Pervasive microstructural abnormalities in autism: A DTI study

Abstract

Recent studies have reported abnormal functional connectivity patterns in the brains of people with autism that may be accompanied by decreases in white matter integrity. Since autism is a developmental disorder, we aim to investigate the nature and location of decreases in white and grey matter integrity in an adolescent sample while accounting for age.
We used structural (T1) imaging to study brain volumetrics and diffusion tensor imaging (DTI) to investigate white and grey matter integrity in people with autism. We obtained magnetic resonance images for adolescents aged 12-18 years with high-functioning autism and from matched controls. Fractional anisotropy and mean diffusivity, as well as grey and white matter volumetrics were analyzed.
There were 17 participants with autism and 25 matched controls included in this study. Participants with autism had lower fractional anisotropy in the left and right superior and inferior longitudinal fasciculus, but this effect was not significant after adjusting for age and intelligence quotient (IQ). The kurtosis of the white matter fractional anisotropy probability distribution was higher in this participant group, with and without adjustment for age and IQ. Most notably, however, the mean diffusivity levels were markedly increased in the autism group throughout the brain, and the mean diffusivity probability distributions of both grey and white matter were shifted toward a higher value, particularly with age and IQ adjustment. No volumetric differences in grey and white matter were found. Limitations: We corrected for age and IQ using a linear model. The study was also limited by its sample size, investigated age range and cross-sectional design.
The findings suggest that autism is characterized by a generalized reduction of white matter integrity that is associated with an increase of interstitial space. The generalized manifestation of the white matter abnormalities provides an important new perspective on autism as a connectivity disorder.

Full text

32 J Psychiatry Neurosci 2011;36(1)
© 2011 Canadian Medical Association
Background: Recent studies have reported abnormal functional connectivity patterns in the brains of people with autism that may be ac-
companied by decreases in white matter integrity. Since autism is a developmental disorder, we aim to investigate the nature and loca-
tion of decreases in white and grey matter integrity in an adolescent sample while accounting for age. Methods: We used structural (T1)
imaging to study brain volumetrics and diffusion tensor imaging (DTI) to investigate white and grey matter integrity in people with autism.
We obtained magnetic resonance images for adolescents aged 12–18 years with high-functioning autism and from matched controls.
Fractional anisotropy and mean diffusivity, as well as grey and white matter volumetrics were analyzed. Results: There were 17 partici-
pants with autism and 25 matched controls included in this study. Participants with autism had lower fractional anisotropy in the left and
right superior and inferior longitudinal fasciculus, but this effect was not significant after adjusting for age and intelligence quotient (IQ).
The kurtosis of the white matter fractional anisotropy probability distribution was higher in this participant group, with and without adjustment
for age and IQ. Most notably, however, the mean diffusivity levels were markedly increased in the autism group throughout the brain, and
the mean diffusivity probability distributions of both grey and white matter were shifted toward a higher value, particularly with age and IQ
adjustment. No volumetric differences in grey and white matter were found. Limitations: We corrected for age and IQ using a linear model.
The study was also limited by its sample size, investigated age range and cross-sectional design. Conclusion: The findings suggest that
autism is characterized by a generalized reduction of white matter integrity that is associated with an increase of interstitial space. The gen-
eralized manifestation of the white matter abnormalities provides an important new perspective on autism as a connectivity disorder.
Research Paper
Pervasive microstructural abnormalities in autism:
a DTI study
Wouter B. Groen, MD, PhD; Jan K. Buitelaar, MD, PhD; Rutger J. van der Gaag, MD, PhD;
Marcel P. Zwiers, PhD
Groen, Buitelaar, van der Gaag — Karakter, Child and Adolescent Psychiatry University Center; Groen, van der Gaag, Zwiers
— Department of Psychiatry; Groen, Buitelaar, Zwiers — Donders Institute for Brain, Cognition and Behavior; Buitelaar —
Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Center, Nijmegen, the Netherlands
Introduction
Recently, various researchers have proposed that impaired
integration of information underlies autism, and that this im-
pairment results from abnormal neural connectivity.1–3 Con-
verging evidence has shown deficient connectivity in tasks
involving language comprehension,2,4 working memory5and
action planning6using functional magnetic resonance imag-
ing (fMRI); listening using magnetoencephalography;7men-
talizing using positron emission tomography,8and also in
resting state networks using fMRI.9These studies, however,
address functional connectivity between grey matter areas,
which should be differentiated from structural (anatomic)
connectivity associated with the intermediating white matter
tracts. How or to what extent functional and structural con-
nectivity are related often remains unclear. Yet, in general, it
is believed that abnormal functional connectivity does not
necessitate abnormal structural connectivity, but rather that
deficient structural connectivity commonly results in a lack of
functional connectivity.10,11 A comprehensive understanding
of neural connectivity in people with autism therefore re-
quires clear evidence as to whether structural connectivity is
affected in these individuals.
