Sex differences in white matter development during adolescence: a DTI study
Yingying Wang 1, 4, 5*, Chris Adamson 6, Weihong Yuan 1, 2, Mekibib Altaye 3, Akila Rajagopal 1,
Anna W. Byars 4, Scott K. Holland 1, 2, 5
1. Pediatric Neuroimaging Research Consortium, 2. Department of Radiology, 3. Department of
Biostatistics and Epidemiology, 4. Division of Neurology, Cincinnati Children's Hospital,
Cincinnati, OH, United States
5. Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, United
6. Developmental and Functional Brain Imaging, Critical Care and Neurosciences, Murdoch
Children’s Research Institute, The Royal Children’s Hospital, Victoria, Australia
Pediatric Neuroimaging Research Center
MLC 5033, 3333 Burnet Avenue
Cincinnati, OH 45229-3039, United States
Adolescence is a complex transitional period in human development, composing physical
maturation, cognitive and social behavioral changes. The objective of this study is to
investigate sex differences in white matter development and the associations between
intelligence and white matter microstructure in the adolescent brain using diffusion tensor
imaging (DTI) and Tract-Based Spatial Statistics (TBSS). In a cohort of 16 typically-
developing adolescents aged 13 to 17 years, longitudinal DTI data were recorded from
each subject at two time points that were one year apart. We used TBSS to analyze the
diffusion indices including fractional anisotropy (FA), mean diffusivity (MD), axial
diffusivity (AD), and radial diffusivity (RD). Our results suggest that boys (13-18 years)
continued to demonstrate white matter maturation, whereas girls appeared to reach
mature levels earlier. In addition, we identified significant positive correlations between
FA and full-scale intelligence quotient (IQ) in the right inferior fronto-occipital fasciculus
when both sexes were looked at together. Only girls showed significant positive
correlations between FA and verbal IQ in the left cortico-spinal tract and superior
longitudinal fasciculus. The preliminary evidence presented in this study supports that
boys and girls have different developmental trajectories in white matter microstructure.
Keywords: adolescence, diffusion tensor imaging, sex differences, tract based spatial
statistics, white matter development
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Adolescence is a complex transitional time period composed of physical maturation,
cognitive development, emotional and social behavioral changes . During this period,
adulthood begins to emerge, childhood is left behind, but the decline of adult aging has
not yet begun . Meanwhile, the increased sexual dimorphism driven by pubertal
maturation may be the underlying cause of cognitive and emotional developmental
differences between boys and girls . Evidence from early brain morphology studies has
revealed greater total gray and white matter volumes in boys than girls , and significant
regional age-related increases in white matter volume of the left inferior frontal region in
boys but not in girls , as well as significant sex by age interaction for gray and white
matter volume . Clinical evidence has also demonstrated sex differences in adolescent
brain-behavior relationships . The early onset of puberty in girls has been associated with
later onset of schizophrenia . Adolescent boys with post-traumatic stress disorder have
shown more evidence of adverse brain development than girls . Adolescent girls have
been shown to be more vulnerable to disorders such as depression and anorexia . Thus,
understanding sexual dimorphism in normal brain maturation is crucial to develop a
better understanding of the mechanism underlying such neuropsychiatric disorders.
The advent of the diffusion tensor imaging (DTI) technique provides a new dimension for
understanding sexual dimorphism in brain maturation. DTI non-invasively measures
water diffusion within brain tissue, which reflects the organizational integrity of white
matter architecture and characteristics of fiber bundles in vivo . This technique provides
quantitative measures including fractional anisotropy (FA) as a normalized scalar
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measure of the degree of diffusion anisotropy, mean diffusivity (MD) as the magnitude of
diffusional motion, axial diffusivity (AD) as diffusion parallel to white matter tracts, and
radial diffusivity (RD) as diffusion perpendicular to white matter tracts . These measures
have been widely used in various clinical applications as markers of pathological changes
in axonal density and size, myelination, and fiber organization . DTI has also been
shown as a valuable imaging technique in studying characteristics of normal development
of white matter . In recent years, an increasing number of studies have demonstrated age-
related differences in FA and MD and evident sex differences in white matter
development during adolescence using regions of interest (ROI) , tractography , or voxel-
wise based analyses. Based on these DTI studies, age-related changes in white matter
microstructure have been well established and most consistently show linear increases in
FA and decreases in MD with age in white matter during adolescence, whereas the results
of sexual dimorphism and sex by age interaction in the white matter seem to be less
coherent. Bonekamp and his colleagues have reported there was no significant effect of
sex by age interaction in FA and MD . However, some other studies have demonstrated
significant sex by age interaction in various white matter tracts.
