Abstract

The absence of all or part of one X chromosome in female humans causes Turner's syndrome (TS), providing a unique “knockout model” to investigate the role of the X chromosome in neuroanatomy and cognition. Previous studies have demonstrated TS-associated brain differences; however, it remains largely unknown 1) how the brain structures are affected by the type of X chromosome loss and 2) how X chromosome loss influences the brain–cognition relationship. Here, we addressed these by investigating gray matter morphology and white matter connectivity using a multimodal MRI dataset from 34 adolescent TS patients (13 mosaic and 21 nonmosaic) and 21 controls. Intriguingly, the 2 TS groups exhibited significant differences in surface area in the right angular gyrus and in white matter integrity of the left tapetum of corpus callosum; these data support a link between these brain phenotypes and the type of X chromosome loss in TS. We further showed that the X chromosome modulates specific brain–cognition relationships: thickness and surface area in multiple cortical regions are positively correlated with working-memory performance in controls but negatively in TS. These findings provide novel insights into the X chromosome effect on neuroanatomical and cognitive phenotypes and highlight the role of genetic factors in brain–cognition relationships.

Introduction

The X chromosome comprises ∼4% of the human genome and has long been considered to play a crucial role in the development of the human brain and intelligence (Lehrke 1972; Turner 1996; Johnson et al. 2009). X-linked gene defects have been disproportionately found in various psychiatric disorders and particularly in mental retardation (Ropers and Hamel 2005; Skuse 2005). Genomic data demonstrated that a large number of X-linked genes are involved in postsynaptic protein coding, which is essential for neuronal plasticity and cognitive processes (Laumonnier et al. 2007; Swingland et al. 2012).

In healthy women with a standard karyotype (46XX), one of the 2 copies of the X chromosome is randomly inactivated to ensure the equal expression of X-linked genes with men (46XY), although a set of genes escapes this X inactivation (Carrel et al. 1999; Disteche 1999). Additionally, to match the expression level of the X-linked genes on the single X chromosome with those of the autosomal genes on the 2 copies, the gene expression of the active copy of the X chromosome is up-regulated in human somatic tissues (Nguyen and Disteche 2006a,b). Intriguingly, this X-linked gene dosage compensation exhibited variations between tissues, leading to a higher global expression of X-linked genes in brain tissues than other tissues for both humans and mice (Nguyen and Disteche 2006a,b). The observed excess dosage in the brain further supports an essential role of the X chromosome in brain development and function. However, to date, empirical investigations on how the X chromosome influences brain structure and function remain scarce, particularly in humans.

A naturally occurring “knockout model” for studying the role of the X chromosome in human brain phenotypes is Turner's syndrome (TS), a disorder in female humans characterized by the absence of all or part of a normal second X chromosome (Sybert and McCauley 2004). TS occurs in ∼1 per 2000 live female births and typically leads to aberrant physical phenotypes such as short stature and gonadal dysgenesis (Gravholt 2005). Notably, cognitive deficits in visuospatial, math, and social processing have been repeatedly reported in TS (Rovet 2004; Hong and Reiss 2012), implying TS-associated brain differences. Previous magnetic resonance imaging (MRI) studies have found neuroanatomical differences in TS patients, including the reduction of parieto-occipital gray matter (GM) volume (Reiss et al. 1995; Molko et al. 2004; Marzelli et al. 2011), as well as aberrant thickness or/and surface area in specific cortical regions (Raznahan et al. 2010; Lepage, Clouchoux et al. 2013; Lepage, Hong et al. 2013; Lepage, Mazaika, et al. 2013) and impaired white matter (WM) integrity in superior longitudinal fasciculus (Holzapfel et al. 2006; Yamagata et al. 2012).

In “classical” TS patients, the entire second X chromosome is absent in all cells; this is referred to as X-monosomy (nonmosaic TS) (Sybert and McCauley 2004). Notably, it has been recognized that a number of TS patients exhibit mosaicism that is characterized by X-monosomy and another cell line with the presence of the second X chromosome, that is, losing the entire second X chromosome in only a proportion of their cells (mosaic TS). These 2 subtypes of TS patients provide a valuable opportunity to understand the “dosage effect” of the X chromosome on neuroanatomy. It is possible that any X chromosome loss could fundamentally influence the neuroanatomy in a similar manner for both nonmosaic and mosaic cases. Alternatively, the degree of neuroanatomical changes could be a function of the type of X chromosome loss. The majority of previous studies either included only nonmosaic patients or mixed the 2 types of TS patients. Murphy et al. (1993, 1997) did compare the 2 TS groups and showed significant between-subtype differences in the sub-cortical nuclei volume and cerebral metabolic rates. However, it remains unknown how the 2 lower-order components of cortical volume, cortical thickness and surface area, and WM connectivity may be affected by X chromosome dosage.

Additionally, while previous studies have demonstrated the impact of X chromosome loss on brain structures, the mechanisms of its role in cognition have been largely a matter of speculation based on functional localizations under normal conditions, such as linking the TS-associated volume reduction in the parieto-occipital region with deficits in visuospatial and math skills (Reiss et al. 1995; Molko et al. 2004). However, a few studies have revealed significant correlations between specific brain measures and cognitive scores within a healthy control group but not within a TS group or vice versa (Murphy et al. 1997; Lepage, Hong, et al. 2013), therefore implying a TS-associated difference in the brain–cognition relationship. Moreover, recent studies have explicitly demonstrated that brain–cognition relations can be modulated by specific genes in a healthy population (Schmidt et al. 2009). Therefore, given the loss of the X chromosome, specific relationships between the cortical morphology/WM connectivity and cognitive performance may be altered in TS patients, further underlying the TS-specific cognitive profiles.

In the current study, we examined 1) whether there is an “X chromosome dosage effect” on GM morphology and WM connectivity and 2) whether the loss of the X chromosome alters the relationship between brain structures (morphology or connectivity) and cognitions. To test these, healthy control, mosaic and nonmosaic TS patients of adolescent age were included. A set of cognitive assessments was performed, and structural MRI and diffusion tensor imaging (DTI) scans were collected to measure brain morphology and connectivity.