Currently, most evidence is still indirect and merely de-
scribes global white matter properties. A volumetric MRI
study found an abnormal developmental trajectory of white
matter in children with autism: they had normal head cir-
cumference at birth and increased cerebral and cerebellar
Correspondence to: Dr. W.B. Groen, Karakter, Child and Adolescent Psychiatry University Center, P.O. Box 9101, 6500 HB Nijmegen, the
Netherlands; w.groen@psy.umcn.nl
J Psychiatry Neurosci 2011;36(1):32-40.
Submitted Aug. 20, 2009; Revised Apr. 23, June 15, 2010; Accepted June 16, 2010.
DOI: 10.1503/jpn.090100
per-groen_JPN template 15/12/10 8:03 AM Page 32

Microstructural abnormalities in autism
J Psychiatry Neurosci 2011;36(1) 33
white matter volume at 2–3 years of age that normalized
again in later years.12 This pattern was confirmed in addi-
tional analyses of head circumferences, postmortem findings
and MRI measurements,13,14 which led the authors to argue
that the early overgrowth interferes with the normal develop-
mental trajectory of cortical connectivity. In a qualitative
review, data were combined from 6 studies on the develop-
ment of white matter volume from 2 to 20 years of age.15
Again, the same developmental trajectory was found, with a
white matter increase in early childhood that normalized at
around 12 years of age. This developmental finding under-
lines the importance of using samples with a narrow age
range when studying autism, and it raises the question as to
whether the integrity of white matter at the age of 12 years
and above is affected. Morphologic studies have shown
that in typical populations, brain structures mature over a
posterior– anterior axis, from primary to higher association
areas.16 It is therefore conceivable that as the brain in people
with autism develops abnormally, the late developing pre-
frontal and superior-temporal areas show the most pro-
nounced defects in adolescence.17
A more direct and informative method for investigating in
vivo white matter integrity is diffusion tensor imaging (DTI),
an MRI-based technique that measures the directional diffu-
sion profile of water molecules, which manifests the axonal
architecture of the brain at the micrometer level. Fractional
anisotropy and mean diffusivity are 2 measures derived from
diffusion tensor data.18,19 Fractional anisotropy and mean dif-
fusivity provide an index for the integrity of neural tissue,
and more specifically, fractional anisotropy provides an indi-
cation of the directionality of white matter microstructural ar-
chitecture and mean diffusivity of the interstitial space in
both white and grey matter. So far, a limited number of stud-
ies have used DTI for the study of autism (Table 1). Barnea-
Goraly and colleagues20 were the first to apply DTI to a small
number of autistic children and controls. Using a voxel-based
approach that excluded the cerebellum, they found reduced
fractional anisotropy in the corpus callosum and in the white
matter of the ventromedial prefrontal cortices, anterior cingu-
late gyri and temporoparietal junctions, indicating a reduc-
tion in white matter integrity in the autism group. A more re-
cent study that used a voxel-based approach in a large
sample of autistic children and adults showed a fractional
anisotropy reduction within and near the corpus callosum
and internal capsule.21The authors argued that these reduc-
tions did not reflect slowing of white matter development
but rather that they persist into adulthood. The effect size did
not allow for correction for multiple comparisons, necessitat-
ing confirmation of the findings. Findings of reduced frac-
tional anisotropy in the frontal lobe and left temporal lobe
were reported in an exploratory study on a small group of
Chinese children with high-functioning autism.22 Neither of
these studies reported on mean diffusivity. A DTI study of
the corpus callosum in a large sample of autistic children and
adults found a reduction of fractional anisotropy and in-
crease of mean diffusivity, indicating a reduced integrity of
the genu, body and splenium of the corpus callosum.23
Analy sis of the superior temporal gyrus and temporal stem
in the same participant group also showed a fractional
anisotropy reduction and mean diffusivity increase.24 An-
other recent DTI study used tractography of the frontal lobe25
and found reduced fractional anisotropy and increased mean
diffusivity along the short frontal association fibres and re-
duced fractional anisotropy along the long frontal fibres. Al-
though the patient group was large, the study sample was
also heterogeneous since it included children with autism,
pervasive developmental disorder not otherwise specified
and Asperger disorder. A cerebellar DTI study in adults with
As perg er sy ndro me al so fo und r educ ed fr acti onal
anisotropy, but mean diffusivity did not differ between the