Recent DTI studies also have raised the recognition of the important role of white matter
in supporting neural circuits and cognitive function . Schmithorst and his colleagues
found a positive correlation between intelligence quotient (IQ) and FA in bilateral frontal
and occipito-parietal regions, suggesting a positive relationship between white matter
fiber organization with cognitive function . Clayden and his colleagues also have
demonstrated that FA and MD independently predict intelligence using a comprehensive
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data-driven approach based on principal components analysis . They found positive
loading in the splenium and left-side inferior longitudinal and arcuate fasciculi.
In this study, we investigated sex differences in white matter development and the
association between intelligence and white matter microstructure in the adolescent brain
using DTI and a novel Tract-Based Spatial Statistics (TBSS) method. TBSS performs
automated analysis of white matter integrity using nonlinear registration method and
projecting individual DTI data onto a common skeleton depicting the estimated bisecting
surfaces of the major white matter tracts. The method combines the strength of both
voxel-based and tractography based analyses, and overcomes the limitations of
conventional methods including partial volume, spatial smoothing, and arbitrary
thresholds . TBSS has been successfully applied to clinical studies and shown to improve
sensitivity for detecting white matter diffusion changes, even with relatively small sample
size (n < 30) . The development of normalization and alignment techniques for DTI data
using TBSS could improve sensitivity for detecting developmental changes in white
matter microstructure and its association with IQ or cognitive function. In the current
literature about DTI studies, most of the results regarding sex differences in white matter
development during adolescence are based on cross-sectional study designs. The existing
longitudinal studies do not focus on sexual dimorphism and sex by age interaction in
white matter microstructure. A recent longitudinal tractography study with DTI data
acquired at 1.5 Tesla by Lebel and Beaulieu demonstrated sex differences in 103 healthy
subjects aged 5-32 years focusing on 10 major white matter tracts . For each tract,
diffusion indices were averaged across all voxels along the tract among all subjects. This
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type of semiautomated tractography method depending on the user defined seed points
can be subjective and less sensitive to small regional changes. Whereas TBSS aligns all
subjects’ FA maps into a common space, creates a mean FA skeleton representing the
centers of all tracts common to the group and then projects all subjects’ FA data onto the
mean FA tract skeleton fed into voxel-wise analysis, which results in the best yet
alignments of DTI data from multiple subjects. Taking advantage of the improved
sensitivity for longitudinal changes in white matter microstructure offered by using TBSS
with high quality DTI data obtained at 3.0 Tesla, we expected to be able to detect specific
regional differences between boys and girls in a one year period of development during
adolescence, even with a small cohort of subjects. We further demonstrate the power in
longitudinal DTI data by examining the relationship of WM development with IQ
separately in boys and the girls using TBSS. Based on our previous work examining sex
differences in WM development, we hypothesized that separation of the sexes in this
analysis would allow us identify relationships between WM and intelligence that would
not be apparent in a combined analysis of boys and girls. The sensitivity provided by
DTI data in a longitudinal cohort allows us to establish this hypothesis unequivocally in a
relatively small same of children.
