Neurobiological Abnormalities in the First Few Years of
Life in Individuals Later Diagnosed with Autism Spectrum
Disorder: A Review of Recent Data
C. S. Allely,1C. Gillberg,1and P. Wilson2
1Institute of Health and Wellbeing, University of Glasgow, Caledonia House, Royal Hospital for Sick Children, Yorkhill,
Glasgow G3 8SJ, UK
2Centre for Rural Health, University of Aberdeen, The Centre for Health Science, Old Perth Road, Inverness IV2 3JH, UK
Correspondence should be addressed to C. S. Allely; email@example.com
Received 22 February 2013; Accepted 23 June 2013; Published 9 February 2014
Academic Editor: Argye E. Hillis
Copyright © 2014 C. S. Allely et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background. Despite the widely-held understanding that the biological changes that lead to autism usually occur during prenatal
life, there has been relatively little research into the functional development of the brain during early infancy in individuals later
diagnosed with autism spectrum disorder (ASD). Objective. This review explores the studies over the last three years which have
We used PRISMA guidelines and selected published articles reporting any neurological abnormalities in very early childhood in
individuals with or later diagnosed with ASD. Results. Various brain regions are discussed including the amygdala, cerebellum,
frontal cortex, and lateralised abnormalities of the temporal cortex during language processing. This review discusses studies
investigating head circumference, electrophysiological markers, and interhemispheric synchronisation. All of the recent findings
from the beginning of 2009 across these different aspects of defining neurological abnormalities are discussed in light of earlier
findings. Conclusions. The studies across these different areas reveal the existence of atypicalities in the first year of life, well before
ASD is reliably diagnosed. Cross-disciplinary approaches are essential to elucidate the pathophysiological sequence of events that
lead to ASD.
1.1. Clinical Importance of Early Identification. Autism spec-
trum disorder (ASD) is a relatively common, neurodevelop-
mental disorder with onset of symptoms in the first few years
cases . ASD is diagnosed in around 1% of the population
[3, 4] and was once considered to be a rare psychological
disorder due to poor parenting . Despite recent advances
in the ability to identify ASD earlier, diagnosis is frequently
not made prior to approximately three years. Currently, no
reliable predictors of ASD in infancy exist but characteristic
behaviours emerge during the second year which are used
to aid diagnosis [6–8]. Study into the neurological basis
of ASD before the age of three years is imperative [9, 10].
in childhood in primary care is important as it may improve
outcomes [11, 12]. Absence of robust biological markers for
identifying ASD has led researchers to focus on behavioural
anomalies in order to detect early symptoms of ASD .
A number of novel lines of investigation have been used
to this end, including retrospective coding of home videos
[14–16], prospective population screening [17–19], and “high
risk” sibling studies [20–22] as well as the investigation of
pre- and perinatal brain development and other biological
factors. Early social abnormalities are not easily identifiable
in the first year of life in infants who later receive a diagnosis,
since they may be present at subtle and subclinical levels.
Hindawi Publishing Corporation
Volume 2014, Article ID 210780, 20 pages
Motor abnormalities, in particular, may be one of the earliest
markers observable within the first year . Recent reviews
(i.e., ) have found evidence for putative ASD biomarkers
[26, 27], heavy metal toxicity [28–30], neurotransmitter
abnormalities [31–33], oxidative stress [34, 35], and elevated
levels of p-cresol in small children with ASD . This
research suggests that ASD might best be considered to be
a multisystem disorder.
ered to be the optimal time in which to detect and examine
the earliest fundamental biological underpinnings of autism
. This review specifically focuses on studies published
were neurological or pathophysiological abnormalities in the
first few years of life in individuals later diagnosed with
ASD. To our knowledge, this is the first review to focus only
on abnormalities within the first few years of life but there
have been recent reviews investigating neuroanatomical dif-
will address structural abnormalities (e.g., atypical volume
of neural sites, morphology), functional abnormalities (e.g.,
atypical activation of neural sites), and abnormalities of head
circumference. Genetic or environmental aetiologies which
may underlie pathophysiological abnormalities are outside
the scope of this review [40, 41].
lie the core symptoms of ASDs has advanced significantly
as a result of neuroimaging techniques [42, 43]. Magnetic
resonance imaging (MRI) affords the noninvasive in vivo
exploration of brain morphology  without any adverse
effects such as radiation exposure, a crucial feature, partic-
ularly when applied to young children . Research on
older children through to adulthood with ASD has indicated
numerous differences in the neural structures compared to
hemisphere regions, a substantial thinning of the cortex has
been observed in individuals with ASD  consistent with
earlier studies . Increased grey matter in the primary and
associative auditory and visual cortex  and reductions
in regions within the corpus callosum  are just some
of the findings of brain morphological differences in older
individuals with ASD. Subtle differences in both behaviour
and brain structure have been discovered within the first
12 months in infants who are later diagnosed with ASD.
What is not known is whether any of these subtle differences
can be used as an early biomarker to identify infants at-
risk of a later ASD diagnosis . Applying behavioural,
electrophysiological, and functional neuroimaging methods
during the first few years of life in individuals at risk of
ASD is essential . The functional brain characteristics of
ASD during the time when the behavioural symptoms first
arise, around 8–36 months, are largely unknown. Functional
magnetic resonance imaging (fMRI) studies have primarily
been limited to studies using normal IQ adolescents and
adults with ASD .
Despite being very much in its infancy, detailed exami-
nation of the postmortem brain from individuals with ASD
is an area of research which has substantially advanced our
understanding of the neurobiological underpinnings of this
disorder . Most brain tissues examined have been from
adults with ASD, and so our knowledge of the characteristics
of the brain in young subjects with ASD is minimal .
Internet-based bibliographic databases (PsycINFO and Web
of Knowledge) were searched to access studies which exam-
ined neurological differences in individuals with, or later
diagnosed with, ASD under the age of three years. Searches
were limited to references published from 2009 to the 21st
of November 2012 yielding 470 references. Search terms
used were “autis∗,” “infan∗,” “brain,” and “neuro∗.” Different
ordering of the search criteria entered into either database
did not result in any variations in the number of returned
abstracts. Duplicates were excluded prior to the retrieval of
references. Abstracts for each reference were obtained and
screened using the following criteria.
(1) human study population
(2) study must involve infants or toddlers under the age
(1) papers not published from January 2009 until 21st
(2) paper not published in English
(4) book reviews.
The process of eliminating nonrelevant papers can be seen
in the flowchart (following PRISMA guidelines) later (see
Figure 1 for flowchart) . We have reviewed studies which
contain a mixture of different diseases, albeit all presenting
with a somewhat similar autism phenotype.
Table 1 includes all the studies which investigated neuro-
logical differences in individuals with ASD and provides
summary detail regarding study characteristics and findings.
interest in the amygdala as the structure predominantly
underpinning ASD is not new. The function of the amygdala
social behaviour. In addition to the abnormal developmental
trajectory of the amygdala, there is a concomitant early
overgrowth in ASD. Numerous studies demonstrate amyg-
dala abnormalities in individuals with ASD with increased
studies have found no difference [100–102]. The association
between abnormalities of amygdala volume and attention to
eyes has been found in a study using a sample of older males
Table 1: Studies investigating neurological differences in individuals with ASD: study characteristics and findings.
Age of sample
Aim of the study
Schumann et al.
1–5 years (mean age 3
years). Each child
returned at approx. 5
years for final clinical
41 autism, 9 PDD-NOS,
and 39 TD controls.
Toddlers with ASD diagnosis (32 boys, 9 girls) had a larger right and left amygdala
compared with typically developing toddlers (28 boys, 11 girls).
Nordahl et al.
Time 1—mean age 37
months. Time 2—one
At time 1—132 boys (85
with ASD and 47 controls with TD). At time 2—70
boys (45 with ASD and 25
Amygdala volumes and
total cerebral volumes
Despite no difference in TCV growth (although the TCV was significantly enlarged at
both time points in the ASD group), at both time points, growth rate and amygdala
volume were greater in children with ASD, with enlargement found to be greater at
Mosconi et al.
18–35 months (2 years).
Followed-up at 42–59
months (4 years).
50 ASD and 33 controls (11
DD and 22 TD).
between specific ASDbehaviours (joint
attention) and amygdala
Bilateral enlargement of amygdala volume was found in children with ASD. There was
a disproportionate right amygdala volume enlargement compared to total tissue
volume. Amygdala enlargement was associated with increased JA at age four years.
Hazlett et al.
52 fragile x syndrome
(FXS); 63 autism, 19 DD,
and 31 TD.
Caudate nucleus volume
and amygdala volume.
Children with FXS and autism disorder had substantially enlarged caudate volume and
smaller amygdala volume. Children with ASD without FXS (i.e., idiopathic autism)
had only modest enlargement in their caudate nucleus volumes while enlargement of
their amygdala volumes were more pronounced.
Webb et al.  3-4 years
45 children with ASD, 14
children with DD, and 26children with TD.
structures and their
association with severity
of symptoms and
cognitive functioning in children with ASD.
Reduced total vermis volumes (vermis lobe VI-VII area) in the ASD children. No
correlation was found between cerebellar measurements and severity of ASD
symptoms nor verbal, nonverbal, or full scale IQ.
et al. 
7 ASD and 6 control male
Children with ASD had 67% more neurons in the PFC compared to controls,
including 79% more in dorsalateral-PFC and 29% more in medial-PFC.
Holmboe et al.
9 to 10 month old
31 siblings of children who
have been diagnosed with
ASD and 33 low-risk
functioning using a task
exploring attention and
A subset of sibs-ASD infants had difficulty disengaging attention from a centrally
presented stimulus in order to orient to a peripheral stimulus indicating atypical
frontal cortex functioning in the infant broader autism phenotype.
Santos et al.  4–14 years
Postmortem brains of 4
young patients with ASD
and 3 aged-matched
von Economo neurons
in the frontoinsular
postmortem brain tissue.
A significantly higher ratio of VENs to pyramidal neurons was found in the sample ofASD patients.
Eyler et al.  12–48 months
40 with ASD and 40 TD.
processing of language.
Deficient left hemisphere response to speech sounds and exhibited abnormally
right-lateralised temporal cortex response to language was found in at-risk toddlers
who later received a diagnosis of ASD. Difference becomes greater with age.
