Teasing apart the heterogeneity of autism: Same behavior, different brains in toddlers with fragile X syndrome and autism.
ABSTRACT To examine brain volumes in substructures associated with the behavioral features of children with FXS compared to children with idiopathic autism and controls. A cross-sectional study of brain substructures was conducted at the first time-point as part of an ongoing longitudinal MRI study of brain development in FXS. The study included 52 boys between 18-42 months of age with FXS and 118 comparison children (boys with autism-non FXS, developmental-delay, and typical development). Children with FXS and autistic disorder had substantially enlarged caudate volume and smaller amygdala volume; whereas those children with autistic disorder without FXS (i.e., idiopathic autism) had only modest enlargement in their caudate nucleus volumes but more robust enlargement of their amygdala volumes. Although we observed this double dissociation among selected brain volumes, no significant differences in severity of autistic behavior between these groups were observed. This study offers a unique examination of early brain development in two disorders, FXS and idiopathic autism, with overlapping behavioral features, but two distinct patterns of brain morphology. We observed that despite almost a third of our FXS sample meeting criteria for autism, the profile of brain volume differences for children with FXS and autism differed from those with idiopathic autism. These findings underscore the importance of addressing heterogeneity in studies of autistic behavior.
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ABSTRACT: Social avoidance and anxiety are prevalent in fragile X syndrome (FXS) and are potentially mediated by the amygdala, a brain region critical for social behavior. Unfortunately, functional brain resonance imaging investigation of the amygdala in FXS is limited by the difficulties experienced by intellectually impaired and anxious participants. We investigated the relationship between social avoidance and emotion-potentiated startle, a probe of amygdala activation, in children and adolescents with FXS, developmental disability without FXS (DD), and typical development. Individuals with FXS or DD demonstrated significantly reduced potentiation to fearful faces than a typically developing control group (p < .05). However, among individuals with FXS, social avoidance correlated positively with fearful-face potentiation (p < .05). This suggests that general intellectual disability blunts amygdalar response, but differential amygdala responsiveness to social stimuli contributes to phenotypic variability among individuals with FXS.Journal of Autism and Developmental Disorders 05/2014; · 3.06 Impact Factor
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ABSTRACT: Background Through the increased availability and sophistication of genetic testing, it is now possible to identify causal diagnoses in a growing proportion of children with neurodevelopmental disorders. In addition to developmental delay and intellectual disability, many genetic disorders are associated with high risks of psychopathology, which curtail the wellbeing of affected individuals and their families. Beyond the identification of significant clinical needs, understanding the diverse pathways from rare genetic mutations to cognitive dysfunction and emotional–behavioural disturbance has theoretical and practical utility.Methods We overview (based on a strategic search of the literature) the state-of-the-art on causal mechanisms leading to one of the most common childhood behavioural diagnoses – attention deficit hyperactivity disorder (ADHD) – in the context of specific genetic disorders. We focus on new insights emerging from the mapping of causal pathways from identified genetic differences to neuronal biology, brain abnormalities, cognitive processing differences and ultimately behavioural symptoms of ADHD.FindingsFirst, ADHD research in the context of rare genotypes highlights the complexity of multilevel mechanisms contributing to psychopathology risk. Second, comparisons between genetic disorders associated with similar psychopathology risks can elucidate convergent or distinct mechanisms at each level of analysis, which may inform therapeutic interventions and prognosis. Third, genetic disorders provide an unparalleled opportunity to observe dynamic developmental interactions between neurocognitive risk and behavioural symptoms. Fourth, variation in expression of psychopathology risk within each genetic disorder points to putative moderating and protective factors within the genome and the environment.ConclusionA common imperative emerging within psychopathology research is the need to investigate mechanistically how developmental trajectories converge or diverge between and within genotype-defined groups. Crucially, as genetic predispositions modify interaction dynamics from the outset, longitudinal research is required to understand the multi-level developmental processes that mediate symptom evolution.Journal of Child Psychology and Psychiatry 12/2014; · 5.42 Impact Factor