The demographics of participants are presented in Table 1 and show no significant
differences in age, FSIQ, VIQ, and PIQ between boys and girls. The mean follow-up
time between the first and second DTI scan was 12 months with standard deviation of 22
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Table 1 and Figure 1
2.1 Global mean of diffusion indices in the TBSS skeleton
For the global mean FA, we found significant sex by age interaction
( 1,14 9.04, 0.009)Fp
. One subject, the youngest boy (age 13 at time 1), had a
value of global mean FA that deviated from the linear regression curve fitted to the entire
sample. We considered the influence of this outlier on the statistical analysis and found
that without this data point, the sex by age interaction was still marginally significant.
Due to this significant sex by age interaction, we then evaluated the effect of age for boys
and girls separately. There was significant effect of age of the global mean FA in boys
( 1,713.41, 0.008)Fp
but not in girls
( 1,70.04, 0.853)Fp
. For the global
mean values of other DTI indices, there was significant effect of sex for MD
( 1,14 7.36, 0.017)Fp
(1,14 10.21, 0.007)Fp
, and RD
( 1,14 4.67, 0.049)Fp
. Girls showed significantly higher global mean value of
MD, AD and RD than boys. But there was no significant sex by age interaction for any
of these three indices. The scatter plots of all the global mean values of diffusion indices
in Figure 1 show linear regression trend lines for the boys and the girls separately. Only
the trend for FA with age in boys is significant, as indicated by the solid line in Figure 1.
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The other trends for MD, AD, and RD with age are not significant, as indicated by the
dashed lines in Figure 1.
Figure 2 and Table 2
2.2 Sex and age interaction
Regions showing significant sex by age interactions based on the TBSS results are
represented by color magenta superimposed on the mean FA skeleton in color green as
shown in Figure 2 and summarized in Table 2. For FA, five clusters of tract regions were
all identified in the left hemisphere including corticospinal tract (CST), forceps minor,
inferior longitudinal fasciculus (ILF), superior longitudinal fasciculus (SLF), and
uncinate fasciculus (UF). There was no region showing significant sex by age interaction
in the other indices (MD, AD, RD).
Figure 3 and Table 3
2.3 Sex difference
Table 3 and Figure 3 summarize the voxel-wise comparison between boys and girls using
TBSS with age as a covariate of no interest (nuisance variable in the design matrix for the
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program “Randomise”). Boys showed significantly higher FA than girls in the bilateral
CST and left SLF, while there was no cluster showing significantly higher FA in girls
than boys. For MD, girls showed significantly higher value than boys in the left inferior
fronto-occipital fasciculus (IFOF), left anterior thalamic radiation (ATR), right forceps
minor, bilateral CST and SLF. For AD, we identified several regions showing
significantly higher values in girls than boys including frontal and temporal parts of the
left ATR, bilateral CST, right IFOF, right SLF, and body of corpus callosum (bCC). For
RD, we found girls showing significantly higher values than boys in the left ATR, left
forceps minor, bilateral CST and SLF. In MD, AD, and RD, we did not detect any
regions showing significantly higher values in boys than girls.
Figure 4 and Table 4
2.4 Age effect
Table 4 and Figure 4 summarize TBSS results of regions showing significant age effect.
Data from boys and girls were evaluated separately for FA due to the significant sex by
age interactions. The slopes fitted to data from girls were not significant based on voxel-
wise TBSS results, while boys showed significant positive age slope in the left ATR,
IFOF, ILF, SLF, and UF. For MD, significant negative correlations between MD and age
were found in the bilateral cingulum (cingulate gyrus) (CGC), CST, IFOF, and SLF. For
AD, significant negative correlations between AD and age were observed in the right
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hemisphere including cingulum (hippocampus) (CGH), CST, IFOF, ILF, SLF, and UF.
For RD, there were significant decreases in RD with age in the left hemisphere covering
ATR, IFOF, SLF, and UF.
Figure 5 and Table 5
2.5 Longitudinal changes
Table 5 and Figure 5 summarize clusters showing significant one-year changes in each
diffusion index. There were seven clusters showing significant longitudinal increases of
FA in boys but not in girls. They are all in the left hemisphere including ATR, CGC,
CST, IFOF, UF, and forceps minor. Both forceps major and minor tracts showed
significant increases of AD in boys but not in girls. There was no cluster showing
significant one-year changes in either MD or RD.
