (151) Frontal_Sup_Medial_R (7) 
308 3.47 0.0003 0.0006 Frontal_Mid_L (260) Frontal_Inf_Tri_L (48) 
253 3.65 0.0002 0.0003 Temporal_Sup_L (151) Temporal_Mid_L (102) 
225 3.51 0.0003 0.0005 Temporal_Mid_R (156) Angular_R (60) Temporal_Sup_R (9) 
109 3.84 0.0001 0.0002 Cuneus_L (57) Occipital_Sup_L (52) 
 Cluster size Maximum t-value Maximum P-value Cluster P-value Anatomical regions 
Male > female 2995 5.71 <0.0001 <0.0001 Fusiform_L (1119) Temporal_Mid_L (948) Temporal_Inf_L (712) Parahippocampal_L (108) Temporal_Sup_L (94) Occipital_Inf_L (12) Insula_L (1) Supramarginal_L (1) 
2562 4.41 <0.0001 <0.0001 Fusiform_R (918) Temporal_Inf_R (829) Temporal_Mid_R (669) Parahippocampal_R (101) Temporal_Sup_R (44) Hippocampus_R (1) 
812 4.11 <0.0001 0.0001 Rolandic_Oper_R (398) Temporal_Sup_R (198) Insula_R (188) Heschl_R (28) 
808 4.22 <0.0001 <0.0001 Supp_Motor_Area_L (520) Frontal_Sup_Medial_L (124) Frontal_Sup_L (114) Frontal_Mid_L (29) Supp_Motor_Area_R (21) 
644 3.44 0.0003 0.0007 Rolandic_Oper_L (298) Heschl_L (170) Insula_L (92) Temporal_Sup_L (84) 
336 3.62 0.0002 0.0004 Postcentral_L (135) Precentral_L (128) Parietal_Sup_R (72) Frontal_Sup_L (1) 
248 3.84 0.0001 0.0002 Temporal_Pole_Sup_L (158) Temporal_Pole_Mid_L (90) 
221 3.76 0.0001 0.0002 Putamen_R (133) Pallidum_R (88) 
125 3.55 0.0002 0.0004 Frontal_Inf_Tri_R (113) Insula_R (8) Frontal_Infer_Oper_R (4) 
125 4.13 <0.0001 0.0001 Temporal_Mid_R (93) Temporal_Inf_R (26) Temporal_Sup_R (6) 
101 3.71 0.0001 0.0003 Temporal_Mid_L (55) Temporal_Inf_L (46) 
Female > male 2407 5.03 <0.0001 <0.0001 Frontal_Mid_L (1451) Precentral_L (796) Frontal_Sup_L (100) Postcentral_L (26) Frontal_Inf_Tri_L (18) Frontal_Inf_Oper_L (16) 
1285 3.99 <0.0001 0.0001 Occipital_Mid_L (567) Temporal_Mid_L (293) Calcarine_L (181) Occipital_Sup_L (131) Occipital_Inf_L (77) Temporal_Inf_L (26) Lingual_L (6) Fusiform_L (3) Angular_L (1) 
1081 3.69 0.0001 0.0003 Occipital_Mid_R (563) Temporal_Mid_R (217) Occipital_Sup_R (122) Calcarine_R (122) Cuneus_R (57) 
873 3.45 0.0003 0.0006 Frontal_Mid_R (344) Precentral_R (304) Frontal_Sup_R (142) Frontal_Inf_Oper_R (74) Frontal_Sup_Medial_R (9) 
790 4.56 <0.0001 <0.0001 Frontal_Inf_Tri_R (600) Frontal_Mid_R (190) 
633 4.47 <0.0001 <0.0001 Temporal_Mid_R (413) Temporal_Inf_R (220) 
596 4.35 <0.0001 <0.0001 Supp_Motor_Area_R (319) Supp_Motor_Area_L (146) Paracentral_Lobule_L (106) Paracentral_Lobule_R (25) 
401 3.34 0.0005 0.0009 Cingulum_Ant_R (149) Frontal_Sup_Medial_R (99) Frontal_Sup_Medial_L (92) Frontal_Sup_R (35) Cingulum_Ant_L (22) Frontal_Mid_R (3) Frontal_Sup_L (1) 
335 3.82 0.0001 0.0002 Frontal_Mid_R (177) Frontal_Sup_R (151) Frontal_Sup_Medial_R (7) 
308 3.47 0.0003 0.0006 Frontal_Mid_L (260) Frontal_Inf_Tri_L (48) 
253 3.65 0.0002 0.0003 Temporal_Sup_L (151) Temporal_Mid_L (102) 
225 3.51 0.0003 0.0005 Temporal_Mid_R (156) Angular_R (60) Temporal_Sup_R (9) 
109 3.84 0.0001 0.0002 Cuneus_L (57) Occipital_Sup_L (52) 

Gonadal Steroid Effects

Automated Region of Interest Volumetry

Because there were no significant sex differences when correcting for overall ICV, we only tested whether ICV was associated with CAG repeat number or digit ratios. CAG repeat number, right 2D:4D, and left 2D:4D did not predict ICV in either males or females in the primary analyses or in the sensitivity analyses.

Tensor-Based Morphometry (Within Areas That are Larger in Males)

Within males, lower CAG repeat number was associated with increased volumes in the left supplementary motor area. However, this relationship was not seen in the sensitivity analyses. Within females, lower CAG repeat number was associated with increased volumes in the right inferior temporal gyrus. This relationship was also seen in the sensitivity analyses, suggesting that testosterone, acting through the androgen receptor, increases volumes of the right inferior temporal gyrus in females. In the main analyses, no negative associations were identified between left or right 2D:4D and brain volumes within cortical regions, which are greater in males than in females in either sex. Associations with the left fusiform and right inferior trigonal were seen with left 2D:4D in females in the analysis including only 1 individual from each MZ pair (Table 6 and Supplementary Tables 3 and 5).