Method and Materials

Participants

The TS patients (34 females; age range: 9–18 years) were recruited from the China–Japan Friendship Hospital (CJFH) and Peking Union Medical College Hospital (PUMCH). Age-matched healthy controls (21 females; age range: 10–18 years) were recruited through local community and parent networks. For each patient, TS was confirmed using a standard cytogenetic karyotype assessment with peripheral blood. In the TS group, 21 had a nonmosaic 45XO karyotype; 13 patients showed mosaicism with 45XO and the other cell line with the presence of the second X chromosome. For the mosaic TS patients, the percentage of peripheral blood cells with the 45XO karyotype differed across subjects (range: 17–77%; mean: 44%; standard deviation: 18%). All of the TS patients showed defective ovarian development, which was verified via pelvic ultrasound tests. Among the TS patients, 29 (19 nonmosaic and 10 mosaic) were on growth hormone (GH) treatment and only 6 (4 nonmosaic and 2 mosaic) were on estrogen replacement (ER). All participants were screened for medical history to ensure that there was no evidence of current or past major neurological or psychiatric disorders. Additionally, there were no visible abnormalities (e.g., white matter hypointensity) on the MR images, which were examined by an experienced radiologist. For each participant, traveling and accommodation expenses for participating in this study were reimbursed. The research protocol was approved by the Research Ethics Committee of the Beijing Normal University. For each participant, informed written consent was obtained from her legal guardian.

Cognitive Assessment

For each participant, the cognitive assessments were performed within 2 days prior to or after the MRI scan. The participants aged 6–16 years were assessed with the Chinese version of Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV). A total of 5 composite scores were generated using the WISC-IV: full scale intelligence quotient (FSIQ), verbal comprehension index (VCI), perceptual reasoning index (PRI), processing speed index (PSI) and working-memory index (WMI).

Given that math deficiency has been consistently reported in TS, we further tested the participants using 3 math tasks: number comparison, numerosity comparison, and simple subtraction (Wei et al. 2012). The tasks have been programed using web-based applications (http://www.dweipsy.com/lattice/) and therefore were performed online. For each math task, we used the number of correct responses per minute as the cognitive measure.

MRI Acquisition

All MRI scans were performed on the same 3T Siemens Tim Trio MRI scanner in the Imaging Center for Brain Research, Beijing Normal University. For each participant, the head was secured using straps and foam pads to minimize head movement. High-resolution 3D T1-weighted images were sagittally acquired using a magnetization prepared rapid gradient echo (MPRAGE) sequence: 144 sagittal slices; echo time (TE), 3.39 ms; repletion time (TR), 2530 ms; inversion time (TI), 1100 ms; 1.33-mm slice thickness with no gap; acquisition matrix, 256 × 256; 1 × 1 mm in-plane resolution; acquisition time, 8:07 min. Diffusion MRI was axially applied using a single-shot echo planar imaging-based sequence: coverage of the whole brain; 62 axial slices; TR, 8000 ms; TE, 89 ms; 30 optimal nonlinear diffusion-weighted directions with b = 1000 s/mm2 and one additional image without diffusion weighting (i.e., b = 0 s/mm2); average, 2; 2.2-mm slice thickness; acquisition matrix, 128 × 128; 2.2 × 2.2 mm in-plane resolution; acquisition time, 9:08 min.

Image Processing

Cortical Thickness and Surface Area

Here, we used the CIVET pipeline to determine the regional thickness and area of the cortical surface, as previously described (Gong et al. 2012). Briefly, the native T1-weighted MR images were first linearly aligned in the stereotaxic space and corrected for nonuniformity artifacts using the N3 algorithm (Sled et al. 1998). The resultant images were further segmented into gray matter, white matter, and cerebrospinal fluid (Zijdenbos et al. 2002; Tohka et al. 2004). Next, the inner and outer gray matter surfaces were automatically extracted for each hemisphere using the CLASP algorithm (MacDonald et al. 2000; Kim et al. 2005). The individual surfaces were further aligned with a surface template to enable comparisons at corresponding vertices across subjects. The cortical thickness was measured between the 2 surfaces at 40 962 vertices per hemisphere using the linked distance in the native space (Lerch and Evans 2005). The middle cortical surface, defined at the geometric center between the inner and outer cortical surfaces, was used to calculate the cortical surface area in the native space (Lyttelton et al. 2009). According to the automated anatomical labeling (AAL) template (Tzourio-Mazoyer et al. 2002), the cortical surfaces for each hemisphere were parcellated into 39 distinct regions (Fig. 1A). For each cortical region, the mean thickness and total area were calculated as the morphological measures.

Figure 1.

Regional parcellation for the cortex and white matter. (A) The cortical parcellation map overlaid on the average surface. This parcellation (in total 78 cortical regions) was based on the automated anatomical labeling (AAL) template. (B) The white matter parcellation map (WMPM) overlaid on the average T1 image. The WMPM (in total 68 WM regions) included the “core white matter” as well as the reproducible blade-type white matter structures beneath the cortical gyri.

Figure 1.

Regional parcellation for the cortex and white matter. (A) The cortical parcellation map overlaid on the average surface. This parcellation (in total 78 cortical regions) was based on the automated anatomical labeling (AAL) template. (B) The white matter parcellation map (WMPM) overlaid on the average T1 image. The WMPM (in total 68 WM regions) included the “core white matter” as well as the reproducible blade-type white matter structures beneath the cortical gyri.

Volume of GM Sub-cortical Structures

We quantified the volume of the sub-cortical structures. Specifically, the FMRIB Integrated Registration and Segmentation Tool (FIRST) was employed to yield a closed mesh for each sub-cortical structure in the native space (Patenaude et al. 2011), thereby defining each structure by segmentation and enabling subsequent volume calculation. Here, a total of 14 sub-cortical structures were calculated: bilateral thalamus, caudate, putamen, pallidum, hippocampus, amygdala, and nucleus accumbens.

WM Diffusion Measures

Diffusion-weighted images were processed with the PANDA pipeline toolbox (Cui et al. 2013). Briefly, PANDA called the modules of the FMRIB Software Library (FSL) to finish the skull-stripping, simple-motion and eddy-current correction, diffusion tensor/parameter calculation, and spatial normalization (Jenkinson et al. 2012). For analysis, the 2 most commonly used diffusion parameters, fractional anisotropy (FA) and mean diffusivity (MD), were chosen (Beaulieu 2002). Here, we conducted an analysis at the regional level using the White Matter Parcellation Map (WMPM) (Mori et al. 2008). Specifically, a total of 68 WMPM regions were chosen (Fig. 1B), including the “core white matter” as well as the reproducible blade-type white matter structures beneath the cortical gyri (Mori et al. 2008; Oishi et al. 2008). The remaining peripheral WM regions near the cortex were excluded because they are highly variable across individuals. For each WMPM region, the mean FA and MD were calculated as the connectivity measures.