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ABSTRACT: Progress in basic neuroscience has led to identification of molecular targets for treatment in fragile X syndrome (FXS) and other neurodevelopmental disorders; however, there is a gap in translation to targeted therapies in humans. One major obstacle to the demonstration of efficacy in human trials has been the lack of generally accepted endpoints to assess improvement in function in individuals with FXS. To address this problem, the National Institutes of Health convened a meeting of leading scientists and clinicians with the goal of identifying and standardizing outcome measures for use as potential endpoints in clinical trials in FXS. Participants in the meeting included FXS experts, experts in the design and implementation of clinical trials and measure development, and representatives from advocacy groups, industry, and federal agencies. The group generated recommendations for optimal outcome measures in cognitive, behavioral, and biomarker/medical domains, including additional testing and validation of existing measures and development of new measures in areas of need. Although no one endpoint or set of endpoints could be identified that met all criteria as an optimal measure, recommendations are presented in this report. The report is expected to guide the selection of measures in clinical trials and lead to the use of a more consistent battery of measures across trials. Furthermore, this will help to direct research toward gaps in the development of validated FXS-specific outcome measures and to assist with interpretation of clinical trial data by creating templates for measurement of treatment efficacy.Journal of developmental and behavioral pediatrics: JDBP 09/2013; 34(7):508-522. · 2.12 Impact Factor
Teasing apart the heterogeneity of autism:
Same behavior, different brains in toddlers
with fragile X syndrome and autism
Heather Cody Hazlett & Michele D. Poe & Amy A. Lightbody & Guido Gerig &
James R. MacFall & Allison K. Ross & James Provenzale & Arianna Martin &
Allan L. Reiss & Joseph Piven
Received: 23 September 2008 /Accepted: 15 February 2009 /Published online: 5 March 2009
# Springer Science + Business Media, LLC 2009
Abstract To examine brain volumes in substructures associ-
ated with the behavioral features of children with FXS
compared to children with idiopathic autism and controls. A
cross-sectional study of brain substructures was conducted at
the first time-point as part of an ongoing longitudinal MRI
study of brain development in FXS. The study included 52
boys between 18–42 months of age with FXS and 118
comparison children (boys with autism-non FXS,
developmental-delay, and typical development). Children
with FXS and autistic disorder had substantially enlarged
caudate volume and smaller amygdala volume; whereas those
children with autistic disorder without FXS (i.e., idiopathic
autism) had only modest enlargement in their caudate nucleus
volumes but more robust enlargement of their amygdala
volumes. Although we observed this double dissociation
among selected brain volumes, no significant differences in
severity of autistic behavior between these groups were
observed. This study offers a unique examination of early
brain development in two disorders, FXS and idiopathic
autism, with overlapping behavioral features, but two distinct
a third of our FXS sample meeting criteria for autism, the
profile of brain volume differences for children with FXS and
autism differed from those with idiopathic autism. These
findings underscore the importance of addressing heteroge-
neity in studies of autistic behavior.
Autism is a behavioral syndrome defined by the presence of
social deficits, abnormalities in communication, the presence
of stereotyped, repetitive behaviors, and a characteristic
course . The clinical phenotype is widely regarded as
heterogenous with diagnosed individuals ranging from those
with no functional language to more subtle deficits in
pragmatic language; and ritualistic, repetitive behaviors
J Neurodevelop Disord (2009) 1:81–90
H. C. Hazlett:J. Piven
Carolina Institute for Developmental Disabilities,
The University of North Carolina at Chapel Hill,
Chapel Hill, NC, USA
J. R. MacFall:J. Provenzale
Department of Radiology, Duke University Medical Center,
Durham, NC, USA
M. D. Poe
Frank Porter Graham Child Development Institute,
The University of North Carolina at Chapel Hill,
Chapel Hill, NC, USA
A. A. Lightbody:A. Martin:A. L. Reiss
Department of Psychiatry and Behavioral Sciences,
Stanford University School of Medicine,
Stanford, CA, USA
A. K. Ross
Department of Anesthesiology, Duke University Medical Center,
Durham, NC, USA
Scientific Computing and Imaging Institute, University of Utah,
Salt Lake City, UT, USA
H. C. Hazlett (*)
Department of Psychiatry,
The University of North Carolina at Chapel Hill,
Chapel Hill, NC 27599-3367, USA
ranging qualitatively from motor stereotypies to difficulties
tolerating minor changes in routine. Cognitive abilities may
range from severe mental retardation to above average
Clinical heterogeneity is thought to be due to underlying
etiologic heterogeneity. The main argument in support of
etiologic heterogeneity in autism has come from studies
showing that approximately 10% of individuals with autism
have an identifiable medical condition of known etiology
. The existence of distinct mechanisms underlying these
medical conditions is often put forward as the best evidence
that multiple etiologic pathways lead to autistic behavior.
Etiologic heterogeneity is often invoked as a major reason
for non-replication in studies of autism. To date there has
not been consistent evidence that particular profiles of
autistic behavior are specifically associated with biologi-
cally defined subgroups of autistic individuals, although
associated features (e.g., hyperarousal in the case of fragile
X syndrome) are often noted to differ between individuals
affected with these various medical conditions and autism.