patient and control groups.26 Contrary to the other DTI find-
ings in populations with autism, a high b-value, diffusion-
Table 1: Diffusion tensor imaging studies on autism
Group; no. Group; age, mean (SD) yr Results
Study Method Diagnosis Autism Control Autism Control FA MD Location
Barnea-Goraly
et al.20
Whole-brain voxel-
based analysis*
Autism 7 9 13.4 (2.8) 14.6 (3.4) ↓NR Frontal white matter,
temporal white matter
Alexander et al.23 Semiautomated VOI Autism, PDD-NOS 43 34 16.2 (6.7) 16.4 (6.0) ↓ ↑ Corpus callosum
Lee et al.24 Semiautomated VOI Autism, PDD-NOS 43 34 16.2 (6.7) 16.4 (6.0) ↓ ↑ Superior temporal gyrus,
temporal stem
Catani et al.26 Tractography o f
cerebellum
Asperger syndrome 15 16 31.0 (9.0) 35.0 (11.0) ↓= Cerebellum
Keller et al.21 Whole-brain voxel-
based analysis*
Autism 34 31 18.9 (7.3) 18.9 (6.2) ↓NR Frontal white matter,
temporal white matter
Bashat et al.27 Whole-brain and VOI Autism 7 41 1.8 (3.3) 0.3 (23.0) ↑NR Frontal white matter,
temporal white matter
Sundaram et al.25 Tractography of
frontal lobe
Autism, PDD-NOS,
Asperger syndrome
50 16 4.8 (2.4) 6.8 (3.5) ↓ ↑ Frontal white matter
Ke et al.22 Whole-brain voxel-
based analysis; VBM
High functioning
autism
12 10 8.8 (2.3) 9.4 (2.1) ↓NR Frontal white matter,
temporal white matter
D = decreased; E = equal; FA = fractional anisotropy; I = increased; MD = mean diffusivity; NR = not reported; SD = standard deviation; PDD-NOS = pervasive developmental disorder
not otherwise specified; VBM = voxel-based morphometry; VOI = volume of interest.
*pevaluated at a level uncorrected for multiple comparison considering the search volume.
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weighted imaging study of 7 toddlers (aged 1.8–3.3 yr) with
autism compared with 41 healthy controls found an increase
in fractional anisotropy as well as increased probability for
zero displacement and a reduced mean displacement prob -
ability that was most prominent in the left frontal lobe.27
Meas ures of mean diffusivity were not reported, but the dis-
placement findings imply a decrease in mean diffusivity in
these areas. It should be noted, however, that high b-value
imaging primarily depicts intracellular diffusion properties,
whereas conventional DTI is believed to mostly reflect extra-
cellular diffusion.28 The authors interpreted their findings as
evidence for accelerated white matter maturation in young
children with autism.
There is a need to add whole-brain DTI analyses in a homo-
geneous sample (both in age and diagnosis) on both the frac-
tional anisotropy and mean diffusivity measures to the autism
literature. Furthermore, as autism is a developmental disor-
der, differences in brain structures change over time. There-
fore, it is interesting to relate these to DTI findings and to
make group inferences on the neuroanatomy of autism with-
out developmental changes as a confounder. We set out to ad-
dress these questions in the current DTI study by extracting
the main moments of a participant’s fractional anisotropy and
mean diffusivity probability distribution in grey and white
matter and compare these moments at the group level using a
homogeneous adolescent sample of participants with autism
and well-matched controls. Further, we investigated the spa-
tial location of voxel-wise differences in fractional anisotropy
and mean diffusivity between these groups by using whole-
brain, search volume– corrected tstatistics. In addition, we
looked into the spatial location of voxel-wise volumetric dif-
ferences in white matter using voxel-based morphometry
(VBM)29 in T1images and correlated the outcome with the
voxel-wise DTI results. We hypothesized that in our adoles-
cent sample, people with autism would show a decrease in
white matter integrity (i.e., abnormalities in fractional anis -
otropy or mean diffusivity), whereas white matter volumes
would not differ between groups, as indicated by previous
studies (see a review15). Absent volumetric abnormalities in
the presence of white matter integrity differences would sug-
gest that volumetric differences cannot account for differences
in integrity. Since cortical maturation follows an abnormal
developmental trajectory in autism, we also hypothesized that
the effects would be most pronounced in the late-developing
higher-association areas such as the prefrontal and superior-
temporal areas. As the development of grey and white matter
is closely related, we predicted that we would find greater
fractional anisotropy and mean diffusivity differences in the
temporal and frontal regions, and smaller or no differences at
all in the parietal and occipital regions.
Methods
Participants
We included typically developing adolescents (controls) and
adolescents with autism aged 12–18 years in the study. We
obtained written informed consent from all participants and
their parents. The local medical ethics committee (CMO regio
Arnhem- Nijmegen) approved our study.