Figure 6 and Table 6
2.6 Voxel-wise correlation with intelligence measures
Table 6 and Figure 6 summarize significant voxel-wise correlations between intelligence
measures and diffusivity indices with age as covariate of no interest. Significant
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correlations were identified between FSIQ score and FA in the right IFOF when both
sexes were looked at together. There were two clusters in which VIQ score showed
significant correlation with FA. They were located in the left CST and the left SLF when
both sexes were looked at together. When we examined correlations for boys and girls
separately, we found that VIQ score showed significant correlation with FA in girls but
not in boys. There was no significant correlation found between PIQ score and any
In this study, we utilized TBSS, a novel method for group analysis of DTI data, to
investigate sex differences in white matter development during adolescence and
correlation between the diffusion characteristics in white matter microstructure and IQ.
We also provided quantitative estimates of one-year change in diffusion indices and
identified sex differences in white matter development over the time course of one year,
which complement our cross-sectional findings of sex differences in the white matter
developmental trajectory .
3.1 Global changes of diffusion characteristics in white matter
Consistent with cross-sectional studies in literature , our results from TBSS have
demonstrated increases in the global mean FA values for adolescents from 13-18 years of
age (see Figure 1). Increased global mean FA values may suggest more organized fiber
bundles. Many studies have shown rapid changes in diffusion parameters from 0-5 years
of age and have suggested an exponential trend in FA increase during childhood with
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more changes from 0-8 years of age and plateauing by late adolescence to early
adulthood . Our results showed significant sex by age interaction in the global mean FA
values and found significant increases of the global mean FA values in boys but not in
girls from 13-18 years of age. This finding might suggest earlier white matter maturation
in girls than in boys based on the differences in FA increases with age. Moreover, we
identified significant sex differences in the global mean values of MD, AD and RD. It
has been reported that AD may be more sensitive to differences in axonal diameter .
Girls showed significantly higher global mean MD, AD and RD than boys, which
suggested sex-related differences in axonal organization, axonal caliber, or myelination.
We also observed that the individual changes between Time 1 and 2 are occasionally in
the opposite direction of the general trend (see Figure 1), which demonstrates the
importance and strength of longitudinal study design because we can see trends in
individuals which may differ from the trend in a cross-sectional cohort. With additional
subjects or additional years of data we may be able to differentiate sub-groups of children
with various trends, possibly correlating with neurocognitive differences.
3.2 Regional changes of diffusion characteristics in white matter
For FA, we found significant sex by age interaction in the left CST, forceps minor, ILF,
SLF, and UF (see Figure 2 and Table 2). When controlling for age, we found significant
higher FA in boys than girls in the bilateral CST and left SLF (see Figure 3 and Table 3),
contrary to the results from Bava et al. , but consistent with other studies . Bava et al.
showed higher FA in girls than boys in bilateral CST. Given that their study participants
were young adolescents from 12-14 years of age, it is not surprising that our results
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differ. It can be interpreted that white matter tracts of girls developed earlier in bilateral
CST during 12-14 years of age, and boys developed later in these regions during 13-18
years of age. Among the 13- to 18- year-old children studied, we found FA increases
with age in five clusters including frontal, parietal, and temporal regions but not in
occipital regions (see Figure 4 and Table 4), consistent with other studies indicating
protracted increases in anterior white matter volume . In addition, we observed that the
age slopes for girls were flat and not significantly different from zero, whereas the age
slopes for boys were significantly bigger than zero in the left ATR, IFOF, ILF, SLF, and
UF, which agrees with recent evidence about sex differences in white matter
developmental trajectory . These findings also suggest earlier white matter maturation in
girls than in boys. Nevertheless, we found continuing maturation of white matter from
13-18 years of age, in agreement with other studies . Longitudinal differences in FA
showed less extensive changes, including six small clusters reflecting significant one-
year increases in FA values in boys but not in girls (see Figure 5 and Table 5). The sex
differences in developmental trajectory of white matter anisotropic diffusion suggest that,
by adolescence, girls have developed earlier than boys in widespread brain regions,
which has been suggested to be related to microstructural processes rather than increases
in total white matter volume . However, Bava et al. found longitudinal increases of FA
mostly in right hemisphere in 22 healthy adolescents from 16-21 years of age. Due to the
differences in age group, statistical analysis, and image thresholding method, we are
limited in comparing their findings directly with ours. Their one-year changes in FA
values range from 0.03 to 0.05, while our results in FA changes over the time course of a
year range from 0.05 to 0.09 for boys. These results are more complementary to each
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other than disparate and could indicate that white matter development continues from 13-
17 into 16-21 years of age with less progressive annual changes in the latter period (16-
21 years of age). The less dramatic FA changes with age in their findings could also be
explained because they did not separate boys and girls.