4 Behavioural Neurology
Table 1: Continued.
Age of sample
Aim of the study
Hazlett et al.
About two years of age.
MRI was carried out
again approximately 24
months later (when aged
4-5 years; 38 children
with ASD; 21 controls).
59 children with ASD and
38 control children.
Thirty-eight children with
ASD and 21 comparison
cases were examined at the
Early growth trajectories
in brain volume
(cerebral gray and white
matter) and cortical
Generalised cerebral cortical enlargement in individuals with ASD at both two and
four to five years (being 9% larger in ASD group). Despite no difference in cortical
thickness, children with ASD had enlargement in both grey and white matter volume
for all cortical lobes (temporal, frontal and parieto-occipital lobes). Disproportionate
enlargement in temporal lobe white matter only was found in the ASD group after
controlling for total brain volume.
Hoeft et al. 
FXS group—mean age
2.9 years. idiopathic
autism (iAUT)—meanage 2.77 years. Typical
2.55 years. Idiopathic
controls—mean age 2.96
52 males diagnosed with
FXS. 63 with Idiopathic
autism (iAUT). 31 TD. 19
idiopathic DD controls.
Greater volume was evident in iAUT compared with controls, who in turn had greater
volume than FXS. Frontal and temporal grey and white matter regions often
implicated in social cognition, including the medial prefrontal cortex, orbitofrontal
cortex, superior temporal region, temporal pole, amygdala, insula, and dorsalcingulum were abnormal in FXS and iAUT.
Schumann et al.
1.5 years–5 years of age.
Mean, 30 months plus or
minus 10 months).
41 toddlers who received a
confirmed diagnosis of
autism disorder at 48
months of age and 44 TD
Cerebral gray and white
Cerebral grey and white matter growth abnormalities in individuals with ASD. Within
cortex, the most significant differences in volume, and age-related change took place in
anterior regions of the brain (frontal grey, temporal grey, and cingulate grey cortices).
Posterior cerebral regions less affected. Abnormal growth most pronounced in
temporal grey matter volumes.
Rommelse et al.
129 children with ASD and
59 children with non-ASD
height, and weight.
Similar abnormal patterns of growth compared to population norms were found in
both groups. Abnormal HC growth may actually be common to psychiatric disorders,
rather than ASD specifically. However, the most apparent difference was that the
children with ASD showed an increased HC relative to height up to two months of age,
an increase not found in the PC group at this age.
Muratori et al.
Birth (TO), 1-2 months
(T1), 3–5 months (T2),
and 6–12 months (T3).
50 with ASD and 100 TD.
body height, and body
Weight was significantly less in ASD subjects compared to healthy infants from 1-2
months onwards. After controlling for weight and height, excessive rate of HC growth
from birth was found in the individuals with ASD.
Fukumoto et al.
280 children with ASD.
body height, and body
Increases in HC growth from 3–12 months, in height from 3–9 months, and in body
weight from 3 to 6 and 12 months were found in the males with ASD. Increases in HC,
body height and body weight were only observed at three months in the females with
ASD. Only HC in the male ASD group was significantly increased from 6–9 months
Chawarska et al.
Autism disorder (푛 = 64),
PDD-NOS (푛 = 34), global
(푛 = 13), and other
(푛 = 18), and TD boys
(푛 = 55).
growth in ASD, height,
and weight growth.
from birth to 24 months
and measures of
Boys with ASD were found to be significantly longer by 4.8 months, had greater HC by
age 9.5 months and weighed more by age 11.4 months, compared to the typically
developing boys. No other clinical groups displayed an overgrowth. Boys with ASD in
the top 10% of overall physical size in infancy displayed more severe social deficits and
lower adaptive functioning at 2 years.
Table 1: Continued.
Age of sample
Aim of the study
Nordahl et al.
Boys and girls with ASD
(푛 = 53, no regression
(nREG); 푛 = 61, regression
(REG)) and a control group
of age-matched typically
(푛 = 66).
Total brain volume
(rapid head growth).
Abnormal brain enlargement was most common in boys with regressive autism. Brain
size in boys without regression was similar to controls. Head circumference in boys
with regressive autism was normal at birth but deviated from normal growth
trajectories (other groups) around the age of 4–6 months. No brain size differences in
girls with autism (푛 = 22, ASD; 푛 = 24, controls).
12 months and 50 years
of age for the typical
group and 2–50 years for
the ASD group.
259 ASD subjects and 327
Brain size based on the
analyses of 586
Evidence of overgrowth throughout infancy and the toddlerhood in both boys and
girls with ASD which was subsequently followed by an accelerated rate of decline in
et al. 
3 time points (9, 24 and 36 months)
About 9,000 children.
No difference in HC at any of the 3 time points in the children with ASDs.
et al. 
18 weeks gestation and
also at birth
14 children with ASD were
matched with four control
participants (푛 = 56).
Head circumference was
ultrasonography at about
18 weeks gestation and
also at birth using a
Overall body size was
indexed by foetal
femur-length and birth
No difference in head circumference at either time-point between the groups.
et al. 
15 time points starting
from birth to 36 months
48 sibling pairs in which
one (푛 = 28) or both
(푛 = 20) sibs were affected
by an ASD and 85 control
male sibling pairs
Serial head orbitofrontal
Significant acceleration of head growth in individuals with ASD compared to controls.
The study also showed that infant HG trajectory may be endophenotypic but was not a
reliable indicator of risk of ASD among siblings of ASD in this study.
Gray et al.  Birth and 18.5 months.
Children with autism
(푛 = 86) and children with
DD without autism
(푛 = 40).
Head circumference at
birth and rate of change
in head circumference.
No differences between the group of children with both ASD and developmental delay
compared with the group with developmental delay alone. However, when compared
with normative data, children with ASD had significantly smaller HCs at birth and
significantly larger HC at 18.5 months of age.
Mraz et al.  0–25 months
24 children who
maintained their diagnoses,
15 children who lost their
diagnoses and 37 TD
length and weight
Compared to controls, HC and weight growth were significantly larger in both ASD
groups and there were no significant differences between ASD groups.
McCleery et al.
20 high-risk infants
(siblings of an older sibling
diagnosed with ASD) and
20 low-risk control
Cortical responses to
The low-risk group displayed faster responses to faces compared to object stimuli
(P400) which was not observed in the high-risk group. Conversely, faster responses to
objects than faces in high risk but not low-risk infants (N290). Right hemisphere
advantage (greater hemispheric asymmetry) in the typical that was not found in the
Table 1: Continued.
Age of sample
Aim of the study
Luyster et al.
32 infants at high-risk of
ASD and 24 low-risk
responses to social
No significant group differences in the neural response to faces. Trend for the low-risk
group to exhibit more marked differential response to familiar and unfamiliar faces (in
the anticipated direction) compared with high-risk infants.
Elsabbagh et al.
Mean age—10 months
Nineteen infant siblings of
children diagnosed with
ASD and 17 control infants
with no family history of
Neural correlates of
direct and averted gaze.
Prolonged latency of the occipital P400 event-related potentials component in
response to direct gaze was exhibited in the sib-ASD group compared to control infant.
No difference between the groups in the P400 latency for Averted gaze.
Elsabbagh et al.
6–10 month. About 18 to
30 months later, these
children were clinically
assessed for ASD.
Infants at high familial risk
for ASD (푁 = 54) and
infants at low risk (푛 = 50).
Neural sensitivity to eye
Characteristics of ERP components evoked in response to dynamic eye gaze shifts
during infancy were associated with ASD diagnosis at 36 months.
Key and Stone
months 15 days old
20 typical infants and 15
infant siblings of children
diagnosed with ASD.
Speed of processing of
novel versus familiar
faces using event related
potentials and eye
Both infant groups demonstrated the ability to differentiate between mothers and
strangers, as shown in the amplitude modulations of posterior N290/P400 and
frontal/central Nc responses. However there was a delayed ERP response to the
stranger face (as evidenced by the latency of the P400 response) in the typical infants
Key and Stone
Mean age of High-Risk
group was 9.01 (0.34).
35 infants (20 average-risk
typical infants, 15 high-risk
siblings of children with
To investigate whether
infants at high risk for
ASDs process facial
features (eyes, month)
differently. Also, whether
this is associated with
All infants detected eye and mouth changes. However, different brain mechanisms
were used. Facial feature changes were related to changes in activity of the face
perception mechanisms (N290) for the average-risk group only.
Testing at 2 years and
then again at 4 years.
(months) for ASD at
time 1 26.9 (6.2) and time 2 46.4 (6.4). For
control group mean age
at time 1 26.3 (6.5) and
time 2 46.3 (4.3).
44 children with ASD and
30 TD controls.
Toddlers with ASD looked increasingly away from faces with age and atypically
attended to key features within the face. They also demonstrated at both ages
impairment in the ability to recognise faces.
Bosl et al. 
46 high risk for ASD,
defined on the basis of
having an older sibling with
a confirmed diagnosis of
ASD and 33 controls.
computed on the basis of
resting state EEG data.
Multiscale entropy appears to go through a different developmental trajectory in
infants at high risk for ASD than it does in typically developing controls with
differences being most marked at ages 9–12 months.
Webb et al. 
24 children with ASD and
32 TD children.
Neural responses to
familiar and unfamiliar faces.
Delayed development in the individuals with ASD was indicated since neural
responses to faces in this group of children resembled those observed in younger
typically developing children.
Table 1: Continued.
Age of sample
Aim of the study
Dinstein et al.
72 toddlers in total. Broken
down in study: All toddlers
(12–46 months)—Autism(푛 = 29), Control (푛 = 30)
and language delay
(푛 = 13). Young toddlers
(12–24 months)—Autism(푛 = 12), Control (푛 = 16)
and Language Delay
(푛 = 11).
activity of naturally
sleeping toddlers with
autism. fMRI data.
Toddlers with autism exhibited significantly weaker interhemispheric synchronization
(i.e., weak “functional connectivity” across the two hemispheres) in putative language
Stahl et al.  10 month olds
10 infant at risk of ASD.