We recruited the participants with autism through Karak-
ter, Child and Adolescent Psychiatry University Center,
Nijmegen. Diagnostic assignment followed DSM-IV criteria
for autistic disorder.30 Diagnostic characterization included
the Autism Diagnostic Interview — Revised (ADI-R)31 as as-
sessed by a trained clinician and a series of clinical assess-
ments, which included detailed developmental history, clin -
ical observation, medical work-up and cognitive testing. The
participants with autism were tested with the full Wechsler
Intelligence Scale for Children III.32 Only participants with an
intelligence quotient (IQ) of 80 or higher were included. To
screen for the presence of comorbid psychiatric disorders or
learning problems in the controls, Child Behavior Checklist
(CBCL) questionnaires33 were completed by the parents or
guardians of the controls and Teacher Report Form (TRF)
questionnaires34 were completed by a teacher at school. Ex-
clusion criteria were any medical condition affecting central
nervous system function, neurologic disorders, substance
abuse and a family history of psychiatric disorders.
For the control group, we assessed more than 200 children
from local high schools for verbal IQ, performance IQ and
full-scale IQ using a short form of the Wechsler Intelligence
Scale for Children III (WISC-III) including vocabulary, simi-
larities, block design and picture completion32,35 to find suit-
able matches. We matched the groups for age, sex, handed-
ness,36 total IQ, performance IQ and verbal IQ.
Data acquisition and procedure
We acquired neuroimaging data on a 1.5 T Siemens Sonata
scanner at the Donders Institute for Brain, Cognition and
Behaviour in Nijmegen. For each participant, a T1-weighted
whole-brain scan was collected (magnetization-prepared
rapid acquisition with gradient echo [MPRAGE], TI 850 ms,
repetition time [TR] 2250 ms, echo time [TE] 3.68 ms, flip an-
gle 15°, field of view [FOV] 256 ×256 ×176 mm3, voxel size
1.0 ×1.0 ×1.0 mm3), as well as a set of whole-brain diffusion-
weighted images (twice-refocused spin-echo echo-planar
imaging [TRSE-EPI]; TR 10100 ms, TE 93 ms, diffusion direc-
tions 30, b-value 900 s/mm2, unweighted images 4, FOV
320 ×320 ×160 mm3, voxel size 2.5 ×2.5 ×2.5 mm3). The T1
image served as a high-resolution anatomic reference image
for the DTI data and was used for assessing grey and white
matter volume differences between participant groups. The
participants were familiarized with the set-up and normal
scanning procedures before the actual image acquisition by
means of a rehearsal in a separate replica (dummy) scanner.
The set of diffusion images was first carefully corrected
for imaging artifacts from head and cardiac motion using
robust tensor estimation software developed in house.37 In
short, this consisted of iteratively reweighted least-squares
tensor estimation with Welsch-type weighting functions
and dedicated image processing to robustly detect and
eliminate cardiac and head motion artifacts. Subsequently,
the images were realigned and eddy current–corrected by
minimization of the diffusion tensor residual errors.38 The
Groen et al.
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Microstructural abnormalities in autism
J Psychiatry Neurosci 2011;36(1) 35
diffusion tensors and their derivative fractional anisotropy
and mean diffusivity measures were then normally com-
puted using linear re gression as implemented in a diffusion
toolbox (http ://sourceforge.net/projects/spmtools) of
SPM5 (Wellcome Department of Cognitive Neurology).
Next, the DTI results were spatially normalized to the
ICBM152 reference template. To this end, the participant’s
anatomic image was co registered to the set of diffusion
images using SPM’s mutual information-based rigid-body
transformation routine. We always visually inspected this
coregistration step to ensure that it was not notably biased
toward the magnetic susceptibility– induced geometric dis-
tortions in the diffusion-weighted images. Subsequently, we
computed the spatial normalization parameters describing
the nonlinear transformation of the anatomic image to the
reference template (all using SPM5 functionality and stan-
dard settings) and applied them to the fractional anisotropy
and mean diffusivity images. These normalized images
were smoothed with an 8 ×8 ×8 mm3full-width at half-
maximum 3-dimensional (3-D) Gaussian kernel and used
for voxel-wise comparisons across all participants.
For investigation of white and grey matter volume differ-
ences, VBM of the T1-weighted images was performed in
SPM5 using a VBM5 toolbox (http:// dbm .neuro .uni-jena.de).
The computed white matter probability map39 was modu-
lated with the Jacobian determinants of the normalization pa-
rameters to allow interpretation of the results in terms of vol-
umetric differences. The modulated white matter partitions
were smoothed with an 8 ×8 ×8 mm3full-width at half-
maximum 3-D Gaussian kernel. We performed the same
analysis for grey matter. We computed total white and grey
matter volume by summation of the probability maps,
thresholded at 0.5.
Statistical analysis
To characterize the fractional anisotropy and mean diffusiv-
ity probability distributions in each participant for white and
grey matter, we computed the raw and first central moments
(i.e., the sample mean, the sample standard deviation [SD],
the sample skewness and the sample kurtosis). We compared
these moments at the group level using 2-sided 2-sample
Student ttests once without and once with inclusion of age
and total IQ as covariates. An α-level of 0.05 was used to de-
note a result statistically significant.