For MD, there was no cluster showing significant sex by age interaction, but there were
seven clusters including the right forceps minor, left ATR, left IFOF, bilateral CST and
SLF presenting significant sex differences (see Figure 3 and Table 3), and eight clusters
showing significant age-related decreases in the bilateral CGC, CST, IFOF, and SLF (see
Figure 4 and Table 4). We found girls had higher MD and lower FA in the bilateral
frontal portion of the CST than boys, reflecting greater white matter density in bilateral
CST for girl. This is consistent with a recent study in which a novel data driven approach
was used to study the tract-based white matter maturation . Our longitudinal analysis did
not detect significant changes in MD for either sex over the time course of a year. This is
contrary to the results from Bava et al. that identified significant longitudinal decreases
of MD over the time course of a year in the cerebellar fibers, bilateral IFOF and SLF in
children from 12-14 years of age. This may indicate that the one-year changes in MD are
less rapid in our study group (13-18 years of age) than theirs (12-14 years of age).
For AD, there was no cluster showing significant sex by age interactions. We identified
six clusters including the left ATR, bCC, bilateral CST, right IFOF and SLF showing
significant sex differences (see Figure 3 and Table 3). Girls show significantly higher
AD in the bilateral CST than boys do, consistent with another study . Connecting with
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our results that high VIQ score correlates with high FA in the left CST for girls but not
for boys, higher AD in this region in girls than boys may indicate earlier development of
verbal abilities in girls than boys. Moreover, girls show significantly higher AD in bCC
than boys do. This result connects with previous fMRI findings of increased functional
connectivity across the corpus callosum between the hemispheres for girls compared with
boys performing a language task .
For RD, there was no cluster showing significant sex by age interaction. We found six
clusters showing significant sex differences in the left ATR, left forceps minor, bilateral
CST and SLF (see Figure 3 and Table 3), and four clusters in the left hemisphere
showing significant age-related changes including ATR, IFOF, SLF, and UF (see Figure
4 and Table 4). Connecting with our TBSS results of FA, the increases of FA are
associated with decreases of RD in the left IFOF and SLF. Boys showed higher FA but
lower RD in the left SLF. Animal studies have shown that higher values of FA and lower
values of RD in mice reflect greater myelination . Our findings can be interpreted that
boys (13-18 years of age) may have greater myelination in the left SLF compared with
girls, which is consistent with other studies .
3.3 Correlations between intelligence measures and diffusion indices
Consistent with our earlier cross-sectional study , FSIQ was positively related to FA in
the frontal part of the right IFOF when both sexes were looked at together, which
indicates region-specific increases in FA may reflect efficient organization of white
matter fiber bundles that lead to optimal cognitive performance . However, we did not
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find negative correlations between FSIQ and FA in boys as reported in that study , which
may be due to different DTI analysis methods (TBSS was not used) and different study
age group. In addition, there were two clusters including left CST and SLF showing
significant correlation between VIQ and FA in girls but not in boys (see Table 6). This is
an interesting finding which agrees with a recent longitudinal study . VIQ changes were
related to the frontal portion of the left CST which controls motor speech, and the frontal
portion of the left SLF which is a key language pathway that connects frontal lobe to
temporal and parietal lobes and is responsible for information exchange between Broca
and Wernicke's language regions .