Statistical Analysis

To assess the differences between groups in age and intelligence quotient (IQ) scores, we used a general linear model (GLM) with the group (healthy control, mosaic TS, and nonmosaic TS) as a main factor. For the scores in math-related tasks, age was included as a covariate in the model. For the group effects of age and the cognitive scores, P < 0.05 was considered significant. If a group effect was found to be significant, post hoc pairwise comparisons were further applied with the Bonferroni correction.

For each brain measure, we first tested if there was a global effect of X chromosome loss. Specifically, a repeated-measures GLM was applied in which the group was used as a main factor and the region was treated as a repeated factor. For the main group effect, P < 0.05 was considered significant, indicating a global effect of X chromosome loss on the brain measure. Post hoc pairwise comparisons with the Bonferroni correction were further applied if a significant main group effect was found. In the statistical models, age was included as a covariate. Note that the interaction term “age × group” was first included but showed no significance for all measures; therefore, this term was excluded from the final model (Engqvist 2005). To control for the effect of brain size in cortical thickness, surface area, and sub-cortical volume analyses, the statistical models also included the whole-brain volume as a covariate.

We further tested the “region × group” interaction in the above repeated-measures GLM. A significant “region × group” interaction here indicates a difference of the group effect between regions, therefore implying a spatially localized effect of the X chromosome loss. In this case, to identify the localized spatial pattern of the effect, the brain measure for each region (i.e., the cortical thickness/surface area for each AAL cortical region, the volume for each sub-cortical structure, or the FA/MD for each WMPM region) were compared between groups, respectively. Likewise, age was included as a covariate in all statistical models, and the whole-brain volume was controlled when analyzing the cortical thickness, surface area, and sub-cortical volume. For each brain measure, the false discovery rate (FDR) procedure was performed to correct for multiple comparisons across different regions, and q < 0.05 (i.e., FDR corrected P < 0.05) was chosen as the level of significance (Genovese et al. 2002). If a region exhibited a significant main group effect (i.e., q < 0.05), post hoc pairwise comparisons were further applied using the Bonferroni correction.

To determine if the X chromosome modulates brain–cognition relations, we tested the “brain measure × group” interaction on each of the cognitive items. This interaction represents the group difference in the regression slopes between the brain measures (e.g., cortical thickness, surface area, sub-cortical volume, FA, or MD) and cognitions. Additionally, all statistical models included age as a covariate, and the whole-brain volume was further included as a covariate for cortical thickness, surface area, and sub-cortical volume analyses. Similarly, to correct for multiple comparisons across different regions, the FDR procedure was applied for each brain measure, and q < 0.05 was considered statistically significant.

Results

Demographics and Cognitive Assessment

The results of demographics and cognitive assessment are summarized in Table 1. There was no significant difference in age between groups (P = 0.89). The GLM on the cognitive scores revealed significant group effects (P < 0.05, see Table 1) on the IQ scores and number/numerosity comparison tasks. A significant group effect trend was observed for the simple subtraction task (P = 0.06).

Table 1

Demographics and cognitive testing

 Healthy control (n = 21) Mosaic TS (n = 13) Nonmosaic TS (n = 21) Group effect (P-value) Post hoc pairwise comparison (Bonferroni-corrected P-value)
 
HC/mTS HC/nTS mTS/nTS 
Age (years) 14.0 ± 2.2 14.4 ± 2.2 14.2 ± 2.8 0.89 — — — 
GH use — 10 19 — — — — 
ER use — — — — — 
IQ scales 
 FSIQ 109.2 ± 15.3 (20) 89.6 ± 17.0 (10) 89.1 ± 16.3 (16) 0.0005 0.009 0.002 1.00 
 WMI 101.7 ± 15.7 (20) 92.7 ± 14.6 (10) 88.3 ± 16.1 (16) 0.04 0.42 0.03 1.00 
 VCI 117.2 ± 14.2 (20) 102.6 ± 19.5 (10) 103.0 ± 20.6 (16) 0.03 0.12 0.06 1.00 
 PRI 103.4 ± 13.4 (20) 84.1 ± 17.3 (10) 84.3 ± 14.1 (16) 0.0003 0.003 0.0009 1.00 
 PSI 103.8 ± 16.1 (20) 83.9 ± 10.5 (10) 84.5 ± 11.8 (16) 0.0001 0.002 0.0003 1.00 
Math tasks 
 Number comparison 56.9 ± 11.8 (20) 55.5 ± 9.4 (13) 42.2 ± 17.2 (20) 0.001 1.00 0.002 0.02 
 Numerosity comparison 26.6 ± 6.0 (20) 21.4 ± 7.0 (13) 20.5 ± 6.6 (20) 0.006 0.03 0.009 1.00 
 Simple subtraction 44.2 ± 7.6 (20) 39.2 ± 10.1 (13) 37.0 ± 11.4 (20) 0.06 — — — 
 Healthy control (n = 21) Mosaic TS (n = 13) Nonmosaic TS (n = 21) Group effect (P-value) Post hoc pairwise comparison (Bonferroni-corrected P-value)
 
