letterdoi:10.1038/nature21369

Early brain development in infants at high risk forautism spectrum disorder
Heather Cody Hazlett1,2, Hongbin Gu1, Brent C. Munsell3, Sun Hyung Kim1, Martin Styner1, Jason J. Wolff4, Jed T. Elison5,
Meghan R. Swanson2, Hongtu Zhu6, Kelly N. Botteron7, D. Louis Collins11, John N. Constantino7, Stephen R. Dager8,9,Annette M. Estes9,10, Alan C. Evans11, Vladimir S. Fonov11, Guido Gerig12, Penelope Kostopoulos11, Robert C. McKinstry13,
Juhi Pandey14, Sarah Paterson15, John R. Pruett Jr7, Robert T. Schultz14, Dennis W. Shaw8,9, Lonnie Zwaigenbaum16,
Joseph Piven1,2 & the IBIS Network*

Brain enlargement has been observed in children with autism
spectrum disorder (ASD), but the timing of this phenomenon, andthe relationship between ASD and the appearance of behaviouralsymptoms, are unknown. Retrospective head circumference andlongitudinal brain volume studies of two-year olds followed up
at four years of age have provided evidence that increased brain
volume may emerge early in development1,2. Studies of infants
at high familial risk of autism can provide insight into the early
development of autism and have shown that characteristic social
deficits in ASD emerge during the latter part of the first and in thesecond year of life3,4. These observations suggest that prospective
brain-imaging studies of infants at high familial risk of ASD might
identify early postnatal changes in brain volume that occur before
an ASD diagnosis. In this prospective neuroimaging study of
106 infants at high familial risk of ASD and 42 low-risk infants,
we show that hyperexpansion of the cortical surface area between
6 and 12 months of age precedes brain volume overgrowth observed
between 12 and 24 months in 15 high-risk infants who werediagnosed with autism at 24 months. Brain volume overgrowth
was linked to the emergence and severity of autistic social deficits.
A deep-learning algorithm that primarily uses surface area
information from magnetic resonance imaging of the brain of
6–12-month-old individuals predicted the diagnosis of autism
in individual high-risk children at 24 months (with a positive
predictive value of 81% and a sensitivity of 88%). These findings
demonstrate that early brain changes occur during the period inwhich autistic behaviours are first emerging.
We first reported increased brain volume in adolescents and adults
with ASD over twenty years ago5. Subsequent reports suggested
that brain overgrowth in ASD may be most apparent during earlychildhood6–8. A study of infants at risk for ASD (33 high-risk and22 low-risk infants), scanned from 6 to 24 months of age, foundenlarged brain volume present at 12 and 24 months in the 10 infants
that were later diagnosed with autism at 24 months of age or later9(mean age, 32.5 months).
In the present study, we examined data from a subset of individuals from a longitudinal study comprising 318 infants at high familial
risk for ASD (HR), of which 70 met clinical best-estimate criteria
for ASD (HR-ASD) and 248 that did not meet the criteria for ASD(HR-neg) at 24 months of age, and 117 infants at low familial risk (LR)for ASD, who also did not meet the criteria for ASD at 24 months(see Methods for diagnostic and exclusion criteria). The three groups
were comparable in (mean) race/ethnicity (85% white), family income,maternal age at birth (33 years old), infant birth weight (8 lb), and gestational age at birth (39 weeks). The HR-ASD group had more males thanthe other two groups (83% of the HR-ASD group was male compared to
59% and 57% of the LR and HR-neg groups, respectively) and mothersin the LR group had a higher education level (Extended Data Table 1).
Infants were evaluated at 6, 12 and 24 months of age, which included
detailed behavioural assessments and high-resolution magnetic resonance imaging (MRI) of the brain, to prospectively investigate brain
and behavioural trajectories during infancy. The analyses described
below were conducted on a subset of 106 high-risk (n =​  15 HR-ASD;
n =​ 91 HR-neg) and 42 low-risk infants for whom all three MRI scans
were successfully obtained. On the basis of our previous findings at
2–4 years of age2, we hypothesized that brain overgrowth in ASD begins
before 24 months of age; that overgrowth is associated with hyper­
expansion of the cortical surface area; and that these early brain changes
are temporally linked to the emergence of the defining behaviours of
ASD. We also investigated whether differences in the development ofbrain characteristics might suggest early biomarkers (that is, occurring
before the onset of the defining behaviours of ASD) for the detectionof ASD.
We first examined group differences in the trajectories of brain
growth rate (Fig. 1). The growth rate of the total brain volume (TBV) did
not differ between groups from 6 to 12 months of age. However, pairwise comparisons at 24 months showed large effect sizes for HR-ASD
compared to LR and HR-ASD compared to HR-neg. The HR-ASD
group showed a significantly increased TBV growth rate in the second
year compared to both the LR and HR-neg groups (Extended Data Table 2).
In addition, the HR-ASD group showed a significantly increased surface area growth rate from 6 to 12 months of age compared to boththe HR-neg and LR groups, with the most robust increases observedin the left/right middle occipital gyrus, right cuneus and right lingual
gyrus area (see Fig. 2). No group differences were observed in cortical
thickness. We observed a significant correlation between surface area
growth rate of 6–12 months and enlargement in TBV at 24 months ofage in all subjects (r192 =​  0.59, P <​ 0.001), as well as in the combined
HR subgroup (r139 =​  0.63, P <​ 0.001). Raw means, standard deviations
and effect sizes for the group comparisons of TBV and surface area areprovided in Extended Data Table 3. Regional differences in surface areachange rate (6–12 months) were observed in the HR-ASD group (Fig. 2).