Fragile X syndrome (FXS) is a well characterized X-
linked genetic disorder and the leading cause of heritable
intellectual disability . FXS is one such condition that
has been strongly associated with autistic behavior. Ap-
proximately one third of individuals with FXS meet criteria
for autistic disorder [4, 5] and approximately 1–3% of
individuals with autistic disorder are found to have FXS. In
studies of boys with the full mutation for FXS, behaviors
similar but milder than those seen in autistic disorder have
been observed, such as social deficits with peers, abnor-
malities in communication, unusual responses to sensory
stimuli, stereotypic behavior, social avoidance, and gaze
aversion [6–11]. Morphological brain abnormalities on
MRI have been described in individuals with FXS and in
individuals with autistic disorder  but to date, no studies
have directly compared brain structure in autistic indivi-
duals with and without FXS.
Converging evidence suggests that brain volume en-
largement is a characteristic feature of autism, with onset of
this enlargement most likely occurring in the latter part of
the first year of life [13–21]. We found generalized
enlargement of cerebral cortical gray and white matter in
a large sample of 2 year olds with autism compared to
controls . Increased volume of selected subcortical
structures (i.e., amygdala, caudate nucleus), along with
decreased size of others (i.e., corpus callosum) have also
been reported in autistic individuals [23–25], and more
recently our group reported amygdala enlargement in
toddlers with autism .
Neuroimaging studies have shown specific subcortical
thalamus (in females), and lateral ventricles (see review by
Hessl et al. 2004 ). Increased caudate volume has also
been reported in individuals with FXS in association with
severity of stereotyped, repetitive behavior [12, 28].
In the present study we contrast the morphological patterns
of selected subcortical structures—components of the basal
ganglia (caudate, putamen/globus pallidus), amygdala, and
hippocampus—in individuals meeting DSM-IV criteria for
autistic disorder with and without FXS. Specifically, we
examined the brain volumes in boys with FXS compared to a
group of controls and to a group of children with idiopathic
autism (without FXS). We also compared a subgroup of
children with FXS in our sample who met criteria for autism
with the group of children with idiopathic autism. We
hypothesized that we would find significant brain volume
differences in the children with FXS compared to controls,
and that the pattern of brain volumes might differ for children
who hadbothFXSandautism. Studiestodatehave compared
autistic individuals with controls and individuals with FXS
with controls, but have not examined the neuroanatomical
profiles underlying autistic individuals defined by both the
presence and absence of the fragile X mutation.
Subjects were combined from two sites (Stanford Univer-
sity (SU) and the University of North Carolina (UNC)) and
included 52 male children with FXS (with and without
autism) and 113 male comparison cases, 18–42 months of
age. In the comparison group, there were 63 boys with
idiopathic autism (AUT), 19 boys with developmental
delay (DD) and 31 boys with typical development (TYP).
Children with FXS were recruited from both the SU and
UNC registry databases, postings on the National Fragile X
Foundation website and quarterly newsletter, and mailings
to regional FXS organizations. Children with autism were
primarily referred from nine specialty clinics for pervasive
developmental disorders in North Carolina (TEACCH,
Treatment and Education of Autistic and related Commu-
nication Handicapped Children) and were referred from
community clinics for the SU sample. Subjects with autism
were referred after receiving a clinical diagnosis of an
autism spectrum disorder. Children with DD and TD were
recruited locally through early intervention programs, pre-
schools, child care centers, community media, and state run
agencies (Regional Center system in California and Child
Development Service Agencies in North Carolina).
Subjects were enrolled between 18 and 42 months of age.
Medical records and developmental history were reviewed
82J Neurodevelop Disord (2009) 1:81–90
for all subjects. Inclusion in the FXS group required DNA
testing confirming the fragile X full mutation as diagnosed
with standard Southern Blot technique. These children also
received testing for the fragile X mental retardation protein
(FMRP) expression by calculating the percentage of
peripheral lymphocytes containing FMRP using immunos-
taining techniques . Subjects with autism were included
after receiving a clinical diagnosis of autism, which was
then confirmed by our team using the Autism Diagnostic
Interview—Revised (ADI-R)  and the Autism Diag-
nostic Observation Schedule-G (ADOS-G) . Subjects
were only included in the AUT group if they met criteria
for autism in all domains of the ADI-R and ADOS-G, and
if they showed no evidence for the fragile X mutation on
the DNA testing. Because the children with autism were
participants in a longitudinal they were re-assessed using
these measures at age 4, and we were able to confirm the
classification of autism at this time. Only children who met
full autism criteria at both timepoints (age 2 and 4) were
included in our autism group. Inclusion in the DD group
was defined as having significant global delays (develop-
mental IQ≤80), scores consistent with DD on the other
assessment measures, no known identifiable cause for their
delay (on medical record review), and no indication of a
pervasive developmental disorder. Inclusion in the TYP
group was defined as having average developmental and
cognitive abilities (i.e. developmental IQ≥85).