Table 6

Positive associations between testosterone exposure and local GM volumes within regions where males are greater than females

 Cluster size Maximum t-value Maximum P-value Cluster P-value Anatomical regions 
Males 
 CAG 104 3.82 0.0001 0.0002 Supp_Motor_Area_L (102) Frontal_Sup_Medial_L (2) 
 Left 2D:4D None 
 Right 2D:4D None 
Females 
 CAG 139 4.80 <0.0001 <0.0001 Temporal_Inf_R (113) Fusiform_R (19) Temporal_Mid_R (7) 
 Left 2D:4D None 
 Right 2D:4D None 
 Cluster size Maximum t-value Maximum P-value Cluster P-value Anatomical regions 
Males 
 CAG 104 3.82 0.0001 0.0002 Supp_Motor_Area_L (102) Frontal_Sup_Medial_L (2) 
 Left 2D:4D None 
 Right 2D:4D None 
Females 
 CAG 139 4.80 <0.0001 <0.0001 Temporal_Inf_R (113) Fusiform_R (19) Temporal_Mid_R (7) 
 Left 2D:4D None 
 Right 2D:4D None 

Note: Lower numbers of CAG repeats are associated with greater sensitivity to testosterone (e.g. greater exposure). Lower 2D:4D ratios are associated with higher circulating levels of testosterone (e.g. greater exposure). This means that a negative correlation with the CAG or digit ratio represents a positive association between testosterone exposure and brain volume.

Tensor-Based Morphometry (Within Areas That are Larger in Females)

Within males, lower CAG repeat number was associated with decreased volumes in bilateral supplementary motor areas and paracentral lobules. This relationship was seen in the sensitivity analysis adjusting for birthweight, but not in the analysis including only 1 individual from each MZ pair. Lower right 2D:4D was also associated with decreased volumes in supplementary motor areas in both the primary and sensitivity analyses. An association between left 2D:4D and supplementary motor area volume was seen in the sensitivity analysis adjusting for birthweight. Taken together, these findings suggest that testosterone, acting through the androgen receptor, decreases volumes of the bilateral supplementary motor areas and paracentral lobules in males. In females, lower CAG repeat number was associated with decreased volumes in areas of the left middle occipital gyrus and left middle temporal gyrus in both the primary and sensitivity analyses, suggesting that testosterone, acting through the androgen receptor, decreases volumes of the left middle occipital gyrus and left middle temporal gyrus in females. Additional clusters were observed in the sensitivity analyses, particularly in that which included only 1 individual from each MZ pair. These results should be treated with caution as this analysis is expected to be less powerful and stable than the primary analysis (Table 7 and Supplementary Tables 4 and 6).

Table 7

Negative associations between testosterone exposure and local GM volumes within regions where females are greater than males

 Cluster size Maximum t-value Maximum P-value Cluster P-value Anatomical regions 
Males 
 CAG 302 4.06 <0.0001 0.0001 Supp_Motor_Area_R (135) Supp_Motor_Area_L (112) Paracentral_Lobule_L (45) Paracentral_Lobule_R (10) 
 Left 2D:4D None 
 Right 2D:4D 164 4.51 <0.0001 <0.0001 Supp_Motor_Area_R (144) Supp_Motor_Area_L (15) Paracentral_Lobule_L (5) 
Females 
 CAG 188 4.40 <0.0001 <0.0001 Temporal_Mid_L (99) Occipital_Mid_L (63) Occipital_Inf_L (23) Temporal_Inf_L (3) 
 Left 2D:4D None 
 Right 2D:4D None 
 Cluster size Maximum t-value Maximum P-value Cluster P-value Anatomical regions 
Males 
 CAG 302 4.06 <0.0001 0.0001 Supp_Motor_Area_R (135) Supp_Motor_Area_L (112) Paracentral_Lobule_L (45) Paracentral_Lobule_R (10) 
 Left 2D:4D None 
 Right 2D:4D 164 4.51 <0.0001 <0.0001 Supp_Motor_Area_R (144) Supp_Motor_Area_L (15) Paracentral_Lobule_L (5) 
Females 
 CAG 188 4.40 <0.0001 <0.0001 Temporal_Mid_L (99) Occipital_Mid_L (63) Occipital_Inf_L (23) Temporal_Inf_L (3) 
 Left 2D:4D None 
 Right 2D:4D None 

Note: Higher numbers of CAG repeats are associated with less sensitivity to testosterone (e.g. lower exposure). Higher 2D:4D ratios are associated with lower circulating levels of testosterone (e.g. lower exposure). This means that a positive correlation with the CAG or digit ratio represents a negative association between testosterone exposure and brain volume.

Discussion

This is the first study to provide detailed information on sex differences in global, regional, and local brain volumes in the neonate. Comparing our study with similar studies in older children and adults suggests that sex differences in cortical structure vary in a complex and highly dynamic way across the human lifespan.

Taking the existing literature into account, results can be grouped into 4 general patterns: (1) Sex differences which are stable across the lifespan, (2) sex differences which are not present in the neonate but arise during childhood and/or adolescence, (3) sex differences which are present during periods of high circulating gonadal steroids (e.g. neonate and adult, but not childhood), and (4) sex differences which are unique to the neonate.

Regarding pattern 1, relatively few sexual dimorphisms appear to be stable from the perinatal period into adulthood. Automated lobar morphometry analyses revealed a robust sex difference in ICV of 5.87% (males larger than females), which accounted for all regional sexual dimorphism. This difference is somewhat smaller than that reported in a previous study carried out in a subset of this group (7.8%, N = 74). Given the much larger sample size in the current study, 5.87% is expected to be a better estimate of sex differences in the general population. We also considered the possibility that differences between the 2 studies reflected the inclusion of twin births in the current study. This did not appear to be the case as sex differences in twins and singletons were comparable (5.66% and 6.40% for singletons and twins, respectively). This difference is smaller than that reported in children (11%) (De Bellis et al. 2001) and adults (10–14.6%) (Gur et al. 1999; Nopoulos et al. 2000), suggesting that males experience accelerated brain growth in the first several years of life when compared with females. Accelerated brain growth in males in the postnatal period might be mediated by the neonatal testosterone surge. Males are born with elevated testosterone levels, as a result of the sudden drop in inhibitory estrogen produced by the placenta. Testosterone rapidly decreases in the first day of life and then begins to rise again after the first week, peaking around the third to fourth months of life and then dropping back to very low levels by 1 year of age (Forest et al. 1974; Fechner 2003). It is interesting to note that one of the most consistent anatomical findings in young children with autism, a condition with a marked male bias, is that they have larger-than-average brains, and that this difference emerges in the first year of life (Hazlett et al. 2005). It has been hypothesized that high levels of prenatal testosterone increase the risk for autism (Baron-Cohen et al. 2005, 2011; Knickmeyer and Baron-Cohen 2006), but no research has addressed the possible role of neonatal testosterone exposure as a risk factor.