1

Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina 27599, USA. 2Carolina Institute for Developmental Disabilities, Chapel Hill, North Carolina 27599, USA. 3College
of Charleston, Charleston, South Carolina 29424, USA. 4Department of Educational Psychology, University of Minnesota, Minneapolis, Minnesota 55455, USA. 5Institute of Child Development,
University of Minnesota, Minneapolis, Minnesota 55455, USA. 6Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina 27599, USA. 7Department of Psychiatry,
Washington University School of Medicine, St. Louis, Missouri 63110, USA. 8Department of Radiology, University of Washington, Seattle, Washington 98105, USA. 9Center on Human Development
and Disability, University of Washington, Seattle, Washington 98105, USA. 10Department of Speech and Hearing Sciences, University of Washington, Seattle, Washington 98105, USA. 11Montreal
Neurological Institute, McGill University, Montreal, Quebec H3A 0G4, Canada. 12Tandon School of Engineering, New York University, New York, New York 10003, USA. 13Mallinckrodt Institute ofRadiology, Washington University, St. Louis, Missouri 63110, USA. 14Center for Autism Research, The Children’s Hospital of Philadelphia and University of Pennsylvania, Philadelphia, Pennsylvania
19104, USA. 15Department of Psychology, Temple University, Philadelphia, Pennsylvania 19122, USA. 16Department of Pediatrics, University of Alberta, Edmonton, Alberta T6G 2R3, Canada.
*A list of participants and their affiliations appears at the end of the paper.
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letter RESEARCH
1,400,000

90,000.0

1,300,000
Total surface area (mm2)

TBV (mm3)

1,100,000
1,000,000
900,000
800,000
700,000

Total cortical thickness (mm)80,000.0

1,200,000

70,000.0

60,000.0

50,000.0

600,000
500,000

0 5 10 15 20 25 30 35 40 45 50

40,000.0

05 10 15 20 25 30 35 40 45 50

Corrected age (months)

Corrected age (months)

6.7
6.6
6.5
6.4
6.3
6.2
6.1
6.0
5.9
5.8
5.7
5.6
5.5
5.4
5.3
5.2
5.1

0

5 10 15 20 25 30 35 40 45 50
Corrected age (months)

Figure 1 | Longitudinal trajectories of TBV, surface area and cortical
thickness from 6 to 24 months. Longitudinal trajectories of TBV, cortical
thickness and surface areas from 6 to 24 months for individuals from the
HR-ASD (red), HR-neg (green) or LR (blue) groups. Only individuals
with complete longitudinal imaging (for 6, 12, and 24 months) were
included in the analysis (HR-ASD, n =​  15; HR-neg, n =​  91; LR, n =​  42).
Group trajectories were estimated from the random coefficient piecewise
linear model (see Methods). The HR-ASD group showed a significantly
increased surface area growth rate in the first year of life (from 6 to
12 months) compared to both the HR-neg (t289 =​  2.01, P =​ 0.04) and LR
groups (t289 =​  2.50, P =​ 0.01). There were no significant group differencesin surface area growth rates in the second year (Extended Data Table 2).
Pairwise comparisons of surface area measured at 12 months of age
showed medium to large effect sizes for HR-ASD vs LR (Cohen’s d =​  0.74)
and HR-ASD vs HR-neg (Cohen’s d =​ 0.41), which became more robust
by 24 months for HR-ASD vs LR (Cohen’s d =​ 0.88) and HR-ASD vs
HR-neg (Cohen’s d =​ 0.70). There were no significant group differencesin trajectories for cortical thickness, with all groups showing a patternof a decrease in cortical thickness over time. No group differences wereobserved in the trajectory of cortical thickness growth in either the first(F2,289 =​  0.00; P =​ 0.99) or second year (F2,289 =​  1.44; P =​ 0.24). The age(in months) is corrected by length (body size, in cm).