We performed regionally specific group analysis of the
grey matter, white matter, fractional anisotropy and mean
diffusivity measures using whole-brain voxel-wise 1-sided
2-sample Student ttests in SPM5 in both directions. Although
the control group was closely matched, we performed the
voxel-wise analyses once without and once with inclusion of
age and total IQ as covariates. The fractional anisotropy
analyses were explicitly restricted to the computed white
matter mask, whereas the mean diffusivity analyses were re-
stricted to a constructed whole-brain mask. Voxel-wise analy-
ses were evaluated at a false discovery rate (FDR) of 0.05, so
that for every reported voxel, the probability of true-positive
discovery was 95%. The results were normalized to Zscores.
Results
Participants
We included 17 adolescents with autism and 25 controls in
the study. All participants were between 12 and 18 years, and
right-handed. Average ADI-R scores for the autism group
were social 18.2 (standard deviation [SD] 4.9), verbal 13.4 (SD
4.6), nonverbal 8.5 (SD 3.2), stereotypy 4.1 (SD 2.7) and onset
2.2 (SD 1.2). None of the controls was within the CBCL or
TRF clinical range. There were no significant differences in
age, sex, handedness,36 total IQ, performance IQ and verbal
IQ between the groups. In the autism group, nobody was on
psychotropic medication, but 2 participants had previously
used risperidone (Table 2).
Microstructural differences
We first examined whether we could find evidence at the
whole-brain level for pervasive differences in brain micro -
structure between the autism and control groups. To this
end, we calculated the fractional anisotropy and mean diffu-
sivity histograms for white and grey matter per participant
and compared the group-averaged distributions (Fig. 1, up-
per panel). The figure shows virtually equal fractional
anisotropy distributions for both groups in both grey and
white matter. The white matter distributions have 2 maxima,
1 at the position of the grey matter maximum and 1 around
the expected position. Two-sample Student ttests on the
main moments of the distributions (Table 3) indicate that for
fractional anisotropy there was no significant difference in
the mean, SD and skewness between the autism and control
groups, but that the kurtosis in white matter is larger in the
autism group. This indicates that there are no prominent frac-
tional anisotropy differences at the whole-brain level but that
white matter fractional anisotropy is somewhat more outlier-
prone in the autism group. The mean diffusivity distributions
in white and grey matter (Fig. 1, lower panel), however, were
both notably shifted toward greater mean diffusivity for the
autism group, whereas the global shape of the distribution
appeared to be unaffected. Two-sample Student ttests on the
main moments of the distributions (Table 3) indicate that the
Table 2: Study participant demographics and general characteristics
Group; mean (SD)
Characteristic Control Autism χ2/t
No. 25 17
Age, yr 15.5 (1.8) 14.4 (1.6) 0.06
Handedness* 86 (15) 81 (22) 0.36
Grey matter volume, mm2882 (62) 890 (91) 0.72
White matter volume, mm2499 (45) 491 (61) 0.61
Total IQ 105 (9) 98 (18) 0.10
Verbal IQ 105 (10) 97 (19) 0.10
Performance IQ 106 (11) 100 (15) 0.11
Sex, male:female 22:3 14:3 0.62
IQ = intelligence quotient; SD = standard deviation.
*Handedness was measured using the Edinburgh Handedness Inventory.36
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average mean diffusivity was significantly larger in the
autism group, but that there was no difference in the other
moments (i.e., in the shape of the distribution). To confirm
the validity of this finding, we examined whether this effect
was similarly present in cerebral spinal fluid (CSF) by sel -
ecting voxels with mean diffusivity values above 1.5 ×
10–3mm2/s. We did not find a difference in average mean
diffusivity for these voxels between the groups (p= 0.94).
Second, we analyzed microstructural differences using
voxel-wise comparisons to allow for fine grained regional in-
ferences. In 3 confined regions, the fractional anisotropy was
significantly decreased (Fig. 2) for the autism group compared
with the control group. The largest region was located in the
left superior and inferior longitudinal fasciculus on the border
of the temporal and occipital lobe. In the right hemisphere,
the superior and inferior longitudinal fasciculi were affected
as well. In the frontal lobe, we found regions with decreased
fractional anisotropy in the left and right corona radiata, but
only the left was statistically significant. We found no regions
with increased fractional anisotropy in the autism group com-
pared with the control group. However, when we included
age and total IQ as covariates, these 3 regions no longer
showed significant differences between groups. This suggests
that the controls’ greater fractional anisotropy values can, at
least partially, be explained by the variance in age and IQ.