HC/mTS HC/nTS mTS/nTS 
Age (years) 14.0 ± 2.2 14.4 ± 2.2 14.2 ± 2.8 0.89 — — — 
GH use — 10 19 — — — — 
ER use — — — — — 
IQ scales 
 FSIQ 109.2 ± 15.3 (20) 89.6 ± 17.0 (10) 89.1 ± 16.3 (16) 0.0005 0.009 0.002 1.00 
 WMI 101.7 ± 15.7 (20) 92.7 ± 14.6 (10) 88.3 ± 16.1 (16) 0.04 0.42 0.03 1.00 
 VCI 117.2 ± 14.2 (20) 102.6 ± 19.5 (10) 103.0 ± 20.6 (16) 0.03 0.12 0.06 1.00 
 PRI 103.4 ± 13.4 (20) 84.1 ± 17.3 (10) 84.3 ± 14.1 (16) 0.0003 0.003 0.0009 1.00 
 PSI 103.8 ± 16.1 (20) 83.9 ± 10.5 (10) 84.5 ± 11.8 (16) 0.0001 0.002 0.0003 1.00 
Math tasks 
 Number comparison 56.9 ± 11.8 (20) 55.5 ± 9.4 (13) 42.2 ± 17.2 (20) 0.001 1.00 0.002 0.02 
 Numerosity comparison 26.6 ± 6.0 (20) 21.4 ± 7.0 (13) 20.5 ± 6.6 (20) 0.006 0.03 0.009 1.00 
 Simple subtraction 44.2 ± 7.6 (20) 39.2 ± 10.1 (13) 37.0 ± 11.4 (20) 0.06 — — — 

The parentheses after the cognitive scores represent the number of subjects who successfully performed the cognitive test. The Bonferroni correction was further applied for the post hoc pairwise comparisons if an overall group effect was found to be significant. GH, growth hormone; ER, estrogen replacement; HC, healthy control; mTS, mosaic Turner syndrome; nTS, nonmosaic Turner syndrome; FSIQ, full scale intelligence quotient; WMI, working-memory index; VCI, verbal comprehension index; PRI, perceptual reasoning index; PSI, processing speed index.

Regarding the post hoc comparisons, the nonmosaic TS subjects had significantly lower IQ score values than the healthy controls (HC), with the exception of the VCI (Bonferroni corrected P = 0.06). The mosaic TS subjects scored lower than the HC on the FSIQ (Bonferroni corrected P = 0.009), PRI (Bonferroni corrected P = 0.003) and PSI (Bonferroni corrected P = 0.002). The 2 TS groups did not differ significantly regarding the 5 IQ scores. For the 2 math-related tasks showing a significant group effect, the nonmosaic TS subjects performed significantly worse than the HC in both the number comparison (Bonferroni corrected P = 0.002) and numerosity comparison (Bonferroni corrected P = 0.009). The mosaic TS subjects scored lower than the HC only in the numerosity comparison (Bonferroni corrected P = 0.03). The mosaic TS subjects outperformed the nonmosaic TS subjects only in the number comparison (Bonferroni corrected P = 0.02).

The X Chromosome Effects on GM Morphology

The whole-brain volume did not differ between groups (P = 0.45). The repeated-measures GLM revealed a significant main group effect on cortical thickness (P = 0.01). The post hoc comparisons indicated a significant difference only between the HC and nonmosaic TS subjects (Bonferroni corrected P = 0.01). A significant “region × group” interaction on cortical thickness was observed (P < 0.001), suggesting a spatially localized effect. Furthermore, the GLM analysis at the regional level (78 AAL regions in total) revealed significant group effects on thickness in 8 cortical regions (FDR corrected P < 0.05, see Supplementary Table 1), including the left/right inferior temporal gyrus, left/right middle temporal gyrus, right lingual gyrus, right superior temporal gyrus, right posterior cingulate gyrus, and right precuneus, as illustrated in Figure 2A. The post hoc comparisons found that nonmosaic TS had a greater thickness in each of the 8 regions than HC (Bonferroni corrected P < 0.05) but showed no significant difference with mosaic TS. Additionally, mosaic TS had a greater thickness than HC in these regions, with the exception of the right precuneus (Bonferroni corrected P = 0.52) and right middle temporal gyrus (Bonferroni corrected P = 0.12).

Figure 2.

Cortical regions showing significant group differences in cortical thickness or surface area. (A) Cortical thickness; (B) Surface area. For both A and B, the first row represents the main group effect, and the next 3 rows indicate the post hoc comparison of HC versus nTS, HC versus mTS, and mTS versus nTS, respectively. In the first row, the color represents the F statistic for the main group effect. In the other rows, the color indicates the T statistic for the pair-wise comparison. HC, healthy control; nTS, nonmosaic Turner syndrome; mTS, mosaic Turner syndrome.

Figure 2.

Cortical regions showing significant group differences in cortical thickness or surface area. (A) Cortical thickness; (B) Surface area. For both A and B, the first row represents the main group effect, and the next 3 rows indicate the post hoc comparison of HC versus nTS, HC versus mTS, and mTS versus nTS, respectively. In the first row, the color represents the F statistic for the main group effect. In the other rows, the color indicates the T statistic for the pair-wise comparison. HC, healthy control; nTS, nonmosaic Turner syndrome; mTS, mosaic Turner syndrome.

Regarding the cortical surface area, the repeated-measures GLM also showed a significant group effect (P < 0.001), and the post hoc comparisons indicated that the HC had significantly larger surface area than both mosaic (Bonferroni corrected P < 0.001) and nonmosaic TS subjects (Bonferroni corrected P < 0.001); the 2 TS groups did not differ significantly (Bonferroni corrected P = 0.09). As well, there was a significant “region × group” interaction (P < 0.001). The regional GLM analyses revealed significant group effects in 9 regions (FDR corrected P < 0.05): left/right precuneus, left/right cuneus, left/right calcarine fissures and surrounding cortex, left/right superior occipital gyrus, and right angular gyrus (Fig. 2B and Supplementary Table 2). With the exception of the left superior occipital gyrus, right precuneus, and right angular gyrus, the HC had a larger surface area than both TS groups in the remaining 6 cortical regions (Bonferroni corrected P < 0.05); no significant differences in these regions were found between the 2 TS groups. For the left superior occipital gyrus and right precuneus, the HC had a significantly larger surface area than the nonmosaic TS (Bonferroni corrected P < 0.05) subjects, and the mosaic TS subjects did not differ from either the HC or nonmosaic TS groups in this regard. In contrast, for the right angular gyrus, there was no significant difference between the HC and nonmosaic TS subjects, both of which showed a larger surface area than the mosaic TS subjects (Bonferroni corrected P < 0.05).

The repeated-measures GLM revealed a significant main group effect on the sub-cortical volume (P = 0.02), and the post hoc comparisons found a significant difference only between the HC and nonmosaic TS groups (Bonferroni corrected P = 0.02). However, no significant “region × group” interaction was observed here (P = 0.11), suggesting a diffuse effect of the X chromosome loss on the sub-cortical volume. Therefore, we did not perform further regional GLM analysis on each sub-cortical structure, separately.