Aim was to discuss the
use of machine learning
methods and their
possible application to
the analysis of infant
Classification methods (regularised discriminant function analyses and support vector
machines) can increase the discriminative power of ERP measurements. Using
cross-validation, both methods successfully discriminated at above chance levels
between groups of infants at high and low risk of a later diagnosis of autism. However,
infants could only be discriminated in the direct gaze condition, not in the averted
Wolff et al. 6 to 24 months in
92 high-risk infant siblings
from an ongoing imaging
study of ASD.
white matter fiber tract
organisation from 6 to
24 months in high-risk
infants who developed
ASD by 24 months.
The fractional anisotropy trajectories for 12 of 15 fiber tracts were significantly different
between the infants who developed ASDs compared to those who did not.
Hazlett et al.
68 boys with idiopathic
autism (ASD). 18 to 42
months of age.
53 boys with fragile X
syndrome (FXS), 68 boys
with idiopathic autism
(ASD), and a comparison
group of 50 typically
developing and developmentally delayed
Total brain volumes and
regional (lobar) tissue
volumes were also
Children with idiopathic autism were found to have a generalised cortical lobe
Calderoni et al.
Female children with
ASD (ASDf)—2–7 years.
ASDf (푛 = 38) compared to
38 female age and non
verbal IQ matched controls.
Aim to investigate the
phenotype of female
children with ASD.
The between-group whole-brain and brain-segment volume comparison revealed a
total intracranial volume (TIV) enlargement of approximately 5% in female children
with ASD. The conventional VBM analysis showed evidence of an increased GM
volume in a specific region of the left superior frontal gyrus of ASDf. The
implementation of the SVM analysis on the GM segments obtained in the
VBM-DARTEL pre-processing highlighted a more complex circuitry of increased
cortical volume in ASDf, involving bilaterally the SFG and the right temporo-parietal
8 Behavioural Neurology
Table 1: Continued.
Age of sample
Aim of the study
Zeegers et al.
Between 2–7 years.
34 children with ASD and
13 developmentally delayed
children without ASD,
(matched on age and
To investigate volumes
of cranium, total brain,
cerebellum, grey and
white matter, ventricles,
No significant differences in volumes of intracranium, total brain, ventricles,
cerebellum, grey or white matter or amygdala and hippocampus between the ASD
group and the developmentally delayed group were found.
Hazlett et al.
6 month-old infants at
high risk for ASD
Infants at high risk for ASD
(푛 = 98) compared to
infants without family
members with ASD
(푛 = 36).
MRI study examining
brain volume and
No group differences.
Duffy and Als
Between 2–4 years
The 2- to 12-year-old
subsample consisted of 430
ASD- and 554 C-group
subjects (푛 = 984).
The current study
attempts to answer the as
yet open question of
between children with
ASD and neuro-typical
healthy controls. EEG
coherence data was
evaluated in a large
sample of children with
ASD and compared to a
A stable pattern of EEG spectral coherence was found to distinguish children withASD from neurotypical controls.
Number of references
Number of duplicates
removed through reading
titles of abstracts—12
Number of additional
through other sources—5
Number of full text
articles assessed for
Number of full text articles excluded
(14 sample too old. 1 was included for
introduction. 16 were not relevant. 2
were dissertations. 1 abstracts were
from conference abstracts. 4 were
Number of papers
Number of papers
Number of studies
included in the
Number of references
(not looking at neurological
abnormalities in the first
Figure 1: Flowchart showing the process for identifying the relevant studies for this systematic review.
. Postmortem studies have found quantifiable abnormal-
ities in the amygdala of individuals with ASD [103, 104].
Recent research has emphasised that abnormal develop-
mental trajectory has been relatively under researched in
the early years and the age at which abnormal amygdala
enlargement begins remains unclear. Schumann et al. 
scans from 89 toddlers at one to five years of age (mean,
nine girls), had a larger right and left amygdala compared
with typically developing toddlers (28 boys and 11 girls). In
size with severity of clinical impairment. Enlargement in
right amygdala volume in males and females and left amyg-
dala volume in females is disproportionate to total cerebral
volume at three years. Unlike ASD males, the enlargement
in ASD females was associated with severity of social and
Nordahl et al.  studied amygdala volumes and total
cerebral volumes at two time points in 132 boys (85 with
ASD and 47 control subjects with typical development (TD);
mean age, 37 months). A year later, longitudinal magnetic
ASD and 25 TD controls) and one year growth rates were
calculated. Despite no difference in total cerebral volume
growth (although the total cerebral volume was significantly
enlarged at both time points in the ASD group), at both time
points, growth rate and amygdala volume were greater in
children with ASD, with enlargement found to be greater
at time two. Difference in amygdala volume between the
two groups was about 6%, increasing to approximately 9%
at time two. Mosconi et al.  investigated associations
between specific autism behaviours (joint attention) and
amygdala volume. Fifty ASD and 33 control (11 developmen-
tally delayed, 22 typically developing) children between 18
and 35 months (two years) of age followed up at 42 to 59
volume was found in children with ASD. Left amygdala was
enlarged proportionately to increases in total tissue volume.
A 5% increase in total tissue volume was found in the
ASD group and amygdala volumes were enlarged by 16%
compared to the control group at the ages of two and four.
Between the groups, no differences in the growth trajectories
between two and four years of age were found. Interestingly,
amygdala enlargement was associated with increased joint
attention at the age of four, and while only the right amyg-
dala volume was increased relative to total tissue volume
enlargement, the strength of the relationship did not differ
Lastly, one study emphasised the importance of taking into
consideration heterogeneity in studies investigating ASD
. Children (between 18 and 42 months) with Fragile
x syndrome (FXS) and autism disorder had substantially
enlarged caudate volume and smaller amygdala volume.
Children with ASD without FXS (i.e., idiopathic autism) had
only modest enlargement in their caudate nucleus volumes
while enlargement of their amygdala volumes was more
3.2. Cerebellum Abnormalities in Individuals with ASD. Pre-
vious studies have observed reduction in cerebellar grey
matter volume in girls with ASD aged between two and six
years  and increased cerebellar white matter volume
and reduced vermis lobules VI-VII in two- and three-year-
old ASD children . Sparks et al.  found an increase
of 7% in the volume of the whole cerebellum in three- and
four-year-old ASD children.
Recent research has also found evidence of cerebellum
vermal structures and their association with severity of
symptoms and cognitive functioning in children with ASD
aged three to four years and found reduced total vermis
volumes (vermis lobe VI-VII area) in the ASD children.
Neither severity of ASD symptoms nor verbal, nonverbal, or
full scale IQ was found to be in correlation with cerebellar
measurement. To our knowledge, no studies have investi-
age of three within the last three years.
3.3. Frontal Cortex Abnormalities in Individuals with ASD.
is commonly found in the prefrontal cortex (PFC) [107–
110]. Carper et al.  found an anterior-to-posterior gra-
dient of overgrowth, with frontal lobes showing the greatest
overgrowth in two to four year olds with ASD. Despite
PFC abnormality being considered to underlie some ASD
symptoms, the cellular defects that produce the abnormal
overgrowth have yet to be discovered.
Studies within the last three years are consistent with ear-
lier findings demonstrating abnormalities within the frontal
cortex in individuals with or later diagnosed with ASD.
Courchesne et al.  examined postmortem prefrontal
tissue from seven children with autism and six control male
children aged 2 to 16 years and found that children with ASD
had 67% more neurons in the PFC compared to controls,
including 79% more in dorsalateral-PFC and 29% more in
medial-PFC. Brain weight in the ASD cases differed from
normative mean weight for age by a mean of 17.6%, while
brains in controls differed by a mean of 0.2%.
Both attention and inhibition, previously shown to be
associated with frontal cortex activation, were explored in
nine to ten month old siblings of children who have been
diagnosed with ASD and low-risk control infants . Par-
ticipants took part in the Freeze-Frame task where infants
are encouraged to inhibit looks to peripherally presented
distractors whilst looking at a central animation. A subset
of sibs-ASD infants had difficulty disengaging attention
from a centrally presented stimulus in order to orient to a
peripheral stimulus. Lastly, Santos et al. (2011)  examined
von Economo neurons (VENs) in the frontoinsular cortex
(FI), a region which has been put forward as the area
which is involved with the integration of internal sensations
of bodily arousal, emotional regulation, and goal-directed
behaviours. Using a stereological method, Santos et al. (2011)
 quantified VENs and pyramidal neurons in layer V of FI
in postmortem brains of four young patients (aged between
4 and 14 years) with ASD and three age-matched controls
and found a significantly higher ratio of VENs to pyramidal
neurons in the patients with ASD.
3.4. Temporal Cortex Abnormalities in Individuals with ASD.
Earlier studies have suggested that failure to develop normal
language comprehension is one of the most common early
but the neural mechanisms underlying this signature deficit
studies have investigated this using fMRI performed during
natural sleep to investigate the brain regions which underlie
speech perception . Decreased functional activity in
and mental age-matched groups (푛 = 11) was found.
temporal cortices in a small sample (푛 = 12) of two to three
A recent study also found lateralised abnormalities of
year olds with ASD compared with chronological (푛 = 12)
toddlers (푛 = 40), aged between 12 and 48 months, was
speech sounds and exhibited abnormally right-lateralised
temporal cortex response to language was found in at-risk
pronounced in the individuals with ASD when they were
three and four years of age. Failed development of language
comprehension, known to be one of the earliest markers in
ASD, may therefore be the result of very early defects in the
superior temporal gyrus which may persist throughout the
in toddlers with ASD (푛 = 40) and typically developing
natural sleep . Deficient left hemisphere response to
measured during the presentation of a bedtime story during
mal growth was most pronounced in temporal grey matter
Behavioural Neurology 11
under the age of two  and over [95, 106]. Therefore,
abnormal early development of grey matter is linked with
ASD (i.e., ) in children between two and four years
old. Numerous conditions of atypical development can lead
to autism, in particular fragile X syndrome (FXS), which
is considered to be the most commonly known single-gene
cause of autism. Many individuals with FXS also exhibit
behaviours common to individuals with ASD.
In a recent study, whole-brain morphometric patterns
mean age, 2.77 years) as well as typically developing (푛 = 31;
may be associated with distinct neuroanatomical patterns,
emphasising the neurobiological heterogeneity of iAUT.