TBM revealed localized sex differences in GM volume that are similar to those reported in adults in the right putamen (larger in males) and the anterior cingulate (larger in females). Sexual dimorphism in the putamen may be related to differences in basal ganglia function between males and females, particularly as regards striatal dopamine release (Munro et al. 2006), and may account for the higher prevalence of males in neuropsychiatric disorders related to basal ganglia dysfunction, such as ADHD (Qiu et al. 2009) and substance abuse (White 1996). Although we did not see an association with CAG repeat length or digit ratio and putamen volumes, amniotic testosterone predicts increased behavioral approach tendencies in peripubertal children by biasing reward-related regions, including the putamen, to be more responsive to positive rather than negatively valenced cues (Lombardo, Ashwin, Auyeung, Chakrabarti, Lai, et al. 2012). The anterior cingulate cortex (ACC) is involved in diverse cognitive and emotional processes including pain perception, reward-processing, conflict-monitoring, error-detection, and theory of mind. It has been suggested that the varied tasks that activate the ACC can be unified by the concept of self-regulation (Posner et al. 2007). Given this, it is interesting to note that newborn girls have a greater capacity for self-regulation than newborn boys (Weinberg et al. 1999; Lundqvist and Sabel 2000; Lundqvist 2001; Boatella-Costa et al. 2007).

Regarding pattern 2, our automated lobar morphometry analyses did not reveal relatively larger volumes of occipital GM or cerebellum in males, which were recently reported in a study of 325 children (ages 4.5–18 years) that also used automated lobar volumetry, suggesting that these sex differences arise postnatally, perhaps as a consequence of neonatal testosterone exposure. Regarding TBM analyses, localized sex differences that are present in children and adults, but absent in the neonate, are primarily in regions associated with social cognition [e.g. orbitofrontal cortex (Stone et al. 1998; Bachevalier and Loveland 2006) (female > male), right temporoparietal junction/posterior superior temporal sulcus (Saxe and Powell 2006; Decety and Lamm 2007; Nummenmaa and Calder 2009), and amygdala (Bachevalier and Loveland 2006; Adolphs 2010) (male > female)] and language [e.g. Heschl's gyrus (Wong et al. 2008) and planum temporale (Shapleske et al. 1999) (female > male)]. This was somewhat surprising given that newborn girls show better social interactive capacities than newborn boys on the Brazelton Scale (Lundqvist and Sabel 2000; Boatella-Costa et al. 2007) and also show a visual preference for a human face over a mechanical mobile, while newborn boys show the reverse (Connellan et al. 2001). There is also a small, but robust, female advantage in emotion expression recognition from infancy onwards (McClure 2000) and females lead males in early communicative gestures, productive vocabulary, word–gesture combinations, and word–word combinations (Ozcaliskan and Goldin-Meadow 2010; Eriksson et al. 2012). In addition, all of the sexually dimorphic regions above, with the exception of the amygdala, were associated with amniotic testosterone levels in a sample of peripubertal children (Lombardo, Ashwin, Auyeung, Chakrabarti, Taylor, et al. 2012). We hypothesize that fetal testosterone may bias the attention of newborn girls toward social stimuli, resulting in a richer learning environment for social cognition and language that promotes the development of these regions in a sexually dimorphic manner. Sex differences in the amygdala may arise through an independent process related to X-chromosome dosage, as females with Turner syndrome (X-monosomy) have enlarged amygdalae (Good et al. 2003) and males with Klinefelter syndrome (XXY) have smaller amygdalae (Patwardhan et al. 2002).

Regarding pattern 3, our findings of greater volumes of the bilateral medial temporal cortex (including parahippocampus and hippocampus) and anterior superior, middle, and inferior temporal gyri in males appear similar to previous reports in adults (Good et al. 2001; Chen et al. 2007), as do our findings of greater volumes in inferior frontal (Good et al. 2001) and primary motor cortices (Lentini et al. 2012) in females, although the literature is less consistent for the latter. In contrast, these dimorphisms were not present in a large peripubertal sample (Lombardo, Ashwin, Auyeung, Chakrabarti, Taylor, et al. 2012). Inconsistencies between studies may reflect differences in methodology or characteristics of the study populations, but we suggest that these findings may also indicate a relationship with circulating testosterone levels. As discussed previously, infant males experience a surge in testosterone levels. The gonads are then relatively quiescent until the onset of puberty when testosterone begins to climb again peaking in adolescence/early adulthood. GM volumes in the parahippocampus are positively correlated with testosterone in adults (Lentini et al. 2012). Regional GM was inversely correlated with testosterone in the left inferior frontal gyrus in a study of young adults (Witte et al. 2010), and greater androgen receptor efficiency in female adolescents is associated with a more masculine pattern of cortical maturation (i.e. increase of loss) in the left inferior frontal gyrus (Raznahan et al. 2010). Unfortunately, no direct measures of circulating testosterone at the time of MRI were available in our sample.