Subjects were excluded for evidence of medical or
genetic conditions such as Tuberous Sclerosis (TS), gross
CNS injury (e.g., cerebral palsy, significant complications
or perinatal/postnatal trauma, drug exposure), prematurity
(<34 weeks), low birth weight (<2000 g), seizures, and
significant motor or sensory impairments. Medical records
were reviewed for any evidence of autism or PDD-NOS for
the DD and TYP subjects and they were excluded from
these groups for any evidence of these disorders. DD and
TYP children were screened for autism with the Childhood
Autism Rating Scale  and excluded if they approached
the cutoff for autism (≥25 total score). All autistic and DD
subjects received testing (cytogenetics or molecular) to
All subjects were given a battery of measures
including the Mullen Scales of Early Learning ,
the Vineland Adaptive Behavior Scales , behavioral
rating scales (e.g., Repetitive Behavior Scales), and a
standardized neurodevelopmental examination to exclude
subjects with any notable dysmorphology, evidence of
neurocutaneous abnormalities, or other significant neuro-
logical abnormalities. Study approval was acquired from
both the SU and UNC/Duke Institutional Review Boards
and written informed consent was obtained by getting
parental (or custodial guardian) consent for each subject.
Table 1 displays the subject characteristics (e.g., age,
cognitive ability, and adaptive functioning) of the final
All subjects were scanned on a 1.5 Tesla GE Signa MRI
scanner (GE Imagine Systems, Milwaukee, WI) at either
Stanford-Lucile Packard Children’s Hospital or Duke-UNC
Brain Imaging and Analysis Center (BIAC). Image acqui-
sition was designed to maximize gray/white tissue contrast
for the pediatric brain and included: (1) a coronal T1 IR
Prepared: T1 300 ms, TR 12 ms, TE 5 ms, 20° flip angle, at
1.5 mm thickness with 1 NEX, 20 cm FOV; and 256×192
matrix; (2) a coronal PD/T2 2D dual FSE, TR 7200 ms, TE
17/75 ms, at 3.0 mm thickness with 1 NEX, 20 cm FOV,
and 256×160 matrix. A series of localizer scans and a set of
phantoms was used to standardize assessments across sites
and time (for the longitudinal study).
Subjects with FXS, autism and DD were scanned using
sedation administered by a sedation nurse and under the
supervision of a pediatric anesthesiologist. Physiological
monitoring was conducted throughout the scan and reco-
very. TYP subjects were scanned without sedation, in the
evening, while sleeping. All scans were reviewed by a
pediatric neuroradiologist and screened for significant
clinical abnormalities (e.g., malformations, lesions, etc.).
Our segmentation procedure involved an automated
pipeline that utilized a probabilistic spatial prior tem-
plate (atlas) aligned to our subject MRI using a linear,
affine transformation and included bias estimation,
inhomogeneity correction, and non-brain stripping pro-
cedures. The result was gray, white, and CSF tissue
segmented images for each subject. The methods and
procedures are described elsewhere [35–39]. Total brain
volume (TBV) measures included total gray and white
matter and all CSF. Total tissue volume (TTV) included all
gray and white matter in the cerebrum and cerebellum.
These measures were obtained to get a covariate of TBV
to be used in the substructure analyses. Standardized
tracing protocols used for each of the substructures we
examined are briefly described below. All brain volume
measurements were completed by raters in the UNC image
processing lab. Reliability was obtained by two raters who
made independent measurements on a set of 15 images,
which included 5 images repeated 3 times (in random
order). We were unable to successfully process some scans
for all the regions examined secondary to insufficient scan
quality or artifact. Group comparisons showed no bias (e.
g., group, gender, age) in association with scans that could
not be successfully segmented.
J Neurodevelop Disord (2009) 1:81–9083
Caudate nucleus (CN) The CN was measured on high
resolution T1 images in ACPC alignment using a semi-
automated 3D segmentation tool (IRIS/SNAP [40, 41]) that
employs a user-defined threshold window, initialization,
and region-growing parameters. This semi-automated meth-
od is more reliable and efficient than a fully manual
protocol. The tool automatically finds tissue boundaries and
will label the caudate. The segmentation label can then be
manually edited as necessary, so that the caudate trace
excluded the nucleus accumbens. The average intra-rater
reliability was r=0.97 and average inter-rater reliability was
r=0.96. There was 1 FXS, 2 AUT, 4 DD, and 3 TYP scans
that were unable to be processed successfully and these
were excluded from analyses.
Putamen/Globus Pallidus The PUT and GP were manually
traced on high resolution T1 images in ACPC alignment as
a combined structure using the IRIS/SNAP tool. The major
boundaries of the combined PUT and GP are the internal
and external capsules. A second step involved using
designated landmarks to separate the two structures. The
average intra-rater reliability for the PUT was r=0.97 and
for the GP was r=0.93. The average inter-rater reliability
for the PUTwas r=0.97 and the GP was r=0.83. Due to the
difficulty in obtaining manual traces of the PUT/GP, there
were 2 FXS, 8 autism, 8 DD, and 3 TYP cases that were of
insufficient quality to be included.