Brain enlargement has been observed in children with
ASD as young as two years of age. Hazlett et al.  looked
at early growth trajectories in brain volume (cerebral grey
and white matter) and cortical thickness. At about two
years of age, 59 children with ASD and 38 control children
were examined using magnetic resonance imaging (MRI).
MRI was carried out again approximately 24 months later
(when aged 4-5 years; 38 children with ASD; 21 controls).
9% larger in ASD group). There was no difference in the rate
of increase of cerebral cortical growth during this interval
between the groups, suggesting that brain enlargement in
ASD results from an increased rate of brain growth prior
to the age of two years. No cerebellar differences were
observed in children with ASD. Despite no difference in
cortical thickness, children with ASD had enlargement in
both grey and white matter volumes for all cortical lobes
(temporal, frontal, and parieto-occipital lobes). However,
disproportionate enlargement in temporal lobe white matter
was only found in the ASD group after controlling for total
Schumann et al.  found both cerebral grey and
white matter growth abnormalities in individuals with ASD
at two and a half years of age. Within cortex, the most
place in anterior regions of the brain (frontal, temporal,
and cingulate cortices). Posterior cerebral regions, on the
other hand, were less affected with respect to volume and
growth trajectory. Abnormal growth was most pronounced
in temporal grey matter volumes. Schumann et al.  also
observed significant gender differences in the longitudinal
growth trajectories in numerous brain regions. Compared to
controls, in males with ASD, frontal, and temporal lobe grey
matter grew at a nonlinear rate. Compared to controls, in
and severe with abnormal growth trajectories observed in
the total cerebrum, cerebral white, cerebral grey, frontal,
and temporal regions. In females with ASD only, there was
were examined in young males diagnosed with FXS (푛 = 52;
mean age, 2.90 years) or idiopathic autism (iAUT) (푛 = 63,
controls (푛 = 19; mean age, 2.96 years) . Overall, greater
exhibit different “neuroanatomical profiles,” with pathology
being more pronounced in females . It has previously
been suggested that, compared to females who are later
diagnosed with ASD, overgrowth may start earlier in males
Wolff et al.  prospectively examined white matter
fiber tract organisation from six to 24 months in high-risk
infants. At 24 months, 28 of the 92 infants met criteria for
ASDs. Microstructural properties of white matter fiber tracts
(considered to be related with ASDs) were characterised by
fractional anisotropy and radial and axial diffusivity. The
fractional anisotropy trajectories for 12 of 15 fiber tracts were
significantly different between the infants who developed
ASDs compared to those who did not. In the infants with
ASDs, development for the majority of fiber tracts was
characterised by higher fractional anisotropy values at six
months followed by slower change over time compared to
infants without ASDs .
One study investigated structural brain volumes using
magnetic resonance imaging across two time points (at
two to three and again at four to five years of age). Total
brain volumes and regional (lobar) tissue volumes were also
examined. The study included 53 boys 18 to 42 months of
age with fragile X syndrome (FXS), 68 boys with idiopathic
autism (ASD), and a comparison group of 50 typically
developing and developmentally delayed controls. Children
with FXS had larger global brain volumes compared with
controls but were not different than children with idiopathic
autism, and the rate of brain growth from two to five years
of age was similar to that observed in controls. Children with
idiopathic autism were found to have a generalised cortical
lobe enlargement, while children with FXS showed specific
enlargement in the temporal lobe white matter, cerebellar
grey matter, and caudate nucleus but a significantly smaller
Recognising the neglect of research investigating the
neuroanatomical phenotype of female children with ASD
(ASDf), Calderoni, Retico, Biagi, Tancredi, Muratori, and
Tosetti  investigated the anatomic brain structures of a
sampleentirelycomposedofASDf(푛 = 38;twotosevenyears
each group were compared using voxel-based morphometry
(VBM) with diffeomorphic anatomical registration through
exponentiated lie algebra (DARTEL) procedure, allowing the
group whole-brain and brain-segment volume comparison
revealed a total intracranial volume (TIV) enlargement of
approximately 5% in female children with ASD with respect
VBM analysis showed evidence of an increased GM volume
in a specific region of the left superior frontal gyrus of
ASDf. Third, the implementation of the SVM analysis on the
GM segments obtained in the VBM-DARTEL preprocessing
highlighted a more complex circuitry of increased cortical
volume in ASDf, involving bilaterally the SFG and the right
temporoparietal junction (TPJ), compared to controls .
and nonverbal IQ matched controls. Whole brain volumes of
ities across brain structure in individuals with ASD in the
and seven years of age (matched on age and developmental
level), participated in an MRI study to investigate volumes
of cranium, total brain, cerebellum, grey and white matter,
ventricles, hippocampus, and amygdale . No significant
group were found. The important suggestion arising from
these findings is that higher intellectual functioning was not
found to be associated with a relatively larger brain volume
in children with ASD, therefore relative brain enlargement
may not be beneficial to individuals with ASD . This
merits further research in this area. Also, an MRI study
examining head circumference, brain volume and radiologic
abnormalities in a group of six-month-old infants at high
risk for autism (푛 = 98) compared to infants without family
members with autism (푛 = 36) found no significant group
3.6. Relationship between Increased Head Circumference (HC)
and Somatic Growth in ASD. Accelerated brain growth is a
well known and intriguingbiological feature in children with
total population level, macrocephaly is uncommon in ASD
. Evidence of accelerated head circumference (HC) or
macrocephaly and body growth during infancy in children
with ASDs is well supported in the literature, although vari-
ation in the timing of acceleration across studies exists [106,
109, 119]. Such accelerated growthhas even been suggested as
of life [120, 121]. Research investigating whether abnormally
large HC during the early years can be a reliable indicator
of ASD is supported by findings that HC during the early
years more accurately reflects brain volume than that during
adolescence and a crucial factor for the analysis of ASD
onset is the timing of the increase in HC in infancy and
toddlerhood [120, 122, 123]. Emergence of brain organisation
and connectivity differences in high risk infants occur at the
same time as observations of accelerated head growth have
been found in children with ASD . Accelerated head
in general body growth [123, 125, 126]. HC trajectories were
still accelerated in children with ASD even after correcting
for body length and height measurements [120, 123]. Despite
young children with ASD, research carried out over the last
three years have produced mixed results.
More recent studies have investigated HC within the first
few years of life. Rommelse et al.  measured HC, height
and weight throughout the first 19 months of life in 129
children with ASD and 59 children with non-ASD psychi-
atric disorders. Fifty-nine children (46 male and 13 female)
with non-ASD psychiatric disorders (Psychiatric controls,
PC) also participated: 39 had a psychiatric disorder other
than ASD (such as ADHD, oppositional defiant disorder,
communication disorder), 12 had a diagnosis according to
the DC: 0-3R (2005; such as regulation problems), and eight
had mental retardation without any psychiatric comorbidity.
and HC in relation to height. Abnormal HC growth may
specifically, questioning the use of HC growth as a marker
for ASD. However, the most apparent difference was that the
children with ASD only showed an increased HC relative to
height up to two months of age, an increase not found in the
PC group at this age.
Muratori et al.  used anthropometric measurements
(HC, body height, and body weight) obtained at birth (T0),
1-2 months (T1), 3–5 months (T2) and 6–12 months (T3) to
investigate HC development during the first year. At T2 and
T3, HC was significantly larger in the ASD group (푛 = 50)
rate of HC growth from birth was found in the individuals
with ASD consistent with an earlier study by Fukumoto et
al.  which compared 280 children with ASD. Increases
in HC growth from 3 to 12 months, in height from 3 to
9 months and in body weight from three-six months and
12 months were found in the males with ASD. Increases in
HC, body height, and body weight were only observed at
three months in the females with ASD. Only the HC in the
months after birth, reaching a peak at six months after birth
after correcting for height, age and weight. Chawarska et al.
 examined whether HC growth in ASD is independent of
height and weight growth during infancy and also whether
there is any association between HC growth from birth to
24 months and measures of cognitive functioning (social,
(푛 = 34), global developmental delay (푛 = 13), and other
significantly longer by 4.8 months, had greater HC by age
9.5 months and weighed more by age 11.4 months, compared
to the typically developing boys. No other clinical groups
10% of overall physical size in infancy exhibited more severe
social deficits and lower adaptive functioning at two years.
Rapid head growth has been suggested as a potential risk
factor for regressive autism . Using a large sample of
group of age-matched typically developing controls (푛 = 66).
ment was most commonly found in boys with regressive
autism whereas brain size in boys without regression were
similar to controls. HC in boys with regressive autism was
compared to the typically developing group (푛 = 100).
of age. Boys diagnosed as having autism disorder (푛 = 64),
pervasive developmental disorder (not otherwise specified)
developmental problems (푛 = 18) and typically developing
= 55) were compared. Boys with ASD were
two to four year old boys and girls with ASD (푛 = 53, no
Retrospective measurements of HC from birth through to 18
months of age were reviewed and abnormal brain enlarge-
regression (nREG); 푛 = 61, regression (REG)) and a control
brain size differences in girls with autism (푛 = 22, ASD;
ASD. For boys with regressive autism, divergence in brain
size occurs well before loss of skills are typically observed.
Investigating age-specific anatomical abnormalities in indi-
viduals with ASD, Courchesne et al.  measured age-
related changes in brain size in ASD and control participants
of 586 longitudinal and cross-sectional MRI scans. Findings
revealed evidence of overgrowth throughout infancy and
toddlerhood in both boys and girls with ASD which was
While the studies discussed so far indicate abnormalities
no evidence of such differences. In a nationally representa-
tive, community-based sample of children with and without
ASDs, derived from the Early Childhood Longitudinal Study
Birth Cohort, Barnard-Brak et al.  followed about 9,000
the HC growthtrajectoryover this time. No difference in HC
found. Whitehouse et al.  were the first to examine foetal
(other groups) around the age of four to six months. No
푛 = 24, controls) were found. Nordahl et al.  argue that
distinct neural phenotypes are linked with different onsets of
HC growth prospectively in children with ASD (푛 = 14) who
growth. HC was measured using ultrasonography at about 18
Overall body size was indexed by foetal femur-length and
birth length. This study found no difference in HC at either
time-point between the groups.