The X Chromosome Effects on WM Connectivity

First, the repeated-measures GLM revealed a significant main group effect on both FA (P < 0.001) and MD (P < 0.001). The post hoc comparisons found that the HC had a significantly higher FA and lower MD than both mosaic (FA: Bonferroni corrected P = 0.002; MD: Bonferroni corrected P = 0.02) and nonmosaic TS subjects (FA: Bonferroni corrected P < 0.001; MD: Bonferroni corrected P < 0.001), but the 2 TS groups did not differ. A significant “region × group” interaction was found for FA (P < 0.001) but not for MD (P = 0.76). This implied that the effect of X chromosome loss was spatially localized for FA, but was spatially diffuse for MD. Consequently, separate GLM analysis was applied to each WMPM region (68 in total) only for FA, and the results are summarized in Supplementary Tables 3. Specifically, FA showed a significant group effect in 45 WMPM regions (FDR corrected P < 0.05), as illustrated in Figure 3. Notably, among the WMPM regions showing significant group effects, the strongest effect primarily involved the WM tracts/regions connecting or adjacent to the temporal, occipital, and parietal cortices. The top 5 regions with the greatest effect on FA were the left/right temporal blade, left/right occipital blade, and right superior parietal blade (Table 2).

Table 2

The top 5 WMPM regions showing the strongest group effects for FA

WMPM regions Healthy control (n = 21) Mosaic TS (n = 13) Nonmosaic TS (n = 21) Group effect (q-value × 10−4Post hoc pairwise comparisons (Bonferroni-corrected P-value)
 
HC/mTS HC/nTS mTS/nTS 
Temporal blade (R) 0.43 ± 0.02 0.40 ± 0.02 0.40 ± 0.01 0.02 0.00 0.00 1.00 
Temporal blade (L) 0.44 ± 0.02 0.42 ± 0.02 0.41 ± 0.02 0.03 0.00 0.00 0.21 
Superior parietal blade (R) 0.42 ± 0.02 0.39 ± 0.02 0.39 ± 0.02 0.32 0.00 0.00 0.84 
Occipital blade (R) 0.39 ± 0.02 0.37 ± 0.02 0.37 ± 0.02 0.37 0.00 0.00 1.00 
Occipital blade (L) 0.40 ± 0.02 0.39 ± 0.02 0.38 ± 0.02 0.53 0.01 0.00 0.33 
WMPM regions Healthy control (n = 21) Mosaic TS (n = 13) Nonmosaic TS (n = 21) Group effect (q-value × 10−4Post hoc pairwise comparisons (Bonferroni-corrected P-value)
 
HC/mTS HC/nTS mTS/nTS 
Temporal blade (R) 0.43 ± 0.02 0.40 ± 0.02 0.40 ± 0.01 0.02 0.00 0.00 1.00 
Temporal blade (L) 0.44 ± 0.02 0.42 ± 0.02 0.41 ± 0.02 0.03 0.00 0.00 0.21 
Superior parietal blade (R) 0.42 ± 0.02 0.39 ± 0.02 0.39 ± 0.02 0.32 0.00 0.00 0.84 
Occipital blade (R) 0.39 ± 0.02 0.37 ± 0.02 0.37 ± 0.02 0.37 0.00 0.00 1.00 
Occipital blade (L) 0.40 ± 0.02 0.39 ± 0.02 0.38 ± 0.02 0.53 0.01 0.00 0.33 

The q-values represent the corrected P-values after the FDR correction for multiple comparisons across different regions when testing the group effects (Genovese et al. 2002). The Bonferroni correction was further applied for the post hoc pairwise comparisons if an overall group effect was found to be significant. WMPM, white matter parcellation map (Mori et al. 2008); HC, healthy control; mTS, mosaic Turner syndrome; nTS, nonmosaic Turner syndrome; (L), left side; (R), right side.

Figure 3.

WMPM regions showing significant group differences in FA. The first row represents the main group effect, and the next 3 rows indicate the post hoc comparison of HC versus nTS, HC versus mTS, and mTS versus nTS, respectively. In the first row, the color represents the F statistic for the main group effect. In the other rows, the color indicates the T statistic for the pair-wise comparison. HC, healthy control; nTS, nonmosaic Turner syndrome; mTS, mosaic Turner syndrome.

Figure 3.

WMPM regions showing significant group differences in FA. The first row represents the main group effect, and the next 3 rows indicate the post hoc comparison of HC versus nTS, HC versus mTS, and mTS versus nTS, respectively. In the first row, the color represents the F statistic for the main group effect. In the other rows, the color indicates the T statistic for the pair-wise comparison. HC, healthy control; nTS, nonmosaic Turner syndrome; mTS, mosaic Turner syndrome.

Among the 53 WMPM regions showing significant group effects on FA (FDR corrected P < 0.05), the post hoc comparisons indicated that the nonmosaic TS group had a lower FA than the HC in 50 regions but showed a lower FA than the mosaic TS subjects in only the left tapetum (Fig. 3A). Additionally, the mosaic TS subjects had a significantly lower FA than the HC in 35 of the 53 WMPM regions.

As a validation analysis, we additionally tested the main group effects on the cortical thickness/surface area at the vertex level and the FA/MD at the voxel level. As illustrated in Supplementary Figure 1, the results at the vertex/voxel level were highly convergent with the current findings at the regional level (Figs 2 and 3).

X Chromosome Effects on the Brain–cognition Relationship

To determine the effect of X chromosome loss on the brain–cognition relationship, the “brain measure × group” interaction was tested for each of the cognitive items. A significant interaction here indicated a significant difference in regression slopes for the brain measures (e.g., cortical thickness, surface area, sub-cortical volume, FA, and MD) between groups. As listed in Table 3, there were significant “brain × group” interactions (FDR corrected P < 0.05) for 3 IQ scores (i.e., FSIQ, PRI, and WMI) but not for any of the math task scores. Specifically, we found a significant “thickness × group” interaction on FSIQ in 3 cortical regions (right middle frontal gyrus, right superior dorsolateral frontal gyrus, and left rolandic operculum) on PRI in the right middle frontal gyrus and on WMI in 16 cortical regions (Table 3). Additionally, a significant “area × group” interaction on FSIQ was also observed in the left middle temporal pole. No sub-cortical structures showed a significant “volume × group” interaction on any of the cognitive scores, and no WMPM regions had a significant “FA × group” or MD × group” interaction. As illustrated in Figure 4 and Supplementary Figure 2, for the cortical regions with a significant “thickness/area × group” interaction, IQ scores correlated positively with thickness/area in the HC group but negatively in the 2 TS groups. The patterns of the brain–cognition relationship were largely similar among the 2 TS groups.