Amygdala (AMY) The AMY was manually traced on high
resolution T1 images aligned along the long axis of the
hippocampus using the IRIS/SNAP tool following a protocol
developed by the Center for Neuroscience and the M.I.N.D.
at UC Davis . We first established our reliability with the
UC Davis group (average inter-rater reliability r=0.92) to
ensure we had been adequately trained on the protocol.
Subsequently, reliability was established on scans from our
sample of 18–35 month olds. Average intra-rater reliability
was r=0.90, and inter-rater was r=0.78. A single rater
(r=.90) completed all the AMY traces. There was 1 FXS, 4
AUT, 2 DD, and 4 TYP scans that were of insufficient
quality to obtain a valid AMY trace and were excluded.
Hippocampus The HIP was obtained using a semi-
automated tool where the user defines landmarks but the
HIP is automatically segmented via a high-dimensional
deformation of a template (MOJO [43–46]). The unaligned
T1 gray level image is the input image to the MOJO tool.
The average intra-rater reliability was 0.95 and average
inter-rater was 0.81. There were 3 FXS, 1 AUT, 3 DD, and
3 TYP cases that were excluded because the HIP could not
be adequately visualized to perform the segmentation.
Descriptive statistics and data plots were first examined to
look for anomalous data or outliers. No anomalous data was
observed or removed. We used a multivariate analysis (a
repeated measures mixed model) in SAS 9.1 to test our a
priori hypotheses (see Gueorguieva & Krystal, 2004 
for a description of this approach). The brain volume
measure was the dependent variable, with diagnostic group
as the predictor. Diagnostic group was entered as a 5 level
categorical variable (FXS, FXS+Aut, AUT, DD, TYP). A
model was fit examining group differences for 5 substruc-
tures: AMY, CN, HIP, GP, and PUT. This model included
up to 10 observations per subject. All analyses adjusted for
the effects of age and IQ ratio on the measured brain
volume by including them as covariates. TBV was included
in the model to test for disproportionate differences in the
subcortical volumes. Data collection site (UNC, SU) was
not included as a predictor because no systematic difference
in brain volumes (GM, WM, CSF, TTV, and 5 substructures
of interest) were observed between sites.
Our primary hypotheses focused on three group compar-
isons (FXS vs AUT vs Controls). For these comparisons,
combined estimates for ‘controls’ (DD + TYP) and ‘FXS’
(FXS and FXS+Aut) were created using post-estimation
commands to create weighted averages. By using a
weighted average of the subgroups the combined group
estimates are accurate estimates of the means, while the
possible error variance that could be accounted for by mean
group differences is minimized. Our secondary analysis
Table 1 Sample characteristics
Site (SU/UNC) Age (years) M (SD)IQ EstimatebM (SD)Adaptive BehcM (SD)
Fragile X Syndrome
aAll subjects were males
bIQ Estimate from Mullen Composite Standard Score
cAdaptive behavior estimate from Vineland Adaptive Behavior Composite
84 J Neurodevelop Disord (2009) 1:81–90
included four group comparisons (FXS+Aut, AUT, DD,
TYP). However, only a single model was fit to obtain these
Laterality was assessed by examining the significance of
interactions between group and hemisphere. A significant
group by hemisphere interaction indicates that the group
difference varies significantly by side (left vs right).
A description of the sample (N, age, IQ) appears in Table 1.
Group differences were evaluated for age, adaptive func-
tioning from the Vineland Adaptive Behavior Scales-
Interview Edition Survey Form , and developmental
IQ from the Mullen Scales of Early Learning . Age
differences were observed, with the TYP group being
slightly younger, so age was included as a covariate. As
mean IQ for combined (TYP + DD) control group was
higher than the FXS and AUT groups, IQ was also included
as a covariate.
Substructure volumes: comparing FXS with control
and autism groups
Age-adjusted means are reported in Table 2 and compa-
risons for total substructure volumes are reported in Table 3
(adjusted for age, IQ, and TBV). The main effect of group
varied between structures (p<.001), therefore the effect of
group is reported individually for each structure.
CN and PUT/GP Total CN volume was significantly
enlarged in the FXS group compared to the control
(40%), AUT (26%), TYP (34%), and DD (45%) groups.
This pattern was also observed for right and left CN
volumes, suggesting no laterality effect. Total PUT volume
was significantly enlarged in the FXS group compared to
the control (8%), TYP (8%), and DD (9%) groups, and
were larger (but not significantly) than the AUT group
(3%). No laterality of the PUT was observed. Volume of the
GP was significantly enlarged in the FXS group compared
to the control (13%), TYP (10%), and DD (16%) groups.