Regarding pattern 4, the majority of our findings appear to be unique to the neonatal period. This includes the findings of greater volumes in the fusiform gyri, left anterior supplementary motor area, rolandic opercula, insular cortex, and Heschl's gyri in males and greater volumes of the dorsolateral prefrontal cortex, visual cortex, posterior supplementary motor areas, and paracentral lobules in females. Whether these differences impact function in the short or long term remains to be elucidated. In the case of the dorsolateral prefrontal and visual cortices, it is interesting to note that sexual dimorphism in the opposite direction has been reported in peripubertal children. This could indicate accelerated maturation of these areas in females. A longitudinal study of brain development from age 4 to 20 has revealed that GM follows an inverted U-shaped trajectory with females peaking earlier than males in many regions (Giedd et al. 1999). This would manifest as a female greater than male difference in early development, when the dominant maturational processes are growth related (synapotogenesis, gliogenesis, dendritic branching, and axonal growth), but a male greater than female difference in late childhood/early adolescence, when the dominant maturational process is cortical thinning (perhaps as a consequence of dendritic pruning). There is functional evidence that the visual system matures earlier in females. Infant girls show earlier binocular function (Gwiazda et al. 1989), higher and earlier rates of habituation in response to smaller visual changes (Creighton 1984), and shorter latency EEG responses to visual pattern reversal (Malcolm et al. 2002).

These divergent spatiotemporal patterns suggest that multiple biological mechanisms contribute to sexual differentiation of the brain. We did explore one potential contributor, early androgen exposure. Androgen exposure and sensitivity, as indexed by the ratio of the second to fourth digit and by the number of CAG triplets in the androgen receptor gene, respectively, had some minor sex-specific effects on local GM volume, but did not appear to be the primary determinant of sexual dimorphism in this age group. However, we cannot rule out the possibility that a more direct measure of early testosterone exposure, such as amniotic testosterone, would have revealed significant effects. We also note that the utility of the digit ratio as a marker for individual differences in prenatal androgen exposure continues to be a matter of debate (Berenbaum et al. 2009; Breedlove 2010), and that a study of typically developing children such as this cannot directly test the possibility that sexual dimorphism in the neonate brain is determined by the presence or absence of testosterone in a binary manner.

In conclusion, we have demonstrated widespread sexual dimorphism in the neonate brain. As this is the first study of its kind, independent replication is critical. Comparison with the existing literature in children and adults suggests that sex is related to cortical development in a highly complex manner in terms of both spatial and temporal effects. It is likely that sexual dimorphism of the brain reflects the dynamic interplay of multiple mechanisms both biological (e.g. prenatal hormone production, neonatal hormone production, pubertal hormone production, direct sex-chromosome effects) and experiential (e.g. parental expectations and interactive behavior, exposure to physical hazards, culturally influenced lifestyle differences) (Rutter et al. 2003). Longitudinal designs which take account of all these factors are necessary to test detailed mechanistic hypotheses about the relationship between sex differences in brain development, cognitive function, and psychiatric risk.

Supplementary Material

Supplementary material can be found at: http://www.cercor.oxfordjournals.org/.

Funding

This work was supported by the National Institutes of Health (MH064065 and MH070890 to J.H.G., MH083045 to R.C.K., HD03110 and MH091645 to M.S., and RR025747, P01CA142538, MH086633, EB005149, and AG033387 to H.Z.) and by Autism Speaks.

Notes

We thank the participating families who made this project possible as well as the staff of the UNC MRI Research Center, the UNC NeuroImage Research and Analysis Laboratories, and the UNC Early Brain Development Program. Conflict of Interest: None declared.

References

Adolphs
R
What does the amygdala contribute to social cognition?
Ann N Y Acad Sci
 , 
2010
, vol. 
1191
 (pg. 
42
-
61
)
Allen
RC
Zoghbi
HY
Moseley
AB
Rosenblatt
HM
Belmont
JW
Methylation of Hpaii and Hhai sites near the polymorphic CAG repeat in the human androgen-receptor gene correlates with X-chromosome inactivation
Am J Hum Genet
 , 
1992
, vol. 
51
 (pg. 
1229
-
1239
)
Bachevalier
J
Loveland
KA
The orbitofrontal-amygdala circuit and self-regulation of social-emotional behavior in autism
Neurosci Biobehav Rev
 , 
2006
, vol. 
30
 (pg. 
97
-
117
)
Baird
G
Cox
A
Charman
T
Baron-Cohen
S
Wheelwright
S
Swettenham
J
Drew
A
Nightingale
N
A screening instrument for autism at 18 months of age: a six year follow-up study
J Am Acad Child Adolesc Psychiatry
 , 
2000
, vol. 
39
 (pg. 
694
-
702
)
Baron-Cohen
S
Knickmeyer
RC
Belmonte
MK
Sex differences in the brain: Implications for explaining autism
Science
 , 
2005
, vol. 
310
 (pg. 
819
-
823
)
Baron-Cohen
S
Lombardo
MV
Auyeung
B
Ashwin
E
Chakrabarti
B
Knickmeyer
R
Why are autism spectrum conditions more prevalent in males?
Plos Biol
 , 
2011
, vol. 
9
 pg. 
e1001081
 