Compared with fractional anisotropy, differences in mean
diffusivity were not limited to small regions, but rather were
significantly increased in most of the brain (including frontal,
temporal, parietal and occipital regions and the cerebellum) in
the autism group compared with the control group, even after
including age and total IQ as covariates (Fig. 3). The left and
right anterior, superior and posterior corona radiata, and the
anterior and posterior limb of the internal capsule, middle
cerebellar peduncle, thalamus and thalamic radiations and in-
ferior and superior longitudinal and fronto-occipital fasciculus
showed an increased mean diffusivity. Parts of the genu, body
and splenium of corpus callosum also showed a significant in-
crease in mean diffusivity. We found no mean diffusivity de-
creases in the autism group. As for the histogram analyses, the
mean diffusivity findings did not seem artifactual (e.g., be-
cause of more head motion during scanning), as no mean dif-
fusivity difference was found in voxels containing CSF.
Volumetric differences and covariates
We found no differences in total white or grey matter vol-
ume. At the voxel level, the VBM analyses showed that there
were no region-specific differences in grey or white matter
volumetrics between groups.
Groen et al.
36 J Psychiatry Neurosci 2011;36(1)
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7
Fractional anisotropy
0 0 .7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5
×10–3
Mean diffusivity, mm2/s
1000
500
0
2000
4000
4000
2000
0
2000
4000
Frequenc
y
WM
GM
WM
GM
Control
Autism
Fig. 1: Fractional anisotropy and mean diffusivity histograms. The
fig ure shows the group-a veraged histog rams of the fractio nal
anisotropy (upper panel; bin size 0.005) and mean diffusivity (lower
panel; bin size 0.01 ×10−3mm2/s) for grey and white matter. Results
for grey matter are depicted downwards for clarity. Upper panel: As
expected, fractional anisotropy in grey matter has the largest num-
ber of low fractional anisotropy values and peaks at around 0.1. (It
deviates from its expectation value zero owing to acquisition noise.)
The fracti on al anis otropy di stribut io n of wh it e matter is much
broader, ranging up to values of 0.7. Note that there are 2 peaks,
one around 0.1 and the other around 0.35. Lower panel: As ex-
pected, the distributions of mean diffusivity are moderately greater in
grey matter than white matter. Note, however, that in the autism
group the distribution of mean diffusivity is shifted toward higher val-
ues in both grey and white matter. Mean diffusivity values above
1.5 ×10–3mm2/ s are not depicted; they do not differ between
groups and reflect cerebral spinal fluid outside the brain tissue.
GM = grey matter; WM = white matter.
Table 3: Main moments of the fractional anisotropy and mean diffusivity probability distributions
Fractional anisotropy Mean diffusivity, mm2/s
Measure Control group Autism group pvalue
pvalue
(age + TIQ) Control group Autism group pvalue
pvalue
(age + TIQ)
Mean, grey matter 0.18 0.18 0.46 0.57 (99 ± 3) × 10–5 (100 ± 4) × 10–5 0.22 0.011
Mean, white matter 0.30 0.30 0.46 0.42 (85 ± 2) × 10–5(87 ± 3) × 10–5 0.014 0.003
SD, grey matter 0.11 0.11 0.17 0.8 1 (34 ± 3 ) × 10–5(33 ± 4) × 10–5 0.34 0.28
SD, white matter 0.16 0.16 0.10 0.25 (25 ± 2) × 10–5(24 ± 3) × 10–5 0.29 0.52
Skewness, grey matter 1.90 1.90 0.20 0.90 2.0 ± 0.2 2.0 ± 0.1 0.72 0.08
Skewness, white matter 0.58 0.63 0.07 0.20 4.1 ± 0.5 4.2 ± 0. 4 0.68 0.26
Kurtosis, grey matter 7.80 8.20 0.09 0.85 9.6 ± 2 9.8 ± 2 0.73 0.1 0
Kurtosis, white matter 2.70 2.80 0.009 0.027 27 ± 5 28 ± 6 0.55 0.23
SD = standard deviation; TIQ = tota l intelligence quotient.
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Microstructural abnormalities in autism
J Psychiatry Neurosci 2011;36(1) 37
Discussion
We found a generalized deficit in the integrity of white matter
in adolescents with autism. More specifically, we found an in-
crease in mean diffusivity throughout the white matter of the
cerebrum and cerebellum, even after correcting for age and
IQ. This is in line with and extends the findings of an overall
mean diffusivity increase in the frontal lobe of people with
autism25 and is also in line with mean diffusivity increase in
the corpus callosum.23 We also found 3 small regions with de-
creased fractional anisotropy located in the left corona radiata
and the left and right superior and inferior longitudinal fasci-
culus. However, these differences disappeared after correct-
ing for age and, to lesser extent, IQ. This is in line with previ-
ous DTI studies reporting on regional differences in fractional
anisotropy, as these have not employed whole-brain analyses
that were corrected for multiple comparisons and for age and
IQ. The only fractional anisotropy difference we did find was
a higher kurtosis of the fractional anisotropy distribution in
white matter for the autism group, indicating that fractional
anisotropy values are slightly more extreme (outlier-prone) in
people with autism. Contrary to our mean diffusivity find-
ings, the significance of this result was much lower after ad-
justment for age and IQ. We therefore take note of this result
but suspect that it may become insignificant with better
matching or nonlinear adjustment for these covariates.