No laterality of the GP volumes was observed.
Amygdala and Hippocampus FXS subjects had smaller
AMY volumes than the control (−7%), DD (−7%), and
TYP (−8%) groups, but only differences with the control
group were significant. The FXS group was significantly
smaller than the AUT group (−19%). The right and left
AMY volumes followed this same pattern (see Table 3),
suggesting no laterality effect. The FXS group had
significantly larger HIP than the DD group (26%), but
showed no significant differences with other groups.
Comparison of FXS subgroups: with and without autism
We identified children in the FXS group who also met
criteria for autistic disorder, referred to as FXS with
autism (“FXS+Aut”). The children with FXS who did not
meet our autism criteria were labeled as FXS without
autism (“FXS−Aut”). Membership in the FXS+Aut sub-
group required meeting cut-offs for autistic disorder on the
ADI-R and the ADOS-G. Using this classification
scheme, there were 17 children with FXS (33%) who also
met criteria for autistic disorder. This is comparable to
FXS Mean (SE) Autism Mean (SE)DD Mean (SE) TYP Mean (SE)
Table 2 Adjusted mean volumes
(cm3) for substructures by group
aMeans adjusted for age
J Neurodevelop Disord (2009) 1:81–9085
the rate of autistic disorder observed in toddlers and
preschoolers with FXS reported by Rogers et al. 2001.
Age adjusted means for the FXS subgroups were similar
to those observed in the total FXS sample. Percent
differences in substructure brain volumes for FXS,
AUT, and FXS+Aut groups compared to the controls
are displayed in Fig. 1. Comparison of the FXS+Aut
group to the AUT group revealed significant CN enlarge-
ment (p<.001), and significantly smaller AMY (p<.001).
The CN enlargement in the FXS+Aut and FXS−Aut
groups compared to controls was dramatic in comparison
to the AUT group. However, these groups showed
opposite trends in their AMY volume, with the AUT
group showing enlargement but the FXS groups showing
decreased volume compared to controls. There were no
significant differences in CN volume between the
FXS+Aut and FXS−Aut groups, but both of these groups
had significantly larger CN volume compared to the AUT
group (see Fig. 2). Volumes of the AMY for the FXS+Aut
and FXS−Aut were significantly smaller than in the AUT
group, and there was approximately a 5% difference (not
significant) between the FXS+Aut and FXS−Aut sub-
groups (see Fig. 2). As was observed in the overall FXS
group comparison, we observed a double dissociation of
greatly enlarged CN and small AMY in FXS, regardless of
brain–behavior relationships differed between the FXS+Aut,
FXS−Aut, and AUT groups. ADI-R subdomain scores are
presented in Table 4. Examination for clinical correlates using
global autism measures (ADI-R algorithm subdomain scores;
ADOS algorithm domain scores) as well as more refined
measures of repetitive behaviors (e.g. RBS-R) did not reveal
any significant brain-behavior correlations in any of these
groups (FXS+Aut, FXS−Aut, AUT).
In this study we observed significant differences in the
neuroanatomical profiles of male children with autistic
disorder with FXS relative to those who did not have FXS.
Specifically, boys with both FXS and autism had substan-
tially enlarged CN volume and smaller AMY volume
compared to boys with FXS without autism. In contrast,
boys with idiopathic autism (no FXS) had only modest
enlargement in their CN volumes compared to controls, but
more robust enlargement of their AMY volumes. Although
Table 3 Group* comparisons for selected substructure volumes controlling for age, IQ, and TBV
RegionFXS v Controls Diff (SE), % diff FXS v DD Diff (SE), % diffFXS v TYP Diff (SE), % diff FXS v AUT Diff (SE), % diff
2.67 (.4), 40% ***
1.33 (.2), 39% ***
1.34 (.2), 41% ***
2.62 (.4), 45% ***
1.45 (.2), 44% ***
1.47 (.2), 46% ***
2.41 (.5), 34% ***
1.20 (.2), 34% ***
1.21 (.2), 35% ***
1.93 (.3), 26% ***
0.98 (.1), 26% ***
0.96 (.1), 26% ***
0.67 (.3), 8% **
0.34 (.1), 8% **
0.33 (.1), 8% **
0.71 (.3), 9% *
0.36 (.2), 9% *
0.35 (.2), 9% *
0.64 (.3), 8% *
0.32 (.2), 8% *
0.31 (.2), 8%
0.30 (.2), 3%
0.18 (.1), 4%
0.12 (.1), 3%
0.35 (.1), 13% ***
0.18 (.1), 13% ***
0.17 (.1), 13% ***
0.42 (.1), 16% ***
0.22 (.1), 17% ***
0.20 (.1), 15% **
0.27 (.1), 10% *
0.13 (.1), 9% *
0.14 (.1), 10% *
0.13 (.1), 4% *
0.10 (.04), 7% *
0.02 (.04), 2%
−0.25 (.1), −7% *
−.14 (.1), −8% *
−.12 (.1), −7%
−0.24 (.2), −7%
−0.11 (.1), −6%
−0.13 (.1), −8%
−0.27 (.2), −8%
−0.16 (.1), −9%
−0.10 (.1), −6%
−0.73 (.1), −19% ***
−0.40 (.1), −20% ***
−0.33 (.1), −18% ***
0.39 (.3), 7%
0.26 (.2), 10%
0.12 (.2), 5%
1.16 (.3), 26% ***
0.64 (.2), 28% ***
0.52 (.2), 24% **
−0.39 (.4), −6%
−0.11 (.2), 4%
−0.27 (.2), −9%
−0.22 (.2), −4%
−0.02 (.1), −1%
−0.20 (.1), −7%
Note: FXS group refers to entire sample of children with FXS. The Control group contains both DD and TYP subjects, but comparisons with the
DD and TYP subgroups are also displayed separately.