Table 3

The significant “brain measure × group” interactions for the cognitive scores

Cognition Cortical regions Thickness × group
 
Area × group
 
F q-value F q-value 
FSIQ Middle temporal pole (L) NS NS 9.38 0.038 
Middle frontal gyrus (R) 11.93 0.008 NS NS 
Superior dorsolateral frontal gyrus (R) 8.22 0.042 NS NS 
Rolandic operculum (L) 7.58 0.044 NS NS 
PRI Middle frontal gyrus (R) 9.62 0.032 NS NS 
WMI Middle frontal gyrus (R) 12.36 0.006 NS NS 
Precentral gyrus (L) 10.48 0.008 NS NS 
Superior dorsolateral frontal gyrus (R) 10.01 0.008 NS NS 
Rolandic operculum (L) 8.79 0.014 NS NS 
Precuneus (R) 7.70 0.021 NS NS 
Inferior parietal gyrus (L) 7.63 0.021 NS NS 
Angular gyrus (L) 7.27 0.024 NS NS 
Inferior occipital gyrus (L) 6.78 0.027 NS NS 
Superior dorsolateral frontal gyrus (L) 6.69 0.027 NS NS 
Superior temporal gyrus (L) 6.61 0.027 NS NS 
Median cingulate gyri (L) 6.39 0.028 NS NS 
Precentral gyrus (R) 6.31 0.028 NS NS 
Supramarginal gyrus (L) 5.90 0.035 NS NS 
Supramarginal gyrus (R) 5.34 0.048 NS NS 
Cuneus (L) 5.27 0.048 NS NS 
Orbital superior frontal gyrus (R) 5.23 0.048 NS NS 
Cognition Cortical regions Thickness × group
 
Area × group
 
F q-value F q-value 
FSIQ Middle temporal pole (L) NS NS 9.38 0.038 
Middle frontal gyrus (R) 11.93 0.008 NS NS 
Superior dorsolateral frontal gyrus (R) 8.22 0.042 NS NS 
Rolandic operculum (L) 7.58 0.044 NS NS 
PRI Middle frontal gyrus (R) 9.62 0.032 NS NS 
WMI Middle frontal gyrus (R) 12.36 0.006 NS NS 
Precentral gyrus (L) 10.48 0.008 NS NS 
Superior dorsolateral frontal gyrus (R) 10.01 0.008 NS NS 
Rolandic operculum (L) 8.79 0.014 NS NS 
Precuneus (R) 7.70 0.021 NS NS 
Inferior parietal gyrus (L) 7.63 0.021 NS NS 
Angular gyrus (L) 7.27 0.024 NS NS 
Inferior occipital gyrus (L) 6.78 0.027 NS NS 
Superior dorsolateral frontal gyrus (L) 6.69 0.027 NS NS 
Superior temporal gyrus (L) 6.61 0.027 NS NS 
Median cingulate gyri (L) 6.39 0.028 NS NS 
Precentral gyrus (R) 6.31 0.028 NS NS 
Supramarginal gyrus (L) 5.90 0.035 NS NS 
Supramarginal gyrus (R) 5.34 0.048 NS NS 
Cuneus (L) 5.27 0.048 NS NS 
Orbital superior frontal gyrus (R) 5.23 0.048 NS NS 

The q-values represent the corrected P-values after the FDR correction for multiple comparisons across different regions when testing the “brain measure × group” effects (Genovese et al. 2002). FSIQ, full scale intelligence quotient; WMI, working-memory index; PRI, perceptual reasoning index; (L), left side; (R), right side; NS, not significant.

Figure 4.

Cortical regions showing significant brain × group interactions on the cognitive scores. (A) The regions showing cortical thickness × group interactions with regard to FSIQ. (B) The regions showing cortical thickness × group interactions with regard to the PRI. (C) The regions showing cortical thickness × group interactions with regard to the WMI. (D) The regions showing surface area × group interactions with regard to FSIQ. The color on the cortical regions represents the F value for the corresponding interaction. Due to limited space, the scatter plot was provided only for the region with the most significant interaction. The selected region is indicated by the blue arrow on the surface. The scatter plots for all significant regions are present in Supplementary Figure 2. FSIQ, full scale IQ; PRI, perceptual reasoning index; WMI, working-memory index.

Figure 4.

Cortical regions showing significant brain × group interactions on the cognitive scores. (A) The regions showing cortical thickness × group interactions with regard to FSIQ. (B) The regions showing cortical thickness × group interactions with regard to the PRI. (C) The regions showing cortical thickness × group interactions with regard to the WMI. (D) The regions showing surface area × group interactions with regard to FSIQ. The color on the cortical regions represents the F value for the corresponding interaction. Due to limited space, the scatter plot was provided only for the region with the most significant interaction. The selected region is indicated by the blue arrow on the surface. The scatter plots for all significant regions are present in Supplementary Figure 2. FSIQ, full scale IQ; PRI, perceptual reasoning index; WMI, working-memory index.

Finally, to evaluate the effects of covarying the age and the whole-brain volume, we reran all analyses after excluding them from our statistical model when applicable. The results are highly consistent with our current findings (data not shown).

Discussion

Using a cohort of adolescent mosaic and nonmosaic TS patients and controls, the present study performed a comprehensive investigation to reveal the role of the X chromosome in brain morphology and connectivity and their relationships with cognition. Intriguingly, the comparative analyses found significant “X chromosome dosage effects”, that is, differences between the mosaic and nonmosaic TS groups in the cortical surface area in the right angular gyrus, as well as in WM integrity of the left tapetum of corpus callosum. Furthermore, the results demonstrated that the X chromosome plays a significant role in modulating the relationship between cortical morphology and the WMI in multiple cortical regions such as the right middle frontal gyrus and the right superior dorsolateral frontal gyrus. These findings provide novel information for the role of the X chromosome on human neuroanatomy and cognition during development, which has great implication for understanding the sex difference in brain and cognition.