Combined with prior DTI studies on autism, our results in-
dicate that reduced white matter integrity is among the most
consistent findings of the neuroanatomy of autism.20,21,23 In the
following paragraphs we discuss the implications of our frac-
tional anisotropy and mean diffusivity findings.
Cerebral diffusivity
We found a mean diffusivity increase throughout the white
Right hemisphere Left hemisphere
R
R
Fig. 2: Voxel-wise fractional anisotropy group comparisons uncor-
rected for age and total intelligence quotient (IQ). The highlighted
patches in the figure indicate regions with significantly decreased
fr ac t io nal aniso tr opy (p< 0.0 5, fal se disco ver y rat e [ FDR ]–
corrected) in white matter in the autism group (overlaid on the Mon-
treal Neurological Institu te coordinate 152 T1brain). Fractional
anisotropy is decreased in the left and right superior and inferior
longitudinal fasciculus in temporal/occipital regions and in the left
corona radiata in frontal regions. We found no regions with in-
creased fractional anisotropy in the autism group. Of note: when cor-
rected for age and total IQ, the group differences no longer reach
significance at p< 0.05 FDR–corrected for multiple comparisons.
Fig. 3: Voxel-wise mean diffusivity group comparisons corrected for age and total intelligence quotient. As indicated by the large highlighted
areas, mean diffusivity is increased throughout the brain (overlaid on the Montreal Neurological Institute coordinate 152 T1brain). We found no
regions with decreased mean diffusivity in the autism group.
per-groen_JPN template 15/12/10 8:03 AM Page 37

matter of the cerebrum and cerebellum that extended into the
grey matter. The mean diffusivity histogram (Fig. 1) shows
that both the grey and white matter distributions were
shifted in the autism group, but the difference in grey matter
wa s only s igni fica nt aft er adj ustm ent for a ge and I Q
(Table 3). The effect was greatest in the white matter, possibly
with partial voluming effects driving part of the observed
shift in grey matter mean diffusivity distribution. The mean
diffusivity increase we found in our study is indicative of in-
creased interstitial space, for example due to reduced neural
or glial cell packing or cell size, or decreased water exchange
rate between the intra- and extracellular compartments.28,40,41
We think it is less likely that membrane permeability is af-
fected in people with autism and that mean diffusivity in-
creases are more likely due to differences in cell size or num-
ber, but evidence as to which cells could be affected in people
with autism is scarce. Among the white and grey matter
brain cells are neurons that process and transmit information
and glial cells that far outnumber neurons (about 9 to 1).42
The lack of pervasive concomitant fractional anisotropy ab-
normalities in white matter suggests that the mean diffusivity
increases must be due to abnormalities in isotropic cells such
as glial astrocytes. To date, empirical data have mainly been
focused on neural abnormalities, but activation of neuroglia
has been reported as well.43 Several postmortem studies have
shown that neurons are abnormally large44 in certain struc-
tures of the brain in young autistic children (nucleus of the
diagonal band of Broca in the limbic system). Neurons that
are reduced in number and size have been found in the cere-
bellar nuclei and inferior olive in the brains of adults with
autism.44
Cerebral volumetry
In our VBM analysis we found no region-specific differences
in white or grey matter volume. Ke and colleagues22 reported
deviations in white matter density and dotted spatial cor -
respondence of these abnormalities with differences in frac-
tional anisotropy. Their findings, however, did not match
voxel-by-voxel and were based on a smaller sample (n= 12)
that is more likely to suffer from lack of statistical power and
adjustment for age and IQ. Moreover, a potentially important
difference with our study is that their participants were
on average 6 years younger. In 2 papers, Waiter and col-
leagues45,46 reported on abnormal regional grey and white
matter densities in a well-matched and comparably large
(n= 16) sample of patients with autism. The authors used
separate segmentation and normalization algorithms rather
than the more recent unified segmentation algorithm.39 An-
other difference is that they constructed custom templates
from their sample of participants. We presume that the sup-
posedly greater accuracy of the unified segmentation or per-
haps a lack of power to construct custom templates from
such a sample may well explain the different findings.