*p<.05; **p<.01; ***p<.001
86J Neurodevelop Disord (2009) 1:81–90
observing this double dissociation among selected brain
volumes, no significant differences in severity of autistic
behavior as measured by subdomains of the ADI-R were
detected between these two study groups. This study
therefore provides evidence of a substantially different
pattern of brain structures in two clinical populations with
presentations of autistic behavior. The findings in the
present study suggest that heterogeneity may be under-
estimated in studies attempting to identify common biolog-
ical underpinnings of individuals meeting DSM IV
behavioral criteria for the behaviorally defined syndrome
of autism (e.g., genetic linkage studies). Clearly the study
of biological mechanisms underlying autistic behavior in
etiologically-defined subgroups such as those with FXS, is
an important and probably under-employed strategy for
dealing with the heterogeneity issue.
* p < .05; ** p < .01; *** p < .001
Substructure Volume Differences
Caudate Globus Pallidus PutamanAmygdala Hippocampus
All FXS vs Controls
Aut vs Controls
FX+Aut vs Controls
Fig. 1 Percent differences in
substructure brain volumes for
FXS, AUT, and FXS+Aut
groups compared to controls
*p<.05; **p<.01; ***p<.001
* p < .05; ** p < .01; *** p < .001
Substructure Volume Differences
FXS+Aut vs Aut
FXS-Aut vs Aut
Fig. 2 Percent differences in
caudate and amygdala volumes
for children with FXS who either
met autism criteria (FXS+Aut) or
did not meet criteria (FXS−Aut)
compared to children with
Autism *p<.05; **p<.01;
J Neurodevelop Disord (2009) 1:81–9087
The finding of enlarged CN in FXS is consistent with
other studies finding enlargement of the CN in FXS [12, 28,
48], and the magnitude of enlargement (∼40%) suggests
this is a robust finding in children with FXS. The CN has
been implicated in the repetitive behaviors seen in both
autism and FXS. One possible cause for such an enlarge-
ment of the CN in FXS may be linked to the underlying
genetics of the disorder. An association has been reported
between a measure of FMR1 gene inactivation (activation
ratio-AR) with caudate volume, and an association of IQ
with both caudate and ventricular volumes . We also
know that FMRP has also been shown to play a direct role
in brain development  and decreased FMRI protein has
been associated with the cognitive deficits seen in FXS
[51–54]. The significant CN enlargement observed in FXS,
in this study almost 3–4 times greater than in controls and
about 3 times greater than cases with autism, may be more
related to the FXS mutation versus diagnosis of autism,
since the findings in the FXS autistic and non-autistic
individuals are the same as those in the overall FXS group,
regardless of autism status.
We report decreased AMY volume in our FXS group
(with and without autism) and enlarged AMY in our AUT
group. Our finding of increased AMY volume in our AUT
sample is consistent with other reports of increased AMY in
young children with autism, where AMY enlargement has
also been associated with deficits in social behavior [21,
55] and social orienting . Our finding of decreased
AMYvolume in boys with FXS is contrary to some reports
of AMY enlargement in FXS , but consistent with
studies that included very young children . Dalton and
colleagues hypothesized that early AMYenlargement is the
result of amygdalar hyperactivity and hypertrophy, in
response to the aversive nature of social stimulation in
autism, with subsequent decreased AMY volume occurring
as the aversive stimulation becomes chronic [56, 57]. Here
we find support to suggest that children with FXS have
decreased AMY despite having social deficits characteristic
of autism. On the other hand, children with autism have
enlargement of the AMY, yet display the same behaviors. It
may be that having an AMY either too large or too small
are ‘two sides of the same coin’ in the same way that hyper
or hypo function of the MECP2 gene both result in the Rett
Syndrome phenotype .