BDCG (Brain Development Cooperative Group)
Total and regional brain volumes in a population-based normative sample from 4 to 18 years: the NIH MRI Study of Normal Brain Development
Cereb Cortex
 , 
2012
, vol. 
22
 (pg. 
1
-
12
)
Bebbington
P
Dunn
G
Jenkins
R
Lewis
G
Brugha
T
Farrell
M
Heltzer
M
The influence of age and sex on the prevalence of depressive conditions: report from the National Survey of Psychiatric Morbidity
Psychol Med
 , 
1998
, vol. 
28
 (pg. 
9
-
19
)
Berenbaum
SA
Bryk
KK
Nowak
N
Quigley
CA
Moffat
S
Fingers as a marker of prenatal androgen exposure
Endocrinology
 , 
2009
, vol. 
150
 (pg. 
5119
-
5124
)
Boatella-Costa
E
Costas-Moragas
C
Botet-Mussons
F
Fornieles-Deu
A
De Caceres-Zurita
ML
Behavioral gender differences in the neonatal period according to the Brazelton scale
Early Hum Dev
 , 
2007
, vol. 
83
 (pg. 
91
-
97
)
Breedlove
SM
Minireview: organizational hypothesis: instances of the fingerpost
Endocrinology
 , 
2010
, vol. 
151
 (pg. 
4116
-
4122
)
Breedlove
SM
Sexual differentiation of the human nervous system
Ann Rev Psychol
 , 
1994
, vol. 
45
 (pg. 
389
-
418
)
Brody
BA
Kinney
HC
Kloman
AS
Gilles
FH
Sequence of central nervous system myelination in human infancy. I. An autopsy study of myelination
J Neuropathol Exp Neurol
 , 
1987
, vol. 
46
 (pg. 
283
-
301
)
Cao
J
The size of the connected components of excursion sets of chi(2), t and F fields
Adv Appl Probab
 , 
1999
, vol. 
31
 (pg. 
579
-
595
)
Caviness
VS
Kennedy
DN
Richelme
C
Rademacher
J
Filipek
PA
The human brain age 7–11 years: a volumetric analysis based on magnetic resonance images
Cereb Cortex
 , 
1996
, vol. 
6
 (pg. 
726
-
736
)
Chakrabarti
S
Fombonne
E
Pervasive developmental disorders in preschool children
J Am Med Assoc
 , 
2001
, vol. 
285
 (pg. 
3093
-
3099
)
Chang
C
Androgens and androgen receptor: mechanisms, functions, and clinical application
 , 
2002
Boston
Kluwer Academic Publishers
Chen
XH
Sachdev
PS
Wen
W
Anstey
KJ
Sex differences in regional gray matter in healthy individuals aged 44–48 years: a voxel-based morphometric study
Neuroimage
 , 
2007
, vol. 
36
 (pg. 
691
-
699
)
Connellan
J
Baron-Cohen
S
Wheelwright
S
Ba'tki
A
Ahluwalia
J
Sex differences in human neonatal social perception
Infant Behav Develop
 , 
2001
, vol. 
23
 (pg. 
113
-
118
)
Cox
RW
AFNI: software for analysis and visualization of functional magnetic resonance neuroimages
Comput Biomed Res
 , 
1996
, vol. 
29
 (pg. 
162
-
173
)
Creighton
DE
Sex differences in the visual habituation of 4-, 6-, and 8-month-old infants
Infant Behav Develop
 , 
1984
, vol. 
7
 (pg. 
237
-
249
)
De Bellis
MD
Keshavan
MS
Beers
SR
Hall
J
Frustaci
K
Masalehdan
A
Noll
J
Boring
AM
Sex differences in brain maturation during childhood and adolescence
Cereb Cortex
 , 
2001
, vol. 
11
 (pg. 
552
-
557
)
Decety
J
Lamm
C
The role of the right temporoparietal junction in social interaction: how low-level computational processes contribute to meta-cognition
Neuroscientist
 , 
2007
, vol. 
13
 (pg. 
580
-
593
)
Dekaban
AS
Sadowsky
D
Changes in brain weights during the span of human life: relation of brain weights to body heights and body weights
Ann Neurol
 , 
1978
, vol. 
4
 (pg. 
345
-
356
)
Eriksson
M
Marschik
PB
Tulviste
T
Almgren
M
Perez Pereira
M
Wehberg
S
Marjanovic-Umek
L
Gayraud
F
Kovacevic
M
Gallego
C
Differences between girls and boys in emerging language skills: evidence from 10 language communities
Br J Dev Psychol
 , 
2012
, vol. 
30
 (pg. 
326
-
343
)
Fatemi
SH
Folsom
TD
The neurodevelopmental hypothesis of schizophrenia, revisited
Schizophr Bull
 , 
2009
, vol. 
35
 (pg. 
528
-
548
)
Fechner
PY
Hayward
C
The biology of puberty: new developments in sex differences
Gender differences at puberty
 , 
2003
Cambridge
Cambridge University Press
(pg. 
17
-
28
)
Filipek
PA
Neuroimaging in the developmental disorders: the state of the science
J Child Psychol Psyc
 , 
1999
, vol. 
40
 (pg. 
113
-
128
)
Filipek
PA
Richelme
C
Kennedy
DN
Caviness
VS
Young-adult human brain—an MRI-based morphometric analysis
Cereb Cortex
 , 
1994
, vol. 
4
 (pg. 
344
-
360
)
Forest
MG
Sizonenko
PC
Cathiard
AM
Bertrand
J
Hypophyso-gonadal function in humans during the first year of life: I. Evidence for testicular activity in early infancy
J Clin Invest
 , 
1974
, vol. 
53
 (pg. 
819
-
828
)
Giedd
JN
Blumenthal
J
Jeffries
NO
Castellanos
FX
Liu
H
Zijdenbos
A
Paus
T
Evans
AC
Rapoport
JL
Brain development during childhood and adolescence: a longitudinal MRI study
Nat Neurosci
 , 
1999
, vol. 
2
 (pg. 
861
-
863
)
Giedd
JN
Castellanos
FX
Rajapakse
JC
Vaituzis