Given that the timing of TBV overgrowth in our study coincidedwith findings from other studies that have shown emergence of social
deficits in the second year of life, we explored whether the rate of volume overgrowth was linked to autism severity. Pearson correlations
between TBV and behavioural measurements of autism symptoms andsocial communication (on the autism diagnostic observation
Schedule10 (ADOS) and communication and symbolic behaviour
scales11 (CSBS)) were generated, adjusting for multiple comparisons.
We first investigated the relationship between autistic behaviour(ADOS severity score) at 24 months and the TBV change rate at
6–12 and 12–24 months in the HR groups (HR-ASD and HR-neg).
We found no significant correlation between the 24-month ADOS
severity score and the 6–12 month TBV change rate (r174 =​  0.14,
P =​ 0.06); however, a significant correlation was found between the
24-month ADOS severity score and the 12–24 month TBV change
rate (r193 =​  0.16, P =​ 0.03). Subsequent analyses designed to investigate the components of overall autism severity (ADOS) during
the latter interval revealed a significant correlation between the
12–24 month TBV change rate and the 24-month ADOS social affectscore (r194 =​  0.17, P =​ 0.01), but not the ADOS restricted/repetitive
behavi­our score (r194 =​  0.07, P =​  0.31).
To follow up on the relationship between change in brain volume
and social deficits in the second year described above, we investigated
the relationship between TBV change rates and social behaviour at
24 months with an independent measure of social behaviour, the CSBS.
Consistent with the findings from the ADOS analysis, the CSBS social
composite score was significantly correlated with a more rapid TBVchange rate at 12–24 months (r158 =​  0.18, P =​ 0.02) in HR subjects. No
significant correlations were observed between CSBS social composite
score at 24 months and TBV change rate at 6–12 months (r143 =​  0.11,
P =​  0.17).
As opposed to the ADOS, which was only carried out at 24 months(the ADOS was primarily designed as a tool for diagnosis), measurements of social behaviour were available from the CSBS at both 12 and24 months. We investigated the change in social behaviour during thistime, taking into consideration our observation of changes in brain
volume during that same period in the HR-ASD group, and a previous
report that social deficits in ASD appear to unfold during the second
year of life3. We observed a significant group (HR-ASD versus HR-neg)
×​time (12–24 months) interaction for the CSBS social composite score(F2,130 =​  10.0, P <​ 0.0001). This finding was further supported by theobservation that the CSBS effect size almost tripled from 12 (d =​  0.39)
to 24 (d =​  1.22) months.