We did not find phenotypic differences in autistic
behavior, as measured by the ADI-R and ADOS in our
FXS+Aut and AUT groups. The defining features of autism
as they appear in the DSM, while perhaps good ways to
characterize the most impairing clinical features of autism,
are not necessarily the best phenotypic features for
separating out the underyling etiologic heterogeneity. Other
behavioral features such as hyperarousal, thought to be
more characteristic of FXS, may better distinguish groups
of autistic individuals with and without FXS and may
eventually find a place in studies of the autistic phenotype.
Clearly additional studies which seek to identify different
behavioral profiles in autistic individuals with and without
FXS would provide important clues to meaningfully
subsetting the autism phenotype.
In this study, we also found evidence for significantly
enlarged CN volume in FXS compared to our control
group, and with and TYP and DD subgroups. We found
significant enlargement in the PUT and GP in the FXS
group compared to the controls. This is the first report of
significant enlargement in the PUT and GP structures in
FXS, and as part of the fronto-striatal circuit with the
caudate, this enlargement provides support for a neuroan-
atomical abnormality in this pathway in FXS. We also
found significant group differences for the HIP volumes
between the FXS and DD groups.
There are several limitations in the current study. The size
of our subgroups of the controls (DD and TYP) and FXS
(with and without autism) was modest and is a limitation.
The heterogeneity we observe in idiopathic autism may
have also limited the generalizability of the findings in this
study. We only examined male children and therefore our
findings may not generalize to females. Lastly, the measures
we employed for behavioral assessment (ADI-R, ADOS)
were developed for categorical diagnosis of autistic disorder
and not for use in the way we have done—contrasting items
and domains. Tools designed for this that have better
dimensional qualities and that examine more varied behav-
iors (e.g., arousal, face processing using eye gaze, etc.) may
have revealed behavioral differences that more closely
correlated with the neuroanatomical differences observed
between the groups. The study also has a number of
strengths, most notable the large sample size of our autism
and FXS groups within the narrow age range we examined.
There is good evidence of age-dependent variation in
neuroanatomical structures in autism (albeit indirectly from
cross sectional studies ) and both FXS and ideopathic
autism are well known to be developmental conditions
where presentations vary with age.
In conclusion, this study offers a unique examination of
early brain development in two behavioral overlapping
Table 4 ADI-R subdomain scores for FXS with autism (FXS+Aut)
and Autism (AUT) groups
ADI-R subdomain FXS+Aut M(SD)AUT M(SD)
88J Neurodevelop Disord (2009) 1:81–90
disorders, FXS and autism, and finds two distinct patterns
of brain morphology. The present study finds uniquely
different neurodevelopmental profiles for these two beha-
viorally similar disorders and suggests that comparative
neuroimaging studies may provide the best window into
teasing apart genetically meaningful aspects of the autism
phenotype in FXS. These findings also underscore the
importance of addressing heterogeneity in studies of autistic
behavior. Simply mentioning that it exists and that it may
be the cause of a lack of replication, may not be enough as
the continued failure to take etiologic heterogeneity into
account in autism is likely to continue to handicap our best
efforts to find circumscribed genetic and neurobiologic
mechanisms underlying this condition. Studies examining
the molecular basis for this difference may provide the best
approach to getting a foot-hold into the pathogenesis of
autistic behavior. For example, mouse studies have revealed
that while social deficits qualitatively similar to those seen
in autism are present in C57B6 FX (−/−) mice, they are not
observed in FX (−/−) on a FVB background. Such findings
suggest that it is the effect of interacting genes (and perhaps
environment) on FMR1 that may have a role in causing
abnormal social behavior, and similarly, studying genes
interacting with FMR1 in humans with autism may provide
clues to the genetic profiles causing autism in this and
perhaps even subgroups of autistic individuals. Clearly
working in an iterative fashion—going from genotype to
phenotype and back again from phenotype to genotype,
may eventually reveal more subtle phenotypic variations,
perhaps outside of the traditional defining features of
autism, to distinguish underlying etiologically-defined
subgroups that could be employed to find new autism
05 (J Piven, A Reiss), MH61696 (J Piven), and P30 HD03110 (J
Piven). We express our appreciation for the assistance we received
from the following: the NC TEACCH centers, the UNC NDRC
Autism and Fragile X Subject Registries, and NC Children’s
Developmental Services Agencies for assisting with recruitment;
Chad Chappell, Judy Morrow, Nancy Garrett, Robin Morris, Cindy
Hagan, Cindy Johnston, Cristiana Vattuone, Ahn Weber, and Christa
Watson for their work with these families; Martin Styner, Rachel
Gimpel Smith, Swetapadma Patnaik, Michael Graves and Matthew
Mosconi for image processing support; and most importantly, all the
families who have participated in this study.
Research supported by NIH grants MH64708-
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