AC
Rapoport
JL
Sexual dimorphism of the developing human brain
Prog Neuropsychopharmacol Biol Psychiatry
 , 
1997
, vol. 
21
 (pg. 
1185
-
1201
)
Giedd
JN
Snell
JW
Lange
N
Rajapakse
JC
Casey
BJ
Kozuch
PL
Vaituzis
AC
Vauss
YC
Hamburger
SD
Kaysen
D
, et al.  . 
Quantitative magnetic resonance imaging of human brain development: ages 4–18
Cereb Cortex
 , 
1996
, vol. 
6
 (pg. 
551
-
560
)
Gilmore
JH
Kang
C
Evans
DD
Wolfe
HM
Smith
JK
Lieberman
JA
Lin
W
Hamer
RM
Styner
M
Gerig
G
Prenatal and neonatal brain structure and white matter maturation in children at high risk for schizophrenia
Am J Psychiatry
 , 
2010
, vol. 
167
 (pg. 
1083
-
1091
)
Gilmore
JH
Lin
W
Prastawa
MW
Looney
CB
Vetsa
YSK
Knickmeyer
RC
Evans
DD
Smith
JK
Hamer
RM
Lieberman
JA
, et al.  . 
Regional gray matter growth, sexual dimorphism, and cerebral asymmetry in the neonatal brain
J Neurosci
 , 
2007
, vol. 
27
 (pg. 
1255
-
1260
)
Gilmore
JH
Shi
F
Woolson
SL
Knickmeyer
RC
Short
SJ
Lin
W
Zhu
H
Hamer
RM
Styner
M
Shen
D
Longitudinal development of cortical and subcortical gray matter from birth to 2 years
Cereb Cortex
 , 
2012
, vol. 
22
 (pg. 
2478
-
2485
)
Goldstein
JM
Seidman
LJ
Horton
NJ
Makris
N
Kennedy
DN
Caviness Jnr
VS
Faraone
SV
Tsuang
MT
Normal sexual dimorphism of the adult human brain assessed by in vivo magnetic resonance imaging
Cereb Cortex
 , 
2001
, vol. 
11
 (pg. 
490
-
497
)
Good
CD
Johnsrude
I
Ashburner
J
Henson
RNA
Friston
KJ
Frackowiak
RSJ
Cerebral asymmetry and the effects of sex and handedness on brain structure: a voxel-based morphometric analysis of 465 normal adult human brains
Neuroimage
 , 
2001
, vol. 
14
 (pg. 
685
-
700
)
Good
CD
Lawrence
K
Thomas
NS
Price
CJ
Ashburner
J
Friston
KJ
Frackowiak
RSJ
Oreland
L
Skuse
DH
Dosage-sensitive X-linked locus influences the development of amygdala and orbitofrontal cortex, and fear recognition in humans
Brain
 , 
2003
, vol. 
126
 (pg. 
2431
-
2446
)
Gur
RC
Turetsky
BI
Matsui
M
Yan
M
Bilker
W
Hughett
P
Gur
RE
Sex differences in brain gray and white matter in healthy young adults: correlations with cognitive performance
J Neurosci
 , 
1999
, vol. 
19
 (pg. 
4065
-
4072
)
Gwiazda
J
Bauer
J
Held
R
Binocular function in human infants: correlation of stereoptic and fusion-rivalry discriminations
J Pediatr Ophthalmol Strabismus
 , 
1989
, vol. 
26
 (pg. 
128
-
132
)
Hayasaka
S
Phan
KL
Liberzon
I
Worsley
KJ
Nichols
TE
Nonstationary cluster-size inference with random field and permutation methods
Neuroimage
 , 
2004
, vol. 
22
 (pg. 
676
-
687
)
Hazlett
HC
Poe
M
Gerig
G
Smith
RG
Provenzale
J
Ross
A
Gilmore
J
Piven
J
Magnetic resonance Imaging and head circumference study of brain size in autism—birth through age 2 years
Arch Gen Psychiatry
 , 
2005
, vol. 
62
 (pg. 
1366
-
1376
)
Hazlett
HC
Poe
MD
Gerig
G
Styner
M
Chappell
C
Smith
RG
Vachet
C
Piven
J
Early brain overgrowth in autism associated with an increase in cortical surface area before age 2 years
Arch Gen Psychiatry
 , 
2011
, vol. 
68
 (pg. 
467
-
476
)
Hua
X
Leow
AD
Parikshak
N
Lee
S
Chiang
MC
Toga
AW
Jack
CR
Jr
Weiner
MW
Thompson
PM
Tensor-based morphometry as a neuroimaging biomarker for Alzheimer's disease: an MRI study of 676 AD, MCI, and normal subjects
Neuroimage
 , 
2008
, vol. 
43
 (pg. 
458
-
469
)
Huttenlocher
PR
Dabholkar
AS
Regional differences in synaptogenesis in human cerebral cortex
J Comp Neurol
 , 
1997
, vol. 
387
 (pg. 
167
-
178
)
Joshi
S
Davis
B
Jomier
M
Gerig
G
Unbiased diffeomorphic atlas construction for computational anatomy
Neuroimage
 , 
2004
, vol. 
23(Suppl 1)
 (pg. 
S151
-
S160
)
Kagan
J
Herschkowitz
N
A young mind in a growing brain
 , 
2005
Mahwah, NJ
Erlbaum Associates
Kasprian
G
Brugger
PC
Weber
M
Krssak
M
Krampl
E
Herold
C
Prayer
D
In utero tractography of fetal white matter development
Neuroimage
 , 
2008
, vol. 
43
 (pg. 
213
-
224
)
Knickmeyer
RC
Baron-Cohen
S
Fetal testosterone and sex differences in typical social development and in autism
J Child Neurol
 , 
2006
, vol. 
21
 (pg. 
825
-
845
)
Knickmeyer
RC
Gouttard
S
Kang
C
Evans
D
Wilber
K
Smith
JK
Hamer
RM
Lin
W
Gerig
G
Gilmore
JH
A structural MRI study of human brain development from birth to 2 years
J Neurosci
 , 
2008
, vol. 
28
 (pg. 
12176
-
12182
)
Knickmeyer
RC
Woolson
S
Hamer
RM
Konneker
T
Gilmore
JH
2D:4D ratios in the first 2 years of life: stability and relation to testosterone exposure and sensitivity
Horm Behav
 , 
2011
, vol. 
60
 (pg. 
256
-
263
)
Lentini
E
Kasahara
M
Arver
S
Savic
I
Sex differences in the human brain and the impact of sex chromosomes and sex hormones
Cereb Cortex
 , 
2012
 