Cerebellum
To date, only 1 DTI study involving people with autism has
included the whole cerebellum.26 Although the authors found
white matter integrity reductions as we did, they found frac-
tional anisotropy decreases in combination with normal mean
diffusivity in the superior cerebellar peduncles, whereas we
found mean diffusivity increases in combination with normal
fractional anisotropy in the midcerebellar peduncles. A pos -
sibly important difference between the studies is that in
Catani and colleague’s study,26 adults with Asperger syn-
drome participated, whereas in our study adolescents with
autism participated. Furthermore, findings in the cerebellum
should be interpreted with caution, since the cerebellum is
susceptible for scan artifacts owing to cardiac pulsation. We
controlled for this by using a robust tensor estimation tech-
nique in which artifacts in the DTI images were robustly dis-
carded from the analysis.37 Catani and colleagues26 have con-
trolled for this by gating the data acquisition to the cardiac
cycle. Both methods increase confidence that the measured
differences indeed reflect differences in white matter integrity
and not stress level and heart rate during data acquisition, for
example. It should be noted, however, that cardiac gating is
certainly not a perfect remedy and that in our study, cerebel-
lar fractional anis otropy values were also found to be region-
ally decreased in the autism group when this preprocessing
correction was not applied (data not shown). Moreover, our
preprocessing method detected a larger number of DTI arti-
facts in the autism group (Fig. 4), suggesting that a difference
in cardiac activity is likely and might well be implicated and
providing false-positive results in the cerebellar area. We
speculate that such greater cardiac activity may be due to
greater stress susceptibility in the autism group for the experi-
mental proced ures and MRI acquisition.
Limitations
The nonlinear transformations from the unified segmentation
algorithm were used in our VBM analysis as well as to trans-
form our DTI data to a common space. Furthermore, we used
rigid body transformations to coregister the (susceptibility
Groen et al.
38 J Psychiatry Neurosci 2011;36(1)
Autism group Control group
Fig. 4: Mean number of diffusion tensor imaging (DTI) artifacts. The
highlighted areas denote the maximum intensity projection of the
group averaged voxel-wise number of cardiac artifacts per participant
(i.e., 34 DTI volumes) in the controls (right) versus participants with
autism (left) overlaid on the Montreal Neurological Institute coordin -
ate 152 T1brain. It is apparent from this figure, as well as from the
average total number of affected voxels per participant (mean 562,
standard deviation [SD] 468 v. mean 1265, SD 1249, respectively;
p= 0.010, 2-sided Student ttest) that the DTI images from the autism
group contain more cardiac pulsation artifacts than the control group.
per-groen_JPN template 15/12/10 8:03 AM Page 38

Microstructural abnormalities in autism
J Psychiatry Neurosci 2011;36(1) 39
distorted) DTI images to the corresponding T1image. It is
hence a risk that inaccuracies in these transformations may
result in spurious differences in fractional anisotropy or
mean diffusivity values. However, as we found no differ-
ences in overall brain volume (white matter and grey matter),
or regionally specific differences between the groups, we be-
lieve this is not a concern in the current study. This does not
imply that our nonlinear transformations were free of inaccu-
racies. On the contrary, we believe that the presence of a
maximum in our white matter fractional anisotropy distribu-
tions around the position of the grey matter maximum can be
(at least partly) explained by and is indicative for the reality
of such inaccuracies. The presence of this maximum is most
likely also due to partial voluming, failure of the tensor
model, as well as geometric mismatch with the white matter
mask due to magnetic susceptibility–induced distortions of
the DTI images.
Developmental stage was an important factor to take into
account when studying fractional anisotropy and mean diffu-
sivity differences between our autism and control groups.
We corrected for age and IQ using a linear model, but it may
well be that a more advanced (nonlinear) account is more
powerful. Also, our study would have benefited from using a
larger sample, greater age range and a longitudinal design.
Conclusion
Diffusion tensor imaging studies in patients with autism
published so far have consistently found decreased micro -
structural integrity in white matter in adolescents and adults
with autism. Importantly, our data indicate that, first, the
microstructural integrity is not region-specific but reduced
throughout grey and white matter and, second, this micro -
structural deficit is isotropic and has no volumetric comple-
ment, suggesting that either the number or the size of neuro -
glial cells is reduced in people with autism. It may thus be
especially worthwhile for future diffusion imaging studies to
turn to more advanced diffusion MRI methods, such as q-
space imaging, to elucidate the nature of the microstructural
abnormalities and hopefully shed new light on the neurobio-
logical causes of autism.
Competing interests: None declared by Drs. Groen, van der Gaag
and Zwiers. Dr. Buitelaar reported being a board member and paid
consultant of Janssen Cilag BV and Eli Lilly, receiving a grant from
Eli Lilly and being paid for the development of educational presenta-
tions by Eli Lilly, Janssen Cilag and Medice.
Contributors: All authors helped design the article, analyzed the
data, wrote and reviewed the article and approved its publication.
Dr. Groen acquired the data.
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PRISTIQ is indicated for the symptomatic relief of major
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