3.4 Future directions and conclusion
Recent DTI methodological advances have improved our ability to discern sex
differences in white matter microstructure and identify subtle changes in white matter
tracts with longitudinal study design. Our study used the voxel-wise TBSS method for
spatial normalization and analysis in order to minimize artifacts from imperfect spatial
registration and avoid using arbitrary smoothing. Using this relatively conservative
approach, the regions with significant age-related changes and sex differences found in
the current study were limited compared to those found in other studies . Some
discrepancies between longitudinal changes in this study and cross-sectional results may
be explained by the relative small sample size (n = 16) and short time-span (1 year).
However, it should be noted that with a longitudinal sample and TBSS methodology we
have been able to identify sex-dependent trends in WM development in a cohort of 16
adolescents, that are consistent with and more specific than our previous work using a
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cross-sectional cohort of 106 children . With equal numbers of scans from boys and girls
obtained longitudinally over a one year span, we have been able to examine the
interaction of IQ and WM development as a function of sex, identifying robust regional
differences between the sexes. In future studies, as we continue to follow this
longitudinal cohort through adolescence, we will develop a customized voxel-wise
algorithm for linear mixed-effects modeling for longitudinal study design. This approach
will allow a more flexible covariance matrix structure, accounting for within-subject error
and missing data points, which will increase statistical power and enable more detailed
examination of sexual dimorphism of WM growth curves during emerging adulthood.
In conclusion, developmental changes in white matter diffusion characteristics seen in
this group of sixteen participants demonstrates that the longitudinal design, combined
with TBSS methods, provides a powerful approach for investigating structural
differences in the white matter that underlie developmental changes in brain function
during adolescence. Our findings support the conclusions that the rates of white matter
maturation differ between boys and girls at global level and the timing varies among
different brain regions with frontal, parietal, and temporal regions maturing at a later age
than the occipital region. This highlights the importance for neuroimaging studies to
approach the analysis of data in developing populations with appropriate attention to the
influence that sex may have on brain development. Boys (13-17 years) continue to
demonstrate white matter maturation, whereas girls appear to reach mature levels earlier.
In addition, we identified significant positive correlations between FA and FSIQ in the
right IFOF when both sexes were looked at together, and positive correlations between
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FA and VIQ in the left CST and SLF in girls but not in boys. Moreover, this study
indicates that sexual dimorphism in white matter maturation is important and should be
taken into account in studies designed to examine white matter maturation.
4. Materials and Methods
Twenty healthy, native English speaking adolescents were drawn from a longitudinal
subgroup recruited from a larger cross-sectional sample of participants previously
included in our fMRI studies of language development . Informed consent or assent was
given by all parents and participants. This study was approved by the Institutional
Review Board at Cincinnati Children’s Hospital Medical Center. None of the
participants had any neurological impairment or neurological trauma. Longitudinal
diffusion image data were recorded at two time points which were one year apart from
each other. Data from four participants were excluded due to poor quality diffusion
images. Sixteen participants were included in this study with an average age of 15.30 ±
1.24 years old at the first time point, and 16.29 ± 1.30 at the second time point (mean ±
SD, 8 boys and 8 girls).
4.2 Neurocognitive testing
Neurocognitive testing was administered under the supervision of one of the co-authors
(AWB), a board certified neuropsychologist. All participants received the Wechsler
Abbreviated Scale of Intelligence (WASI) at time 2. The WASI is nationally
standardized and yields verbal IQ (VIQ), performance IQ (PIQ), and full-scale IQ (FSIQ)
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scores, which has been regarded as a quick, reliable measure of intelligence and can be
used to assess a broad age range from ages 6 through 89 years .