[epub ahead of print]
Lepore
N
Brun
C
Chou
YY
Chiang
MC
Dutton
RA
Hayashi
KM
Luders
E
Lopez
OL
Aizenstein
HJ
Toga
AW
, et al.  . 
Generalized tensor-based morphometry of HIV/AIDS using multivariate statistics on deformation tensors
IEEE Trans Med Imaging
 , 
2008
, vol. 
27
 (pg. 
129
-
141
)
Li
Y
Gilmore
JH
Shen
D
Styner
M
Lin
W
Zhu
H
Multiscale adaptive generalized estimating equations for longitudinal neuroimaging data
Neuroimage
 , 
2013
, vol. 
72
 (pg. 
91
-
105
)
Li
YM
Zhu
HT
Shen
DG
Lin
WL
Gilmore
JH
Ibrahim
JG
Multiscale adaptive regression models for neuroimaging data
J Roy Stat Soc B
 , 
2011
, vol. 
73
 (pg. 
559
-
578
)
Liang
KY
Zeger
SL
Longitudinal Data-Analysis Using Generalized Linear-Models
Biometrika
 , 
1986
, vol. 
73
 (pg. 
13
-
22
)
Lombardo
MV
Ashwin
E
Auyeung
B
Chakrabarti
B
Lai
MC
Taylor
K
Hackett
G
Bullmore
ET
Baron-Cohen
S
Fetal programming effects of testosterone on the reward system and behavioral approach tendencies in humans
Biol Psychiatry
 , 
2012
, vol. 
72
 (pg. 
839
-
847
)
Lombardo
MV
Ashwin
E
Auyeung
B
Chakrabarti
B
Taylor
K
Hackett
G
Bullmore
ET
Baron-Cohen
S
Fetal testosterone influences sexually dimorphic gray matter in the human brain
J Neurosci
 , 
2012
, vol. 
32
 (pg. 
674
-
680
)
Lucas
A
Beard
C
O'Fallon
W
Kurland
L
Fifty-year trends in the incidence of anorexia nervosa in Rochester, Minnesota: a population-based study
Am J Psychiatry
 , 
1991
, vol. 
148
 (pg. 
917
-
922
)
Lundqvist
C
Correlation between level of self-regulation in the newborn infant and developmental status at two years of age
Acta Paediatr
 , 
2001
, vol. 
90
 (pg. 
345
-
350
)
Lundqvist
C
Sabel
KG
Brief report: The Brazelton Neonatal Behavioral Assessment Scale detects differences among newborn infants of optimal health
J Pediatr Psychol
 , 
2000
, vol. 
25
 (pg. 
577
-
582
)
Malcolm
CA
McCulloch
DL
Shepherd
AJ
Pattern-reversal visual evoked potentials in infants: gender differences during early visual maturation
Dev Med Child Neurol
 , 
2002
, vol. 
44
 (pg. 
345
-
351
)
Manning
JT
Wilson
JD
Lewis-Jones
DI
D. S
The ratio of 2nd to 4th digit length: a predictor of sperm numbers and levels of testosterone, LH and Oestrogen
Hum Reprod
 , 
1998
, vol. 
13
 (pg. 
3000
-
3004
)
McCarthy
MM
Arnold
AP
Reframing sexual differentiation of the brain
Nat Neurosci
 , 
2011
, vol. 
14
 (pg. 
677
-
683
)
McClure
EB
A meta-analytic review of sex differences in facial expression processing and their development in infants, children, and adolescents
Psychol Bull
 , 
2000
, vol. 
126
 (pg. 
424
-
453
)
McLean
C
Asnaani
A
Litz
B
Hofmann
S
Gender differences in anxiety disorders: prevalence, course of illness, comorbidity and burden of illness
J Psychiatr Res
 , 
2011
, vol. 
45
 (pg. 
1027
-
1035
)
Moffitt
TE
Juvenile-delinquency and attention deficit disorder—boys developmental trajectories from age 3 to age 15
Child Dev
 , 
1990
, vol. 
61
 (pg. 
893
-
910
)
Moffitt
TE
Caspi
A
Childhood predictors differentiate life-course persistent and adolescence-limited antisocial pathways among males and females
Dev Psychopathol
 , 
2001
, vol. 
13
 (pg. 
355
-
375
)
Munoz
A
Rosner
B
Carey
V
Regression-analysis in the presence of heterogeneous intraclass correlations
Biometrics
 , 
1986
, vol. 
42
 (pg. 
653
-
658
)
Munro
CA
McCaul
ME
Wong
DF
Oswald
LM
Zhou
Y
Brasic
J
Kuwabara
H
Kumar
A
Alexander
M
Ye
W
, et al.  . 
Sex differences in striatal dopamine release in healthy adults
Biol Psychiatry
 , 
2006
, vol. 
59
 (pg. 
966
-
974
)
Nopoulos
P
Flaum
M
Andreasen
NC
Sex differences in brain morphology in schizophrenia
Am J Psychiatry
 , 
1997
, vol. 
154
 (pg. 
1648
-
1654
)
Nopoulos
P
Flaum
M
O'Leary
D
Andreasen
NC
Sexual dimorphism in the human brain: evaluation of tissue volume, tissue composition and surface anatomy using magnetic resonance imaging
Psychiatry Res
 , 
2000
, vol. 
98
 (pg. 
1
-
13
)
Nummenmaa
L
Calder
AJ
Neural mechanisms of social attention
Trends Cogn Sci
 , 
2009
, vol. 
13
 (pg. 
135
-
143
)
Ozcaliskan
S
Goldin-Meadow
S
Sex differences in language first appear in gesture
Dev Sci
 , 
2010
, vol. 
13
 (pg. 
752
-
760
)
Patwardhan
AJ
Brown
WE
Bender
BG
Linden
MG
Eliz
S
Reiss
AL
Reduced size of the amygdala in individuals with 47,XXY and 47,XXX Karyotypes
Am J Med Genet
 , 
2002
, vol. 
114
 (pg. 
93
-
98
)
Petanjek
Z
Judas
M
Kostovic
I
Uylings
HBM