A retrospective study obtained serial head orbitofrontal
circumference measurements taken from 48 sibling pairs in
were each matched with four control participants (푛 = 56)
on a variety of factors which can have an effect on foetal
which one (푛 = 28) or both (푛 = 20) siblings were affected
significant acceleration of head growth in individuals with
HG trajectory may be endophenotypic but was not a reliable
indicator of risk of ASD among siblings of ASD in this study.
Gray et al.  measured HC at birth and rate of change in
by an ASD and 85 control male sibling pairs over 15 time
points starting from birth to 36 months . There was a
HC in young children with autism (푛 = 86) and children
with developmental delay alone. However, compared with
normative data, children with ASD had significantly smaller
HCs at birth and significantly larger HC at 18.5 months of
age with no difference in the HCs of children with ASD and
developmental delay and children with developmental delay
only indicating that HC measurement has limited reliability
in terms of its use as an early indicator for ASD. Lastly, in
the first study to examine head growth in children who later
lose their diagnoses of ASD, Mraz et al.  measured HC,
length, and weight growth during infancy for 24 children
who maintained their diagnoses, 15 children who lost their
diagnoses, and 37 typically developing controls. Compared
to controls, HC and weight growth were significantly larger
with developmental delay without autism (푛 = 40) and
found no differences between the group of children with
in both ASD groups (birth to 25 months) and there were no
significant differences between ASD groups.
with ASD. By around one year, infants at high risk for
ASD display behavioural deficits in social development at
subtle brain function signatures (atypical neural electro-
physiological responses) in the first few years of life may
provide an early indicator to later development of complex
which primarily has been limited to older children, suggests
that early detection of abnormalities in electroencephalog-
raphy (EEG) signals may be used as an early biomarker for
developmental cognitive disorders . Atypicalities in face
and object processing in children and adults with ASDs have
previously been shown in three to four year olds with ASD
 and adults with ASD . Since indicators of brain
function may serve as potentially sensitive predictors of ASD
and atypical eye contact are characteristic of this syndrome
, studies have previously investigated whether neural
sensitivity to eye gaze during infancy is associated with later
autism outcomes [133, 134] and atypical eye gaze processing
in children and adults with ASD have been shown.
A more recent study investigated whether such atypi-
calities reflect an early genetically mediated risk factor 
by measuring cortical responses to face/object processing in
ten month old high-risk infants (siblings of an older sibling
diagnosed with ASD) using event-related potentials (ERPs).
Latencies and amplitudes of four ERP components (P100,
N290, P400, and Nc) were compared between 20 high-risk
infants and 20 low-risk control subjects. The low-risk group
(P400) which was not observed in the high-risk group.
Conversely, faster responses to objects rather than faces
in high-risk but not low-risk infants (N290) were shown.
Responses to objects were also faster in high-risk compared
to low-risk infants(both N290 and P400). Overallthere were
significantly less hemispheric asymmetries exhibited in the
high-risk compared to the low-risk group.
Luyster et al.  investigated whether high-risk infants
might also exhibit atypical neural responses to social stimuli.
with the allocation of attention, were studied. Thirty-two 12-
infants were presented with familiar and unfamiliar faces.
There were no significant group differences in the neural
response to faces. A more negative Nc to unfamiliar faces
than to familiar ones across both groups were displayed, thus
indicating that infants recruited more attentional resources
when presented with an unfamiliar face compared with a
unfamiliar stimuli in high-risk infants is consistent with the
findings reported earlier by McCleery et al. .
In light of previous findings demonstrating atypical eye
gaze processing in children and adults with ASD, Elsabbagh
et al.  recently examined the neural correlates of direct
and averted gaze in infant siblings of children diagnosed
with ASD (sib-ASD) were compared with 17 control infants
with no family history of ASD (mean, ten months) on their
response to direct versus averted gaze in static stimuli. Pro-
longed latency of the occipital P400 event-related potentials
component in response to direct gaze was exhibited in the
sib-ASD group compared to control infant. However, there
was no difference between the groups in the P400 latency for
tional modulation in infants . While the control group
showed no difference in latency values between Direct and
Averted gaze, the sib-ASD group had a tendency to respond
faster to the Averted relative to the Direct gaze condition.
Neural sensitivity to eye gaze in infancy may therefore serve
as an early predictor of ASD later in toddlerhood. Elsabbagh
et al.  examined whether neural sensitivity to eye gaze
during infancy is associated with later diagnosis of ASD and
outcomes. Infants at high familial risk for ASD (푛 = 54) and
responses (ERPs) while six to ten month old infants viewed
faces with dynamic eye gaze directed either towards them or
away from them. Characteristics of ERP components evoked
in response to dynamic eye gaze shifts during infancy were
associated with ASD diagnosis at 36 months. Despite the
rarity of observing behavioural symptoms or signs of ASD
in the first year, atypical brain function during this first year
distinguished the group of infants who were later diagnosed
with ASD .
In another study the usefulness of two methods, regu-
larised discriminant function analyses and support vector
machines, were shown by reanalysing an ERP dataset of
infants from a study discussed earlier in this section .
Stahl, Pickles, Elsabbagh, Johnson, and The BASIS Team 
found supportive evidence that these classification methods
Using cross-validation, both methods successfully discrimi-
and low risk of a later diagnosis of autism. However, infants
could only be discriminated in the direct gaze condition, not
in the averted gaze condition .
with ASD (sibs-ASD) process familiar and novel faces dif-
ferently from typical infants . ERPs were recorded in 35
infants, approximately nine months 15 days old (20 typical
infants, 15 sibs-ASD) using an oddball paradigm presenting
photographs of infants’ mothers and an unfamiliar female.
No differences were revealed in the distribution, number,
or duration of fixations between the groups. Both groups
by the latency of the P400 response) in the typical infants
only. Another eye tracking study in two to four year old
toddlers with ASD found atypical face scanning to become
more pronounced with age . Toddlers with ASD looked
increasingly away from faces with age (from testing at two
years and again at four years) and atypically attended to key
features within the face and demonstrated impairedability to
recognise faces at both ages.
a comparison group of infants at low risk (푛 = 50) took
part in a study which recorded electrophysiological brain
Key and Stone  examined whether, on average, nine
month old infants, compared to infants at high risk for
ASD, process facial features (eyes, mouth) differently and
whether such differences were related to the infants’ social
and communicative skills. Eye tracking and visual event-
related potentials (ERPs) were recorded in 35 infants (20
average-risk typical infants, 15 high-risk siblings of children
with ASD) while they viewed photographs of a smiling
unfamiliar female face. On 30% of the trials, the eyes or
the mouth of that face was replaced with corresponding
features from a different female. No group differences in the
number, duration, or distribution of fixations were evident
and all infants looked at the eyes and mouth regions equally.
Findings from ERP analysis showed that all infants detected
eye and mouth changes but did so using different brain
mechanisms. Facial feature changes were related to changes
in activity of the face perception mechanisms (N290) for the
average-risk group only. For all infants, correlations between
ERP and eye-tracking measures indicated that larger and
faster ERPs to feature changes were associated with fewer
fixations on the irrelevant regions of stimuli. Size and latency
of the ERP responses correlated with parental reports of
receptive and expressive communication skills.
Bosl et al.  adopted modified multiscale entropy
(mMSE) computed on the basis of resting state EEG data,
to determine whether typically developing children can be
distinguished from a group of infants at high risk for ASD
(older sibling with ASD). To the author’s knowledge, this is
the first study to look into connectivity changes across time
differences in resting brain state entropy, possibly indicating
a biomarker for risk for a complex neurodevelopmental
disorder. Classification was computed separately within each
age group from six to 24 months. Data was collected from
a total of 143 sessions and from 79 individuals. Multiscale
entropy appears to go through a different developmental
trajectory in infants at high risk for ASD than it does in
typically developing controls with differences being most
marked at ages nine to 12 months. Lastly, Webb et al. 
in twenty-four children with ASD (18 to 47 months old)
compared with responses of thirty-two typically developing
children (12 to 30 months old). Delayed development in the
faces in this group of children resembled those observed in
younger typically developing children. Interestingly, electro-
report of adaptive social behaviours for children with ASD
and typically developing children.
Lastly, in a large case control study, a stable pattern of
EEG spectral coherence was found to distinguish children
with ASD from neurotypical controls in the subgroup aged
between two and four years .
3.8. Interhemispheric Synchronisation in Individuals with
ASD. Connectivity studies during the very early years in
ASD are few in number. Using fMRI data, Dinstein et al.
 found disrupted synchronisation in the spontaneous
Behavioural Neurology 15
cortical activity of 29 naturally sleeping toddlers with ASD
(1–3.5 years old) which was not evident in the toddlers with
language delay or the typical development group. In toddlers
with ASD, significantly weaker interhemispheric synchro-
nisation (weak “functional connectivity” across the two
and superior temporal gyrus (STG), two areas commonly
associated with language production, and comprehension.
There was also a significant inverse relationship between
ity. Strength of interhemispheric synchronisation was posi-
tively correlated with verbal ability. Investigation of neural
synchronisation may be useful as a diagnostic measure to aid
growing efforts of identifying ASD during infancy .
4. Future Directions
Further research delineating the neurological mechanisms
underlying ASD is of clinical importance . Within the
in research focused on understanding the biological mech-
anisms underlying ASD; however, many fundamental issues
remain. For instance, the causes of ASD, the specific brain
regions most impacted by ASD, and why are there more
males with ASD and what are the underlying mechanisms
involved that produce neurological gender differences .
Given that behavioural markers of ASD within the first
year of life are subtle in nature, it may be that neurological
methods may prove to be more sensitive at this early stage
in identifying and quantifying risk. Research investigating
whether a combination of risk markers early in infancy is
more effective than individual markers of risk in predicting
diagnostic outcomes for ASD is necessary .
to brain volume, further research is needed to explain
what is involved in producing the unusual amygdala growth
trajectory as well as other areas which have been found to
be enlarged in individuals with ASD. What we do know
is the importance of taking into account both the age
and gender of the individual when interpreting findings in
volumetric studies of ASD . Behavioural correlates of
different amygdala growth trajectories is another potentially
children with ASD who exhibit accelerated amygdala growth
might show higher anxiety levels .
Further research investigating the association between
HC growth rates and ASD is necessary since the majority
of research so far has been limited by small sample sizes
and by an absence of necessary group comparisons, such
as developmentally delayed children. Characteristics of the
subgroup of children who exhibit accelerated head growth
a longitudinal approach . Future studies are required
to examine whether impaired interhemispheric synchro-
nisation in putative language areas plays a causal role in
generating autism behavioural symptoms .