4.3 Image acquisition
DTI data were acquired using a single-shot spin-echo, echo-planar imaging (SE-EPI)
sequence on a Philips Achieva 3T MRI scanner with Dual Quasar gradients and
transmit/receive quadrature head coil (Philips Medical Systems, Best, The Netherlands).
Acquisition parameters were: TR/TE = 12000/89 ms, acquisition matrix = 92 × 89, field
of view = 180 × 180 (in-plane resolution = 2 × 2 mm), and slice thickness = 2 mm with
no gap. Diffusion images were comprised of 32 diffusion weighted volumes with
gradient encoding applied in 32 non-collinear directions and
1000 s/mmb =
, and one
non-diffusion weighted (T2-weighted,
0 s/mmb =
) reference image, denoted
4.4 Image processing
Images were pre-processed in the FSL software package (FMRIB software Library,
FMRIB, Oxford, UK) including correction for eddy current induced distortion and
participant’s head motion and generation of brain mask based on
0b image . Then, pre-
processed images were subjected to tensor decomposition for generating FA, MD, AD,
and RD indices using FDT (FMRIB’s Diffusion Toolbox) . TBSS was then used to
prepare the individual diffusion maps for voxel-based group analysis by performing the
following steps: all subjects’ FA images were aligned into a template of averaged FA
images (FMRIB-58) in Montreal Neurological Institute (MNI) space using a non-linear
registration algorithm implemented in FNIRT (FMRIB’s Non-linear Registration Tool) .
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After transformation into MNI space, a cohort mean FA image was created and thinned
to generate a cohort mean FA skeleton of the white matter tracts. Each subject’s aligned
FA image was then projected onto the cohort mean FA skeleton by filling the cohort
mean FA skeleton with FA values from the nearest relevant tract center, which was
achieved by searching perpendicular to the local skeleton structure for maximum value.
This second local coregistration step can alleviate the alignment problems. Voxel-wise
statistical analysis across subjects on the skeleton space was carried out for voxels with
FA > 0.2 to include only major fiber bundles and exclude peripheral tracts with
significant inter-subject variability. The MD, AD and RD images were then transformed
and projected in the same way onto the group white matter skeleton using the same
registration parameters as the FA images.
4.5 Statistical Analysis
Prior to the voxel-wise analysis, we calculated the global mean values of each DTI index
(FA, MD, AD, and RD) from the whole-brain TBSS skeleton for each subject. Then, we
()()() global mean valuessex agesex age
, treating the
two time points as repeated measures and performed mixed-effect model with restricted
maximum likelihood (REML) (‘PROC MIXED’ procedure) using SAS 9.2 for Windows
(SAS Institute Inc., Cary, NC, USA). If the sex by age interaction was not significant, we
simplified the full model by removing the interaction term, leaving the reduced model:
()() global mean values sexage
βββε=+++ . If the sex by age interaction was
significant, we modeled
() global mean valuesage
ββε=++ for boys and girls,
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respectively. The scatter plots were then generated using the ggplot package in the R
system (version 2.11.1 64 bit) .
For the voxel-wise analysis, a permutation program “Randomise” in FSL was used to
produce statistic maps with a standard general linear model (GLM)
()()() diffusion index valuessex agesex age
×ββββε=++++ for each voxel. Since
we have a between subject factor (sex) in addition to a within subject factor (age), the
current version of “Randomise” cannot fit this type of data in a GLM. Thus, data from
Time 1 and 2 for each subject were averaged and then fitted in a GLM. For voxels found
to exhibit a significant sex by age interaction, we performed voxel-wise correlation
analyses to determine the effect of age for boys and girls separately. The resulting
contrast maps were enhanced via Threshold-Free Cluster Enhancement (TFCE)
algorithm in FSL which can avoid the problem of selecting arbitrary smoothing kernel
and thresholds for cluster size. All statistical results are corrected p-values at p < 0.05
after controlling for family wise error rate. The center of gravity for each significant
cluster was defined anatomically in MNI coordinates using the probabilistic John
Hopkins University White Matter Atlas .