Neuroanatomical Differences Between TS Patients and Healthy Controls During Adolescence

For genetic disorders such as TS, it is important to separately analyze the 2 lower-order components of cortical volume, cortical thickness, and surface area, because the 2 components have shown independent genetic determinants (Panizzon et al. 2009). In the present study, the observed increases in cortical thickness but decreases in surface area for the TS patients are highly convergent with previous findings (Raznahan et al. 2010; Lepage, Mazaika, et al. 2013; Lepage, Hong, et al. 2013). Specifically, the increased cortical thickness in the TS subjects was primarily located around the bilateral dorsolateral temporal lobes, which is likely to be the reason for the enlargement of temporal lobe cortical volume (Kesler et al. 2003; Rae et al. 2004). In contrast, the decreases in surface area primarily affected the parieto-occipital lobes; this was likely a main driving factor for the parieto-occipital GM reduction in the TS subjects (Reiss et al. 1995; Brown et al. 2002; Molko et al. 2004; Marzelli et al. 2011). Notably, the cortical spatial pattern of the TS-associated changes were quite different between the thickness and surface area, which further emphasizes different genetic basis for the 2 cortical measures and favors separate analysis on cortical thickness and surface area for genetic analysis on cortical morphology.

In addition to the GM morphology, 2 DTI studies reported FA or MD changes in specific WM tracts (such as the superior longitudinal fasciculus in TS patients) compared with healthy controls (Holzapfel et al. 2006; Yamagata et al. 2012). The present study observed a more diffusive pattern of disrupted WM integrity (i.e., decreased FA and increased MD) in both of the TS groups. Notably, despite the diffusive change pattern, the strongest X chromosome effect primarily involved the tracts/regions connecting or adjacent to the temporal, occipital, and parietal cortices. Together with cortical morphology findings, it appears that the loss of the X chromosome primarily influences the parietal-temporal-occipital neural system. However, it remains to be determined whether the abnormalities in GM and WM observed here are caused independently or have a causal relationship. Putatively, structural anomalies in both GM and WM should jointly underlie the abnormalities in functional activity and connectivity in TS patients (Molko et al. 2003; Hart et al. 2006; Bray et al. 2011,, 2013).

“X chromosome Dosage Effects” on Neuroanatomical Phenotypes During Adolescence

The present study was the first to include mosaic TS patients as an independent group when studying cortical morphology and WM connectivity; this method enabled the testing of the “X chromosome dosage effect” on these brain measures. As proposed previously (Murphy et al. 1993,, 1997), a significant difference between mosaic and nonmosaic TS indicates an “X chromosome dosage effect”, which suggests a phenotypic dependence on the X chromosome dosage. The lack of such a dosage effect implies a binary/categorical consequence of X chromosome loss, reflecting nonspecific anatomical responses to genomic effects of altered X chromosome dosage.

Among the GM measures, only the surface area of the right angular gyrus exhibited a significant difference between the 2 TS groups, with a similar trend observed in the left superior occipital gyrus (uncorrected P < 0.05). Accumulating evidence has demonstrated that the angular gyrus supports very complex brain functions and is involved in multiple high-level cognitive processes such as language, math, and memory (Seghier 2013). Both structural and functional alterations in the angular gyrus have been repeatedly found in TS (Haberecht et al. 2001; Molko et al. 2003; Kesler et al. 2006), suggesting a link between this structure and the X chromosome. The observed “X chromosome dosage effect” here sheds further light on the relationship between the X chromosome and this structure. Intriguingly, the direction of the group differences was unexpected to some degree: the surface area of the right angular gyrus in the mosaic TS subjects was smaller than those of both the control and nonmosaic TS subjects. This finding suggests that neuroanatomical changes do not necessarily follow a monotonic pattern as the X chromosome loss increases. In contrast, the surface area of the left superior occipital gyrus in the mosaic TS subjects was intermediate between that of the control and nonmosaic TS; this finding was compatible with a linear function of the X chromosome dosage and brain structure in this scenario. Given its significant role in visuo-spatial processing (Kesler et al. 2004), the smaller area of the left superior occipital gyrus may relate to the less severe visuo-spatial impairment in mosaic TS compared with nonmosaic TS (Rovet 2004).

Among the WM measures, only the FA of the left tapetum of corpus callosum exhibited an “X chromosome dosage effect”: the nonmosaic TS group showed a deceased FA compared with the mosaic TS group. This suggests a positive effect of the X chromosome dosage on WM integrity. Given the X dosage effect on the corpus callosum, an inferior interhemispheric communication was expected in nonmosaic TS, which may be associated with worse performance in most cognitive tasks compared with mosaic TS (Rovet 2004).

Finally, it should be noted that Murphy et al. (1993) reported “X chromosome dosage effects” on the lenticular and thalamic nuclei volume, which the present study failed to detect. These discrepant results may be due to the differences in the age range of samples (adults vs. adolescents), neuroimaging acquisition techniques or methods of analysis.

The X Chromosome's Role in the Brain–cognition Relationship During Adolescence

Intriguingly, the current study observed significant changes in the relationship between cortical morphology and IQ scores in specific cortical regions, as indicated by significant “cortical thickness/surface area × group” interactions for the IQ scores. These results suggest that some genes on the X chromosome may act as modulators in the brain–cognition relationship. Note that the alteration of the brain–cognition relationship does not necessarily mean a significant group change in brain measures and vice versa. This finding is of particular implication for cognitive studies, in which the same brain–cognition relation is typically presumed across both healthy and patient populations.

Specifically, the majority of detected differences in the brain–cognition relationship between TS and controls are between cortical thickness and the WMI of the IQ test. The alterations of the thickness–WMI relationship were primarily located in the association cortex (locations such as the right middle frontal gyrus, right superior dorsolateral frontal gyrus, and left inferior parietal gyri, most of which have been previously reported as related to working memory) (Baddeley 2003). We also observed changes in the thickness–FSIQ relationship in the right middle frontal gyrus, right superior dorsolateral frontal gyrus, and left rolandic operculum, which are likely attributable to the detected changes in the thickness–WMI relationship in these regions (given the substantial contribution of WMI to the FSIQ score).