On the basis of earlier findings from our group with regards to surface area, cortical thickness and brain volume2, we examined whether
selected MRI brain measurements at 6 and 12 months of age could
be used to accurately identify those infants who would later meet thecriteria for ASD at 24 months of age. Independent of knowledge about
the results of the above analyses, a machine learning classification
algorithm based on a deep-learning network, was employed to investigate how well regional surface area and cortical thickness at 6 and 12
months, intracranial volume (at 6 and 12 months) and sex predicted
HR-ASD diagnosis at 24 months of age. We used only data from thoseinfants for whom cortical thickness and surface area data at both 6 and12 months were available (HR-ASD =​  34, HR-neg =​ 145). A tenfold
cross-validation was used to compute classification performance,
whereby the whole classification procedure, including network training
was performed separately in each fold (see Supplementary Information
for details on method, validation and comparison to other approaches).
The classification scheme distinguished the HR-ASD group fromthe HR-neg group in the cross-validation with 94% accuracy (n =​  168out of 179), 88% sensitivity (n =​ 30 out of 34), 95% specificity (n =​  138out of 145), 81% positive predictive value (n =​ 30 out of 37) and 97%
negative predictive value (n =​ 138 out of 142) (Extended Data Table 4).
Additional analysis of the trained deep-learning network suggeststhat contributions to the discrimination are mostly on the basis of surface area and not cortical thickness (or TBV or sex), particularly at
6 months of age, as 11 of the top 12 measurements that contributed tothe deep-learning network were regional surface area variables andthe top six were variables from 6 months of age (Fig. 3 and Extended
Data Fig. 1).
Our data suggest that very early, post-natal hyperexpansion of corticalsurface areas may have an important role in the development of autism.
The rate of cortical surface area expansion from 6 to 12 months was significantly increased in individuals diagnosed with autism at 24 months,
and was linked to subsequent brain overgrowth, which, in turn, waslinked to the emergence of social deficits. This suggests a sequence
whereby hyperexpansion of the cortical surface area is an early event ina cascade leading to brain overgrowth and emerging autistic deficits. In
infants diagnosed with autism at 24 months, surface area hyperexpansion in the first year was observed in cortical areas linked to the processing of sensory information (for example, the left middle occipitalcortex), consistent with regions previously reported to show the earliest
increase in surface area growth rate in typically developing infants12,
and with reports showing early sensory differences in infants who will
later develop ASD13,14.
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RESEARCH letter
4

3
1

1

2

6 months
P value < 0.05: LR vs HR-ASD
0

Contribution

High

Low

12 months
High0.01 0.02 0.03 0.04 0.05

Figure 2 | Cortical regions that show significant expansion in surface
area from 6 to 12 months in HR-ASD. A map of significant groupdifferences in surface area from 6 to 12 months. Exploratory analyses
were conducted with a surface map containing 78 regions of interest(see Supplementary Information), using an adaptive Hochberg methodof P <​ 0.05. The coloured areas show the group effect for the HR-ASD
versus LR subjects. Compared to the LR group, the HR-ASD group had
significant expansion in the cortical surface area in the left/right middle
occipital gyrus and right cuneus (1), right lingual gyrus (2), and to a lesser
extent in the left inferior temporal gyrus (3), and middle frontal gyrus (4)(HR-ASD, n =​  34; LR, n =​  84).

Figure 3 | Visualization of cortical regions with surface area
measurements among the top 40 features contributing to the reduction
in deep learning dimensionality. The cortical regions with surface area
measurements that were among the top 40 features obtained from thenonlinear deep learning approach are visualized. The top 10 deep learningfeatures observed include: surface area at 6 months in the right and left
superior frontal gyrus, post-central gyrus, and inferior parietal gyri, andintracranial volume at 6 months. These features produced by the deeplearning approach are highly consistent with those observed using analternative approach (linear sparse learning) (Extended Data Fig. 1). Two
tables listing the top 40 features from the deep learning approach andsparse learning are provided in Supplementary Tables 2 and 3.

The finding of brain overgrowth in this sample of young children
with ‘idiopathic’ ASD is consistent with emerging literature demonstrating brain overgrowth in genetically defined ASD subgroups(for example, 16p11 deletions (ref. 15), CHD8 (ref. 16)). Cellular mechanisms and heritability that underlie surface area expansion are thought
to differ from mechanisms that underlie cortical thickness17,18, and surface area hyperexpansion has been reported in genetically-engineeredmouse models of autism19. Our findings are not inconsistent with themini-column hypothesis of autism20, which postulates that symmetricalproliferation of periventricular progenitor cells leads to an increased
number of mini-columns. These mini-columns may have a role in thepathogenesis of surface area hyperexpansion and the later emergenceof the disorder18,21. Overproliferation of cortical progenitor cells may
affect other mechanisms of post-natal development (for example, dendritic arborization and decreased pruning22). Overproduction of upperlayer neurons in the neocortex was previously shown to be associatedwith autism-like features in mice23 and the 16p11.2-deletion mousehas been shown to exhibit altered cortical progenitor proliferation24.
Furthermore, an imaging study described increased brain volume inindividuals with a 16p11 deletion, a genetically defined subgroup ofindividuals often presenting with ‘syndromic autism’15. Expansion ofbasal progenitor cells in rodent models25 has been shown to regulate
cerebral volume size and folding, while the dysregulation of neuralprogenitor-cell proliferation has been observed in genetically engineeredmouse models of ASD-associated genes (for example, CHD8)26. The
importance of CHD8 in mediating regulatory networks during neuro­development was previously demonstrated27 and suggests a potential
role of CHD8 in disrupting the proliferation and differentiation ofneurons during early human brain development. In addition, increased
rates of proliferation of neural progenitor cells and neuron number
compared to controls have been observed in induced pluripotent stem
cells derived from individuals with ASD who also had increased brain
volume on MRI28. Increased proliferation resulted from dysregulationof a β​-catenin/BRN2 transcriptional cascade and was associated with