The longitudinal changes between time 2 and 1 were evaluated by using the difference
between time 2 and time 1 data. Then, the program “Randomise” was used to detect
significant non-zero mean clusters indicating significant one-year changes in all the
diffusion indices. To further investigate this difference, DTI data from boys and girls
were evaluated separately.
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The voxel-wise correlation between diffusion indices (FA, MD, AD, and RD) and the
intelligence measures (FSIQ, VIQ, and PIQ) was performed using the program
“Randomise” while treating age as a covariate of no interest. To further investigate this
correlation, DTI data from boys and girls were evaluated separately.
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This study was supported in part by a grant from the U.S. National Institute of Health
(NIH grant R01-HD38578, P.I. SK Holland). The study was also supported in part by the
Victorian Government’s Operational Infrastructure Support Program. The authors
acknowledge the assistance of Ms. Amanda Huber, Ms. Sara Robertson and Ms. Julie
Franks, for helping with recruitment and data collection, and Mr. Kendall O’Brien and
Ms. Amanda Woods, for performing all the MRI scans.
Page 23 of 34
Figure 1: Scatter plots of the global mean values of each diffusion index calculated from
the whole-brain TBSS skeleton, along with least squares regression lines for each sex.
FA: fractional anisotropy; MD: mean diffusivity; AD: axial diffusivity; RD: radial
diffusivity. Red symbols and lines represent girls and blue represents boys. Time point 1
and 2 are represented by triangles and circles respectively and are connected to each other
by grey dotted lines. The standard errors are shown with a grey band around the
regression line. The solid regression line denotes statistical significance, while the
dashed regression lines indicate the trend without significance.
Figure 2: TBSS results of significant sex by age interactions (p < 0.05, corrected) for
FA corresponding to Table 2. The results from sex by age interactions are shown in
magenta superimposed on the fiber skeleton (Green) and overlaid on the FMRIB FA
template. Images are in radiological convention. x: right ↔ left direction; y: anterior ↔
posterior direction; z: superior ↔ inferior direction.
Figure 3: TBSS results of significant sex effects (p < 0.05, corrected) in each diffusion
index corresponding to Table 3. The results from sex effect superimposed on the fiber
skeleton (Green) and overlaid on the FMRIB FA template. Red represents the regions
that girls show significant higher values than boys. Blue represents regions that boys
show significant higher values than girls. Images are in radiological convention. x: right
↔ left direction; y: anterior ↔ posterior direction; z: superior ↔ inferior direction.
Page 24 of 34
Figure 4: TBSS results of significant age effects (p < 0.05, corrected) in each diffusion
index corresponding to Table 4. The results from age effect are superimposed on the
fiber skeleton (Green) and overlaid on the FMRIB FA template. Yellow represents
increases with age and cyan represents decreases with age using data from both boys and
girls. Note the age trend for FA was merely driven by data from boys. Images are in
radiological convention. x: right ↔ left direction; y: anterior ↔ posterior direction; z:
superior ↔ inferior direction.
Figure 5: TBSS results of significant changes between time 2 and time 1 (p < 0.05,
corrected) corresponding to Table 5 for boys. No regions were found with significant
time dependence in girls. Results are superimposed on the fiber skeleton (Green) and
overlaid on the FMRIB FA template. The color red represents increases from time 1 to 2.
Images are in radiological convention. x: right ↔ left direction; y: anterior ↔ posterior
direction; z: superior ↔ inferior direction.
Figure 6: TBSS results of significant correlations between FA and intelligence measures
(p < 0.05, corrected) corresponding to Table 6 when both sexes were looked at together.
Results are superimposed on the fiber skeleton (Green) and overlaid on the FMRIB FA
template. The color red represents positive correlation between FA and FSIQ, and
fuchsia represents positive correlation between FA and VIQ. Images are in radiological
convention. x: right ↔ left direction; y: anterior ↔ posterior direction; z: superior ↔
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