Despite the advances in our knowledge of neurological
challenges still remain, for instance, the heterogeneity of
symptoms, symptom severity, differences in IQ, total brain
volume, and psychiatric comorbidity . Lastly, research
into the plasticity in autism has yet to be carried out but it
altering the course of brain development in individuals with
With growing interest in identifying earlier methods for
detecting ASD, these studies are paving the way towards the
development of noninvasive, brain-based screening methods
that could potentially detect differences prior to behavioural
emergence  which would constitute an important sci-
entific breakthrough . Cross-disciplinary advances have
contributed to a “more optimistic outcome” for individuals
with ASD  and the development of new methods for
early detection and more effective treatments . Since we
are stillnotaware of theprotectivefactors, ethicalissues con-
cerning the implementation and clinical recommendations
. The importance of cross disciplinary research, in
particular combining findings from the behavioural and
neurological fields, is emphasised by Happ´ e et al.  when
single entity of autism may also mean abandoning the search
for a single “cure” or intervention.”
6. Conflict of Interests
The authors declare that there is no conflict of interests
regarding the publication of this paper.
 American Psychiatric Association, Diagnostic and Statistical
Association, Washington, DC, USA, 4th edition, 2000.
 E. M. Morrow, S.-Y. Yoo, S. W. Flavell et al., “Identifying autism
loci and genes by tracing recent shared ancestry,” Science, vol.
321, no. 5886, pp. 218–223, 2008.
 G. Baird, E. Simonoff, A. Pickles et al., “Prevalence of disorders
of the autism spectrum in a population cohort of children in
South Thames: the Special Needs and Autism Project (SNAP),”
The Lancet, vol. 368, no. 9531, pp. 210–215, 2006.
 T. Brugha, S. A. Cooper, S. McManus et al., “Autism spectrum
conditions in adults: data quality and methodology document,”
NHS, The Information Centre for Health and Social Care, 2012.
of the Self, Free Press, New York, NY, USA, 1967.
 L. Zwaigenbaum, A. Thurm, W. Stone et al., “Studying the
emergence of autism spectrum disorders in high-risk infants:
methodological and practical issues,” Journal of Autism and
Developmental Disorders, vol. 37, no. 3, pp. 466–480, 2007.
 M. Elsabbagh, E. Mercure, K. Hudry et al., “Infant neural
autism,” Current Biology, vol. 22, no. 4, pp. 338–342, 2012.
 K. Pierce, S. J. Glatt, G. S. Liptak, and L. L. McIntyre, “The
power and promise of identifying autism early: insights from
Psychiatry, vol. 21, no. 3, pp. 132–147, 2009.
 G. J. Mizejewski, “Biomarker testing for suspected autism
spectrum disorder in early childhood: is such testing now
feasible?” Biomarkers in Medicine, vol. 6, no. 4, pp. 503–506,
the prevention of autism spectrum disorder,” Development and
Psychopathology, vol. 20, no. 3, pp. 775–803, 2008.
 C. Gillberg and M. Coleman, The Biology of the Autistic
Syndromes, Clinics in Developmental Medicine, Mac Keith
Press, London, UK, 3rd edition, 2000.
 J. L. Matson, R. D. Rieske, and K. Tureck, “Additional consider-
ations for the early detection and diagnosis of autism: review of
available instruments,” Research in Autism Spectrum Disorders,
vol. 5, no. 4, pp. 1319–1326, 2011.
 J. L. Adrien, M. Faure, A. Perrot et al., “Autism and family
home movies: preliminary findings,” Journal of Autism and
Developmental Disorders, vol. 21, no. 1, pp. 43–49, 1991.
 P. Teitelbaum, O. Teitelbaum, J. Nye, J. Fryman, and R. G.
Maurer, “Movement analysis in infancy may be useful for early
diagnosis of autism,” Proceedings of the National Academy of
Sciences of the United States of America, vol. 95, no. 23, pp.
 C. Saint-Georges, R. S. Cassel, D. Cohen et al., “What studies
of family home movies can teach us about autistic infants: a
literature review,” Research in Autism Spectrum Disorders, vol.
4, no. 3, pp. 355–366, 2010.
in the detection of autism in infancy in a large population,”
British Journal of Psychiatry, vol. 168, pp. 158–163, 1996.
 M. Dereu, P. Warreyn, R. Raymaekers et al., “Screening for
autism spectrum disorders in flemish day-care centres with the
checklist for early signs of developmental disorders,” Journal of
Autism and Developmental Disorders, vol. 40, no. 10, pp. 1247–
 G. Nygren, M. Cederlund, E. Sandberg et al., “The prevalence
of autism spectrum disorders in toddlers: a population study of
2-year-old Swedish children,” Journal of Autism and Develop-
mental Disorders, vol. 42, no. 7, pp. 1491–1497, 2012.
 S. Ozonoff, A.-M. Iosif, F. Baguio et al., “A prospective study of
American Academy of Child and Adolescent Psychiatry, vol. 49,
no. 3, pp. 256.e1-2–266.e1-2, 2010.
 A. Rozga, T. Hutman, G. S. Young et al., “Behavioral profiles
of affected and unaffected siblings of children with autism:
verbal communication,” Journal of Autism and Developmental
Disorders, vol. 41, no. 3, pp. 287–301, 2011.
 T. Hutman, M. K. Chela, K. Gillespie-Lynch, and M. Sigman,
“Selective visual attention at twelve months: Signs of autism in
early social interactions,” Journal of Autism and Developmental
Disorders, vol. 42, no. 4, pp. 487–498, 2012.
 P. Teitelbaum, R. G. Maurer, J. Fryman, O. B. Teitelbaum, J.
Vilensky, and M. P. Creedon, “Dimensions of disintegration
in the stereotyped locomotion characteristic of parkinsonism
and autism,” in Stereotyped Movements: Brain and Behavior
Relationships, R. L. Sprague and K. M. Newell, Eds., pp. 167–
 L. Wang, M. T. Angley, J. P. Gerber, and M. J. Sorich, “A review
Biomarkers, vol. 16, no. 7, pp. 537–552, 2011.
 S. V. Gondalia, E. A. Palombo, S. R. Knowles, and D. W. Austin,
“Gastrointestinal microbiology in autistic spectrum disorder: a
review,” Reviews in Medical Microbiology, vol. 21, no. 3, pp. 44–
 P. Goines and J. van de Water, “The immune system’s role in the
biology of autism,” Current Opinion in Neurology, vol. 23, no. 2,
pp. 111–117, 2010.
 M. Careaga, J. Van de Water, and P. Ashwood, “Immune
dysfunction in Autism spectrum disorders,” in Immunotoxicity,
Immune Dysfunction, and Chronic Disease. Molecular and
Integrative Toxicology. Part 3, R. R. Dietert and R. W. Luebke,
Eds., pp. 253–269, Humana Press, 2012.
 D. A. Geier, T. Audhya, J. K. Kern, and M. R. Geier, “Blood
level?” Acta Neurobiologiae Experimentalis, vol. 70, no. 2, pp.
 S. T. Schultz, “Does thimerosal or other mercury exposure
Neurobiologiae Experimentalis, vol. 70, no. 2, pp. 187–195, 2010.
 A. Albizzati, L. Mor` e, D. Di Candia, M. Saccani, and C. Lenti,
“Normal concentrations of heavy metals in autistic spectrum
disorders,” Minerva Pediatrica, vol. 64, no. 1, pp. 27–31, 2012.
 C. A. Pardo and C. G. Eberhart, “The neurobiology of autism,”
Brain Pathology, vol. 17, no. 4, pp. 434–447, 2007.
 E. C. Azmitia, J. S. Singh, X. P. Hou, and J. Wegiel, “Dystrophic
serotonin axons in postmortem brains from young autism
patients,” Anatomical Record, vol. 294, no. 10, pp. 1653–1662,
of autism: trimethylated H3K4 landscapes in prefrontal neu-
rons,” Archives of General Psychiatry, vol. 69, no. 3, pp. 314–324,
 N. A. Meguid, A. A. Dardir, E. R. Abdel-Raouf, and A. Hashish,
“Evaluation of oxidative stress in autism: defective antioxidant
enzymes and increased lipid peroxidation,” Biological Trace
Element Research, vol. 143, no. 1, pp. 58–65, 2011.
Biological Trace Element Research, vol. 147, no. 1–3, pp. 25–27,
ers, vol. 16, no. 3, pp. 252–260, 2011.
the life span in autism: age-specific changes in anatomical
pathology,” Brain Research, vol. 1380, pp. 138–145, 2011.
 K. A. Pelphrey, S. Shultz, C. M. Hudac, and B. C. Vander Wyk,
“Research review: constraining heterogeneity: the social brain
and its development in autism spectrum disorder,” Journal of
Child Psychology and Psychiatry and Allied Disciplines, vol. 52,
no. 6, pp. 631–644, 2011.
 J. C. McPartland, M. Coffman, and K. A. Pelphrey, “Recent
disorder,” Current Opinion in Pediatrics, vol. 23, no. 6, pp. 628–
Behavioural Neurology 17
 G. Dawson, “Recent advances in research on early detection,
causes, biology, and treatment of autism spectrum disorders,”
Current Opinion in Neurology, vol. 23, no. 2, pp. 95–96, 2010.
 M. Coleman and C. Gillberg, The Autisms, Oxford University
 L. Mazzone and P. Curatolo, “Conceptual and methodological
challenges for neuroimaging studies of autistic spectrum dis-
orders,” Behavioral and Brain Functions, vol. 6, no. 1, pp. 6–17,
 K. A. Stigler, B. C. McDonald, A. Anand, A. J. Saykin, and C.
J. McDougle, “Structural and functional magnetic resonance
imaging of autism spectrum disorders,” Brain Research, vol.
1380, pp. 146–161, 2011.
spectrum disorder,” Pediatric Research, vol. 69, no. 5, pp. 63–68,
 S. Eliez and A. L. Reiss, “Annotation: MRI neuroimaging of
childhood psychiatric disorders: a selective review,” Journal of
Child Psychology and Psychiatry and Allied Disciplines, vol. 41,
no. 6, pp. 679–694, 2000.