reduced synaptogenesis that led to functional defects in neuronal networks, and these proliferation deficits could be rescued by stimulating
the insulin growth factor 1 pathway28. The findings in the present
study together with these recent reports suggest that understanding
the mechanisms that underlie surface area hyperexpansion in the firstyear in human infants can probably provide important insights into thedownstream pathogenesis of autism.
Prediction models developed from behaviourally based algorithms
during infancy have not provided sufficient predictive power to beclinically useful29. We found that a deep-learning algorithm primarily
using surface area information from brain MRI at 6 and 12 monthsof age predicted the 24 month diagnosis of autism in children athigh familial risk for autism. This finding may have implications
for early detection and intervention, given that this period is before
consolidation of the defining features of ASD and the typical age fordiagnosis30. The latter part of the first and early second years of life arecharacterized by greater neural plasticity relative to later ages and is atime when the social deficits associated with autism are not yet wellestablished. Intervention at this age may prove more efficacious thanlater in development. The fact that we demonstrate group differencesin surface area growth rate from 6 to 12 months, that very early surface
area changes are linked to later brain overgrowth in the second year,
and that overgrowth is, in turn, linked to the emergence of core social
deficits in autism during this period, provides additional context tosupport the validity of the prediction model we report. The positive
predictive value findings from this study on high-risk infants are probably conservative in nature owing to the likelihood that our HR-ASD
group may be higher functioning than those who are clinically referredand diagnosed with ASD at 24 months of age, and that HR-neg groups
are known to be more heterogeneous with respect to later developmentof cognitive, behavioural, social-communication and motor deficits
than typical case–control studies29,31–33. The algorithm described in thispaper will require replication before it could be considered a possible
clinical tool for predicting ASD in high familial risk infants, as false
diagnostic predictions have the potential to adversely affect individuals

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letter RESEARCH
and families. In addition, we do not know whether the brain differences we observed are specific to so-called idiopathic autism or sharecharacteristics with other neurodevelopmental disorders. Although thefindings of this study do not have direct application to the larger population of children with ASD who are not known to be at high familial
risk for ASD, they provide a proof of principle that early prodromal
detection using a brain biomarker may be possible. Future analyses
incorporating complementary data from other relevant modalities (forexample, behaviour, molecular genetics, electrophysiology and otherimaging modalities such as whole brain functional MRI) may improve
the accuracy of the prediction we observed.
Online Content Methods, along with any additional Extended Data display items andSource Data, are available in the online version of the paper; references unique tothese sections appear only in the online paper.
received 15 April 2016; accepted 6 January 2017.
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Supplementary Information is available in the online version of the paper.
Acknowledgements The IBIS (Infant Brain Imaging Study) Network is an NIH
funded Autism Center of Excellence (HDO55741) and consists of a consortiumof 8 Universities in the US and Canada. This work was supported by an NIH
Autism Center of Excellence grant (NIMH and NICHD HD055741 to J.Pi.),
Autism Speaks (6020) and the Simons Foundation (140209). Further support
was provided by the National Alliance for Medical Image Computing (NA-MIC),
funded by the NIH through grant U54 EB005149, the IDDRC Imaging andParticipant Registry cores (NICHD HD003110 to J.Pi.) and R01 MH093510(to J.R.P.Jr). We thank M. Burchinal and K. Y. Truong for their consultation on
the statistical methods and approach. Given the large commitment of time andeffort required by this study, we extend our appreciation to the families who
have participated in this study and the numerous research assistants and staff
who have contributed to this work.
Author Contributions All co-authors discussed the results, made criticalcontributions to the work and contributed to the writing of the manuscript.
H.C.H., K.N.B., S.R.D., A.M.E., R.C.M., S.P., J.Pi., R.T.S., J. Pa. and D.W.S. contributed
to the data collection. A.C.E., P.K. provided support for data management.
B.C.M., S.H.K., M.S., D.L.C., A.C.E., V.S.F. and G.G. conducted image processing.
H.G., B.C.M., S.H.K., M.S. and H.Z. analysed the data. H.C.H. wrote the manuscriptwith J.Pi., H.G., B.C.M., M.S. and with J.J.W., J.T.E., M.R.S., J.N.C., J.R.P.Jr, A.M.E.,
R.T.S. and L.Z. providing additional feedback.
Author Information Reprints and permissions information is available atwww.nature.com/reprints. The authors declare no competing financial
interests. Readers are welcome to comment on the online version of thepaper. Correspondence and requests for materials should be addressed to
H.C.H. (hcody@med.unc.edu).
Reviewer Information Nature thanks M. Johnson, G. Ramsay, T. Yarkoni and theother anonymous reviewer(s) for their contribution to the peer review of this work.
IBIS Network
Clinical Sites J. Piven1, H. C. Hazlett1, C. Chappell1, S. R. Dager2, A. M. Estes2,
D. W. Shaw2, K. N. Botteron3, R. C. McKinstry3, J. N. Constantino3, J. R. Pruett Jr3,
R. T. Schultz4, S. Paterson4, L. Zwaigenbaum5, J. T. Elison6, J. J. Wolff6;
Data Coordinating Center A. C. Evans7, D. L. Collins7, G. B. Pike7, V. S. Fonov7,
P. Kostopoulos7, S. Das7; Image Processing Core G. Gerig8; M. Styner1;
Statistical Analysis Core H. Gu1
1