Lifespan alterations of basal dendritic trees of pyramidal neurons in the human prefrontal cortex: a layer-specific pattern
Cereb Cortex
 , 
2008
, vol. 
18
 (pg. 
915
-
929
)
Posner
MI
Rothbart
MK
Sheese
BE
Tang
Y
The anterior cingulate gyrus and the mechanism of self-regulation
Cogn Affect Behav Neurosci
 , 
2007
, vol. 
7
 (pg. 
391
-
395
)
Prastawa
M
Gilmore
JH
Lin
WL
Gerig
G
Automatic segmentation of MR images of the developing newborn brain
Med Image Anal
 , 
2005
, vol. 
9
 (pg. 
457
-
466
)
Qiu
A
Crocetti
D
Adler
M
Mahone
EM
Denckla
MB
Miller
MI
Mostofsky
SH
Basal ganglia volume and shape in children with attention deficit hyperactivity disorder
Am J Psychiatry
 , 
2009
, vol. 
166
 (pg. 
74
-
82
)
Rapoport
JC
Addington
AM
Frangou
S
The neurodevelopmental model of schizophrenia: update 2005
Mol Psychiatry
 , 
2005
, vol. 
10
 (pg. 
439
-
449
)
Raznahan
A
Lee
Y
Stidd
R
Long
R
Greenstein
D
Clasen
L
Addington
A
Gogtay
N
Rapoport
JL
Giedd
JN
Longitudinally mapping the influence of sex and androgen signaling on the dynamics of human cortical maturation in adolescence
Proc Natl Acad Sci USA
 , 
2010
, vol. 
107
 (pg. 
16988
-
16993
)
Reiss
AL
Abrams
MT
Singer
HS
Ross
JL
Denckla
MB
Brain development, gender and IQ in children—a volumetric imaging study
Brain
 , 
1996
, vol. 
119
 (pg. 
1763
-
1774
)
Remschmidt
H
Schulz
E
Martin
M
Warnke
A
Trott
G
Childhood-onset schizophrenia: history of the concept and recent studies
Schizophr Bull
 , 
1994
, vol. 
20
 (pg. 
727
-
745
)
Rijpkema
M
Everaerd
D
van der Pol
C
Franke
B
Tendolkar
I
Fernandez
G
Normal sexual dimorphism in the human basal ganglia
Hum Brain Mapp
 , 
2012
, vol. 
33
 (pg. 
1246
-
1252
)
Rutter
M
Caspi
A
Moffitt
TE
Using sex differences in psychopathology to study causal mechanisms: unifying issues and research strategies
J Child Psychol Psychiatry
 , 
2003
, vol. 
44
 (pg. 
1092
-
1115
)
Saxe
R
Powell
LJ
It's the thought that counts: specific brain regions for one component of theory of mind
Psychol Sci
 , 
2006
, vol. 
17
 (pg. 
692
-
699
)
Shapleske
J
Rossell
SL
Woodruff
PW
David
AS
The planum temporale: a systematic, quantitative review of its structural, functional and clinical significance
Brain Res Brain Res Rev
 , 
1999
, vol. 
29
 (pg. 
26
-
49
)
Stone
VE
Baron-Cohen
S
Knight
RT
Frontal lobe contributions to theory of mind
J Cogn Neurosci
 , 
1998
, vol. 
10
 (pg. 
640
-
656
)
Szatmari
P
Offord
DR
Boyle
MH
Ontario child health study—prevalence of attention deficit disorder with hyperactivity
J Child Psychol Psychiatry
 , 
1989
, vol. 
30
 (pg. 
219
-
230
)
Wang
HS
Kuo
MF
Tourette's syndrome in Taiwan: an epidemiological study of tic disorders in an elementary school at Taipei County
Brain Dev
 , 
2003
, vol. 
25
 (pg. 
S29
-
S31
)
Weinberg
MK
Tronick
EZ
Cohn
JF
Olson
KL
Gender differences in emotional expressivity and self-regulation during early infancy
Dev Psychol
 , 
1999
, vol. 
35
 (pg. 
175
-
188
)
White
NM
Addictive drugs as reinforcers: multiple partial actions on memory systems
Addiction
 , 
1996
, vol. 
91
 (pg. 
921
-
949
discussion 951–965
Witte
AV
Savli
M
Holik
A
Kasper
S
Lanzenberger
R
Regional sex differences in grey matter volume are associated with sex hormones in the young adult human brain
Neuroimage
 , 
2010
, vol. 
49
 (pg. 
1205
-
1212
)
Wolff
JJ
Gu
H
Gerig
G
Elison
JT
Styner
M
Gouttard
S
Botteron
KN
Dager
SR
Dawson
G
Estes
AM
, et al.  . 
Differences in white matter fiber tract development present from 6 to 24 months in infants with autism
Am J Psychiatry
 , 
2012
, vol. 
169
 (pg. 
589
-
600
)
Wong
PC
Warrier
CM
Penhune
VB
Roy
AK
Sadehh
A
Parrish
TB
Zatorre
RJ
Volume of left Heschl's Gyrus and linguistic pitch learning
Cereb Cortex
 , 
2008
, vol. 
18
 (pg. 
828
-
836
)
Worsley
KJ
Andermann
M
Koulis
T
MacDonald
D
Evans
AC
Detecting changes in nonisotropic images
Hum Brain Mapp
 , 
1999
, vol. 
8
 (pg. 
98
-
101
)
Worsley
KJ
Marrett
S
Neelin
P
Vandal
AC
Friston
K
Evans
AC
A unified statistical approach for determining significant voxels in images of cerebral activation
Hum Brain Mapp
 , 
1996
, vol. 
4
 (pg. 
58
-
73
)
Yushkevich
PA
Piven
J
Hazlett
HC
Smith
RG
Ho
S
Gee
JC
Gerig
G
User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability
Neuroimage
 , 
2006
, vol. 
31
 (pg. 
1116
-
1128
)
Zhu
HT
Li
Y
Ibrahim
JG
Lin
W
Shen
DG
MARM: multiscale adaptive regression for neuroimaging data
Inf Process Med Imaging
 , 
2009
, vol. 
21
 (pg. 
314
-
325
)