 G. L. Wallace, N. Dankner, L. Kenworthy, J. N. Giedd, and A.
Martin, “Age-related temporal and parietal cortical thinning in
autism spectrum disorders,” Brain, vol. 133, no. 12, pp. 3745–
 N. Hadjikhani, R. M. Joseph, J. Snyder, and H. Tager-Flusberg,
cognition network inautism,” CerebralCortex,vol.16,no.9,pp.
 K. L. Hyde, F. Samson, A. C. Evans, and L. Mottron, “Neu-
and other core features of autism revealed by cortical thickness
analysis and voxel-based morphometry,” Human Brain Map-
ping, vol. 31, no. 4, pp. 556–566, 2010.
 J. S. Anderson, T. J. Druzgal, A. Froehlich et al., “Decreased
interhemispheric functional connectivity in autism,” Cerebral
Cortex, vol. 21, no. 5, pp. 1134–1146, 2011.
spectrum disorder: studies of infants at risk,” Neural Networks,
vol. 23, no. 8-9, pp. 1072–1076, 2010.
 G. A. Stefanatos and I. S. Baron, “The ontogenesis of language
impairment in autism: a neuropsychological perspective,” Neu-
ropsychology Review, vol. 21, no. 3, pp. 252–270, 2011.
Neurology, vol. 23, no. 2, pp. 124–130, 2010.
 V. Haroutunian and J. Pickett, “Autism brain tissue banking,”
Brain Pathology, vol. 17, no. 4, pp. 412–421, 2007.
 D. G. Amaral, “The promise and the pitfalls of autism research:
an introductory note for new autism researchers,” Brain
Research, vol. 1380, pp. 3–9, 2011.
 D. Moher, A. Liberati, J. Tetzlaff, and D. G. Altman, “Preferred
reporting items for systematic reviews and meta-analyses: the
PRISMA statement,” PLoS Medicine, vol. 6, no. 7, Article ID
 C. M. Schumann, C. C. Barnes, C. Lord, and E. Courchesne,
“Amygdala enlargement in toddlers with autism related to
severity of social and communication impairments,” Biological
Psychiatry, vol. 66, no. 10, pp. 942–949, 2009.
 C. W. Nordahl, R. Scholz, X. Yang et al., “Increased rate of
amygdala growth in children aged 2 to 4 years with autism
spectrum disorders: a longitudinal study,” Archives of General
Psychiatry, vol. 69, no. 1, pp. 53–61, 2012.
 M. W. Mosconi, H. Cody-Hazlett, M. D. Poe, G. Gerig, R.
Gimpel-Smith, and J. Piven, “Longitudinal study of amygdala
volume and joint attention in 2- to 4-year-old children with
autism,” Archives of General Psychiatry, vol. 66, no. 5, pp. 509–
 H. C. Hazlett, M. D. Poe, A. A. Lightbody et al., “Teasing apart
the heterogeneity of autism: same behavior, different brains
in toddlers with fragile X syndrome and autism,” Journal of
Neurodevelopmental Disorders, vol. 1, no. 1, pp. 81–90, 2009.
volumes and behavioral correlates in children with autism
 E. Courchesne, P. R. Mouton, M. E. Calhoun et al., “Neuron
number and size in prefrontal cortex of children with autism,”
 K. Holmboe, M. Elsabbagh, A. Volein et al., “Frontal cortex
functioning in the infant broader autism phenotype,” Infant
Behavior and Development, vol. 33, no. 4, pp. 482–491, 2010.
 M. Santos, N. Uppal, C. Butti et al., “von Economo neurons
in autism: a stereologic study of the frontoinsular cortex in
children,” Brain Research, vol. 1380, pp. 206–217, 2011.
 L. T. Eyler, K. Pierce, and E. Courchesne, “A failure of left
temporal cortex to specialize for language is an early emerging
and fundamental property of autism,” Brain, vol. 135, no. 3, pp.
in autism associated with an increase in cortical surface area
before age 2 years,” Archives of General Psychiatry, vol. 68, no.
5, pp. 467–476, 2011.
 F. Hoeft, E. Walter, A. A. Lightbody et al., “Neuroanatomical
differences in toddler boys with fragile X syndrome and
idiopathic autism,” Archives of General Psychiatry, vol. 68, no.
3, pp. 295–305, 2011.
 C. M. Schumann, C. S. Bloss, C. C. Barnes et al., “Longitudinal
magnetic resonance imaging study of cortical development
30, no. 12, pp. 4419–4427, 2010.
 N. N. J. Rommelse, C. T. R. Peters, I. J. Oosterling et al., “A pilot
study of abnormal growth in autism spectrum disorders and
other childhood psychiatric disorders,” Journal of Autism and
Developmental Disorders, vol. 41, no. 1, pp. 44–54, 2011.
 F. Muratori, S. Calderoni, F. Apicella et al., “Tracing back to
the onset of abnormal head circumference growth in Italian
children with autism spectrum disorder,” Research in Autism
Spectrum Disorders, vol. 6, no. 1, pp. 442–449, 2012.
S. Kagami, “Head circumference and body growth in autism
spectrum disorders,” Brain and Development, vol. 33, no. 7, pp.
 K. Chawarska, D. Campbell, L. Chen, F. Shic, A. Klin, and J.
Chang, “Early generalized overgrowth in boys with autism,”
 C. W. Nordahl, N. Lange, D. D. Li et al., “Brain enlargement is
associated with regression in preschool-age boys with autism
spectrum disorders,” Proceedings of the National Academy of
Sciences of the United States of America, vol. 108, no. 50, pp.
children with autism spectrum disorders,” Pediatric Neurology,
vol. 44, no. 2, pp. 97–100, 2011.
C. E. Pennell, “Brief report: a preliminary study of fetal head
circumference growth in autism spectrum disorder,” Journal of
Autism and Developmental Disorders, vol. 41, no. 1, pp. 122–129,
 J. N. Constantino, P. Majmudar, A. Bottini et al., “Infant head
growth in male siblings of children with and without autism
spectrum disorders,” Journal of Neurodevelopmental Disorders,
vol. 2, no. 1, pp. 39–46, 2010.
 K. M. Gray, J. Taffe, D. J. Sweeney, S. Forster, and B. J. Tonge,
“Could head circumference be used to screen for autism in
young males with developmental delay?” Journal of Paediatrics
and Child Health, vol. 48, no. 4, pp. 329–334, 2012.
 K. D. Mraz, J. Dixon, T. Dumont-Mathieu, and D. Fein,
“Accelerated head and body growth in infants later diagnosed
outcome children,” Journal of Child Neurology, vol. 24, no. 7, pp.
 J. P. McCleery, N. Akshoomoff, K. R. Dobkins, and L. J. Carver,
“Atypical face versus object processing and hemispheric asym-
metries in 10-month-old infants at risk for autism,” Biological
Psychiatry, vol. 66, no. 10, pp. 950–957, 2009.
 R. J. Luyster, J. B. Wagner, V. Vogel-Farley, H. Tager-Flusberg,
and C. A. Nelson III, “Neural correlates of familiar and unfa-
miliar face processing in infants at risk for autism spectrum
 M. Elsabbagh, A. Volein, G. Csibra et al., “Neural correlates of
eye gaze processing in the infant broader autism phenotype,”
Biological Psychiatry, vol. 65, no. 1, pp. 31–38, 2009.
 A. P. F. Key and W. L. Stone, “Processing of novel and
familiar faces in infants at average and high risk for autism,”
 A. P. Key and W. L. Stone, “Same but different: 9-month-old
features but process them using different brain mechanisms,”
Autism Research, vol. 5, no. 4, pp. 253–266, 2012.
 K. Chawarska and F. Shic, “Looking but not seeing: atypical
visual scanning and recognition of faces in 2 and 4-Year-old
Developmental Disorders, vol. 39, no. 12, pp. 1663–1672, 2009.
 W. Bosl, A. Tierney, H. Tager-Flusberg, and C. Nelson, “EEG
complexity as a biomarker for autism spectrum disorder risk,”
BMC Medicine, vol. 9, article 18, 2011.
 S. J. Webb, E. J. H. Jones, K. Merkle et al., “Developmental
change in the ERP responses to familiar faces in toddlers with
autism spectrum disorders versus typical development,” Child
Development, vol. 82, no. 6, pp. 1868–1886, 2011.
nization in toddlers with autism,” Neuron, vol. 70, no. 6, pp.
 D. Stahl, A. Pickles, M. Elsabbagh, M. H. Johnson, and The
BASIS Team, “Novel machine learning methods for ERP anal-
ysis: a validation from research on infants at risk for autism,”
Developmental Neuropsychology, vol. 37, no. 3, pp. 274–298,
 J. J. Wolff, H. Gu, G. Gerig et al., “Differences in white matter
fiber tract development present from 6 to 24 months in infants
pp. 589–600, 2012.
 H. C. Hazlett, M. D. Poe, A. A. Lightbody et al., “Trajectories
of early brain volume development in fragile X syndrome
and autism,” Journal of the American Academy of Child and
Adolescent Psychiatry, vol. 51, no. 9, pp. 921–933, 2012.
 S. Calderoni, A. Retico, L. Biagi, R. Tancredi, F. Muratori, and
M. Tosetti, “Female children with autism spectrum disorder:
an insight from mass-univariate and pattern classification
analyses,” NeuroImage, vol. 59, no. 2, pp. 1013–1022, 2012.
 M. Zeegers, H. H. Pol, S. Durston et al., “No differences in
MR-based volumetry between 2- and 7-year-old children with
autism spectrum disorder and developmental delay,” Brain and
Development, vol. 31, no. 10, pp. 725–730, 2009.
 H. C. Hazlett, H. Gu, R. C. McKinstry et al., “Brain volume
The American Journal of Psychiatry, vol. 169, no. 6, pp. 601–608,
 F. H. Duffy and H. Als, “A stable pattern of EEG spectral
coherence distinguishes children with autism from neuro-
typical controls: a large case control study,” BMC Medicine, vol.
10, no. 1, article 64, 2012.