In healthy girls, both the cortical thickness and surface area showed a positive correlation with IQ; this finding was compatible with previous IQ studies (Shaw et al. 2006). However, these relationships were consistently reversed in both the mosaic and nonmosaic TS patients: the IQ scores increases with reductions in thickness. This negative correlation in TS patients is compatible with the group differences between TS patients and controls (where TS patients had an increased thickness but a decreased IQ score). The direction of the brain–cognition relationship did not differ between the mosaic and nonmosaic TS subjects, though the slopes differed in a couple of regions, such as the left middle cingulate gyrus. The dramatic alterations in the brain–cognition relationship due to X chromosome loss highlight the necessity of taking the brain–gene interactions into account when predicting human cognition abilities (Schmidt et al. 2009). Particularly, more attention should be paid to the role of genetic factors on the brain–cognition relationship in the context of understanding cognitive profiles of brain diseases (especially the genetic ones).

Direct Genetic Effect or Indirect Hormonal Effect

X-linked genes are known to affect the brain at least in 2 ways: by directly acting on the brain and by indirectly acting on the gonads to induce differences in specific gonadal secretions (i.e., hormones) that have specific effects on the brain (Arnold 2004). To isolate the direct genetic effect from the indirect hormonal effect, one possible approach is to ensure identical hormonal levels across individuals with different X-linked genotypes. However, hormonal deficits due to gonadal dysgenesis are extremely common in TS; therefore, in our case, it is difficult to differentiate between the direct genetic effect of the X chromosome and the indirect hormonal effect on the brain. Our observed neuroanatomical and cognitive phenotypes in TS patients could be due to a direct genetic factor, an indirect hormonal factor, or a combination of the 2.

Although identical hormone levels between adolescent TS patients and healthy controls are difficult to achieve in practice, a suboptimal alternative is to match the pubertal stage, as an approximate for the sex hormone level, between groups. Unfortunately, despite of the age range from 9 to 18 years, the majority of TS patients in the present study were at the prepubertal stage (i.e., pubertal stage I) because spontaneous puberty development is very rare in TS girls (Pasquino et al. 1997; Bannink et al. 2009), and most of our TS patients did not undergo ER to artificially induce puberty development.

Nonetheless, we reanalyzed the data with only subjects at the prepubertal stage (age: 9–12 years), including 8 controls, 8 nonmosaic, and 3 mosaic TS patients. This additional analysis approximately ensured the matching in both age and pubertal status between the TS patients and controls. Intriguingly, the spatial patterns of statistical results for the pre-pubertal stage (data not shown) are largely similar with those from the entire cohort, favoring a direct genetic effect for our current findings. A larger cohort matching for both age and pubertal status between TS patients and controls is desired to confirm our findings in the future.

While animal models are essential to dissociate the genetic and hormonal effects (Arnold and Chen 2009; Raznahan et al. 2013), other human MRI studies have also provided important clues on this issue. For example, cortical thinning of the temporal cortex has been found in 47XXY men compared with 46XX women and 46XY men (Savic and Arver 2014). This finding is reciprocal to the comparatively thickening temporal cortex found in 45XO girls. Given that the sex steroids are low in both 47XXY males and 45XO females, a direct genetic effect on the thickness of the temporal cortex is more likely. Moreover, the observed neuroanatomical differences between the nonmosaic and mosaic TS patients (i.e., “X chromosome dosage effect”) imply a direct genetic effect: both TS had gonadal dysgenesis but had different amounts of the X chromosome (Murphy et al. 1997).

However, a few studies have also demonstrated significant correlations between hormone levels and neuroanatomical phenotypes such as the GM volume of the amygdala and parahippocampus (Lentini et al. 2013). Particularly, in summarizing MRI findings of the human brain, a recent review found consistent changes of the medial temporal lobe structures among different endocrine disorders with either sex steroid excess or deficiency (Mueller 2013), therefore supporting an indirect hormonal effect on related brain structures rather than a direct genetic effect.

Limitations

Finally, a few caveats need to be addressed. First, despite the scarceness of TS patients and a narrow age range limit, we collected a relatively large number of samples compared with other TS studies. However, the absolute sample size remains small. Additionally, the mosaic TS group had fewer samples than the other 2 groups, resulting in a difference in the statistical power between post hoc pairwise comparisons. Further, mosaic TS patients with similar proportions of cells missing the entire second X chromosome are difficult to match, given the limited number of volunteers available. Therefore, our current mosaic group was heterogeneous in terms of the cell proportion. Furthermore, while the mosaicism was confirmed using a peripheral blood sample, it remains unknown if a detected mosaicism in the blood can indicate a mosaicism in brain. Third, factors such as GH use, ER treatment, and X-linked imprinting may also influence the brain structures in TS (Kesler et al. 2003; Cutter et al. 2006; Lepage, Clouchoux, et al. 2013; Lepage, Hong, et al. 2013; Lepage, Hong, et al. 2012). Due to the limited sample size and the lack of related information, it is not feasible to evaluate their effects in the present study. Future studies with a large sample size are warranted to test these potential confounding factors. Lastly, by design, the present study focused on a limited age range of adolescence, which provides a valuable opportunity for understanding the X chromosome effects on the brain and cognitive development. Caution should be exercised when extrapolating these findings across the entire life span.

Conclusion

By showing differences between mosaic and nonmosaic TS patients, the present study revealed “X chromosome dosage effects” on cortical surface area and WM connectivity, supporting a link between the brain structural phenotypes and the type of X chromosome loss. Furthermore, the relationship between the cortical morphology and WMI exhibited dramatic alterations in both TS patient types in specific regions, suggesting that the X chromosome modulates specific brain–cognition relationships. These novel findings provide new insights into how the X chromosome affects the human brain, and suggest an important role of genetic factors in brain–cognition relationships.

Supplementary Material

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

Notes

The authors thank Prof. Alan Evans for providing the CIVET tool during the cortical morphological analysis. The authors thank Prof. Xinlin Zhou for the helps during the math-related cognitive testing. Also, the authors sincerely thank the 3 anonymous reviewers for their thoughtful and constructive comments. Conflict of Interest: None declared.

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