 C. M. Schumann, J. Hamstra, B. L. Goodlin-Jones et al., “The
amygdala is enlarged in children but not adolescents with
autism; the hippocampus is enlarged at all ages,” Journal of
Neuroscience, vol. 24, no. 28, pp. 6392–6401, 2004.
 B. F. Sparks, S. D. Friedman, D. W. Shaw et al., “Brain structural
abnormalities in young children with autism spectrum disor-
der,” Neurology, vol. 59, no. 2, pp. 184–192, 2002.
 C. M. Schumann and D. G. Amaral, “Stereological analysis of
amygdala neuron number in autism,” Journal of Neuroscience,
vol. 26, no. 29, pp. 7674–7679, 2006.
of amygdala and hippocampus in non-mentally retarded autis-
tic adolescents and adults,” Neurology, vol. 53, no. 9, pp. 2145–
 K. Pierce, R.-A. M¨ uller, J. Ambrose, G. Allen, and E. Courch-
esne, “Face processing occurs outside the fusiform “face area”
in autism: evidence from functional MRI,” Brain, vol. 124, no.
10, pp. 2059–2073, 2001.
 B. M. Nacewicz, K. M. Dalton, T. Johnstone et al., “Amygdala
volume and nonverbal social impairment in adolescent and
no. 12, pp. 1417–1428, 2006.
and macrocephaly,” The American Journal of Neuroradiology,
vol. 24, no. 10, pp. 2066–2076, 2003.
 M. M. Haznedar, M. S. Buchsbaum, T.-C. Wei et al., “Limbic
circuitry in patients with autism spectrum disorders studied
with positron emission tomography and magnetic resonance
imaging,” The American Journal of Psychiatry, vol. 157, no. 12,
pp. 1994–2001, 2000.
 S. J. M. C. Palmen, S. Durston, H. Nederveen, and H. Van
Engeland, “No evidence for preferential involvement of medial
temporal lobe structures in high-functioning autism,” Psycho-
logical Medicine, vol. 36, no. 6, pp. 827–834, 2006.
 M. L. Bauman and T. L. Kemper, “Neuroanatomic observations
of the brain in autism: a review and future directions,” Interna-
tional Journal of Developmental Neuroscience, vol. 23, no. 2-3,
pp. 183–187, 2005.
 S. J. M. C. Palmen, H. Van Engeland, P. R. Hof, and C. Schmitz,
“Neuropathological findings in autism,” Brain, vol. 127, no. 12,
pp. 2572–2583, 2004.
 C. S. Bloss and E. Courchesne, “MRI neuroanatomy in young
girls with autism: a preliminary study,” Journal of the American
Academy of Child and Adolescent Psychiatry, vol. 46, no. 4, pp.
 E. Courchesne, C. M. Karns, H. R. Davis et al., “Unusual brain
growth patterns in early life in patients with autistic disorder:
an MRI study,” Neurology, vol. 57, no. 2, pp. 245–254, 2001.
 R. A. Carper, P. Moses, Z. D. Tigue, and E. Courchesne,
“Cerebral lobes in autism: early hyperplasia and abnormal age
effects,” NeuroImage, vol. 16, no. 4, pp. 1038–1051, 2002.
 R. A. Carper and E. Courchesne, “Localized enlargement of the
frontal cortex in early autism,” Biological Psychiatry, vol. 57, no.
2, pp. 126–133, 2005.
 E. Courchesne and K. Pierce, “Why the frontal cortex in autism
might be talking only to itself: local over-connectivity but long-
distance disconnection,” Current Opinion in Neurobiology, vol.
15, no. 2, pp. 225–230, 2005.
 H. Kosaka, M. Omori, T. Munesue et al., “Smaller insula
and inferior frontal volumes in young adults with pervasive
developmental disorders,” NeuroImage, vol. 50, no. 4, pp. 1357–
 A. De Giacomo and E. Fombonne, “Parental recognition of
developmental abnormalities in autism,” European Child and
Adolescent Psychiatry, vol. 7, no. 3, pp. 131–136, 1998.
 A. M. Wetherby, J. Woods, L. Allen, J. Cleary, H. Dickinson,
and C. Lord, “Early indicators of autism spectrum disorders in
the second year of life,” Journal of Autism and Developmental
Disorders, vol. 34, no. 5, pp. 473–493, 2004.
 E. Redcay and E. Courchesne, “Deviant functional magnetic
resonance imaging patterns of brain activity to speech in 2-
3-year-old children with autism spectrum disorder,” Biological
Psychiatry, vol. 64, no. 7, pp. 589–598, 2008.
 H. C. Hazlett, M. Poe, G. Gerig et al., “Magnetic resonance
imaging and head circumference study of brain size in autism:
birth through age 2 years,” Archives of General Psychiatry, vol.
62, no. 12, pp. 1366–1376, 2005.
 H. Petropoulos, S. D. Friedman, D. W. W. Shaw, A. A. Artru, G.
Dawson, and S. R. Dager, “Gray matter abnormalities in autism
no. 4, pp. 632–636, 2006.
 C. Gillberg and L. DeSouza, “Head circumference in autism,
Asperger syndrome, and ADHD: a comparative study,” Devel-
opmental Medicine and Child Neurology, vol. 44, no. 5, pp. 296–
 L. Kanner, “Autistic disturbances of affective contact,” Child’s
Nervous System, vol. 2, no. 3, pp. 217–250, 1943.
dren with autism spectrum disorders are rarelymacrocephalic.
A population study”.
brain development in autism,” Neuron, vol. 56, no. 2, pp. 399–
 G. Dawson, J. Munson, S. J. Webb, T. Nalty, R. Abbott, and K.
Toth, “Rate of head growth decelerates and symptoms worsen
in the second year of life in autism,” Biological Psychiatry, vol.
61, no. 4, pp. 458–464, 2007.
 K. M. Gray, B. J. Tonge, D. J. Sweeney, and S. L. Einfeld,
“Screening for autism in young children with developmental
delay: an evaluation of the developmental behaviour checklist:
early screen,” Journal of Autism and Developmental Disorders,
vol. 38, no. 6, pp. 1003–1010, 2008.
 C. Dissanayake, Q. M. Bui, R. Huggins, and D. Z. Loesch,
autism and Asperger disorder during the first 3 years of life,”
Development and Psychopathology, vol. 18, no. 2, pp. 381–393,
 A. Fukumoto, T. Hashimoto, H. Ito et al., “Growth of head
circumference in autistic infants during the first year of life,”
Journal of Autism and Developmental Disorders, vol. 38, no. 3,
pp. 411–418, 2008.
 E. Redcay and E. Courchesne, “When is the brain enlarged in
autism? A meta-analysis of all brain size reports,” Biological
Psychiatry, vol. 58, no. 1, pp. 1–9, 2005.
circumference as an early predictor of autism symptoms in
younger siblings of children with autism spectrum disorder,”
Journal of Autism and Developmental Disorders, vol. 38, no. 6,
pp. 1104–1111, 2008.
 E. F. Torrey, D. Dhavale, J. P. Lawlor, and R. H. Yolken, “Autism
and head circumference in the first year of life,” Biological
Psychiatry, vol. 56, no. 11, pp. 892–894, 2004.
 T. D. Cassel, D. S. Messinger, L. V. Ibanez, J. D. Haltigan, S.
I. Acosta, and A. C. Buchman, “Early social and emotional
communication in the infant siblings of children with autism
spectrum disorders: an examination of the broad phenotype,”
Journal of Autism and Developmental Disorders, vol. 37, no. 1,
pp. 122–132, 2007.
 A. S. Nadig, S. Ozonoff, G. S. Young, A. Rozga, M. Sigman,
and S. J. Rogers, “A prospective study of response to name in
infants at risk for autism,” Archives of Pediatrics and Adolescent
Medicine, vol. 161, no. 4, pp. 378–383, 2007.
 R. Kulisek, Z. Hrncir, M. Hrdlicka et al., “Nonlinear analysis
of the sleep EEG in children with pervasive developmental
 S. J. Webb, G. Dawson, R. Bernier, and H. Panagiotides, “ERP
evidence of atypical face processing in young children with
no. 7, pp. 881–890, 2006.
 J. McPartland, G. Dawson, S. J. Webb, H. Panagiotides, and L. J.
poral processing of faces in autism spectrum disorder,” Journal
no. 7, pp. 1235–1245, 2004.
 K. A. Pelphrey, J. P. Morris, and G. McCarthy, “Neural basis of
eye gaze processing deficits in autism,” Brain, vol. 128, no. 5, pp.
 G. Dawson, S. Webb, G. D. Schellenberg et al., “Defining the
broader phenotype of autism: genetic, brain, and behavioral
perspectives,” Development and Psychopathology, vol. 14, no. 3,
pp. 581–611, 2002.
 M. H. Johnson, R. Griffin, G. Csibra et al., “The emergence of
the social brain network: evidence from typical and atypical
development,” Development and Psychopathology, vol. 17, no. 3,
pp. 599–619, 2005.
 E. Kotsoni, D. Mareschal, G. Csibra, and M. H. Johnson,
“Common-onset visual masking in infancy: behavioral and
electrophysiological evidence,” Journal of Cognitive Neuro-
science, vol. 18, no. 6, pp. 966–973, 2006.
 K. Pierce, “Early functional brain development in autism and
the promise of sleep fMRI,” Brain Research, vol. 1380, pp. 162–
 K. A. Pelphrey and J. C. McPartland, “Brain development:
neural signature predicts autism’s emergence,” Current Biology,
vol. 22, no. 4, pp. R127–R128, 2012.
20Behavioural Neurology Download full-text
 R. Griffin and C. Westbury, “Infant EEG activity as a biomarker
for autism: a promising approach or a false promise?” BMC
Medicine, vol. 9, article 61, 2011.
 B. D. Pearce, P. Thorsen, K. M. Sullivan, and P. B. Ryan,
“Pathophysiological pathways in autism spectrum disorders:
tal Medicine and Biology, vol. 8, pp. 231–249, 2010.
 B. E. Yerys and B. F. Pennington, “How do we establish a
biological marker for a behaviorally defined disorder? Autism
as a test case,” Autism Research, vol. 4, no. 4, pp. 239–241, 2011.
 F.Happ´ e,A.Ronald,andR.Plomin,“Timetogiveuponasingle
explanation for autism,” Nature Neuroscience, vol. 9, no. 10, pp.