University of North Carolina, Chapel Hill, North Carolina 27599, USA. 2University ofWashington, Seattle, Washington 98105, USA. 3Washington University, St. Louis, Missouri
63130, USA. 4Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, USA.
5
University of Alberta, Edmonton, Alberta T6G 2R3, Canada. 6University of Minnesota,
Minneapolis, Minnesota 55455, USA. 7Montreal Neurological Institute, Montreal, Quebec
H3A 0G4, Canada. 8New York University, New York, New York 10003, USA.

1 6 F e b ruar y 2 0 1 7 | V O L 5 4 2 | N A T U R E | 3 5 1© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.

RESEARCH letter
Methods

Data reporting. No statistical methods were used to predetermine sample size.
The experiments were not randomized.
Sample. This study includes data acquired from an NIH-funded Autism Centersof Excellence (ACE) network study, referred to as the ‘Infant Brain Imaging Study’(IBIS). The network includes four clinical data collection sites (University ofNorth Carolina at Chapel Hill, University of Washington, Children’s Hospital ofPhiladelphia, Washington University in St. Louis), a Data Coordinating Center at
the Montreal Neurological Institute (McGill University), and two image processing sites (University of Utah and UNC). Data collection sites had study protocols
approval from their Institutional Review Boards (IRB), and all enrolled subjects
had informed consent provided by parent/guardian. Infants at high (HR) and low
familial risk (LR) entered the study at 6 months of age (a subset of HR infants
entered at 12 months) and were followed-up at 12 and 24 months. Results fromthe 6 month brain volume findings have previously been reported for a subset of
this sample34.
Subjects were enrolled as HR if they had an older sibling with a clinical diagnosis of ASD confirmed with the Autism Diagnostic Interview-Revised35 (ADI-R).
Subjects were enrolled in the LR group if they had an older sibling without evidence of ASD and no family history of a first or second-degree relative with ASD.
Exclusion criteria for both groups included the following: (1) diagnosis or physicalsigns strongly suggestive of a genetic condition or syndrome (for example, fragile
X syndrome) reported to be associated with ASDs, (2) a significant medical orneurological condition affecting growth, development or cognition (for example,
CNS infection, seizure disorder, congenital heart disease), (3) sensory impairment such as vision or hearing loss, (4) low birth weight (<​2,000 g) or prematurity(<​36 weeks gestation), (5) possible perinatal brain injury from exposure toin utero exogenous compounds reported to likely affect the brain adversely in atleast some individuals (for example, alcohol, selected prescription medications),(6) non-English speaking families, (7) contraindication for MRI (for example,
metal implants), (8) adopted subjects, and (9) a family history of intellectual
disability, psychosis, schizophrenia or bipolar disorder in a first-degree relative.
The sample for this analysis included all children with longitudinal imaging dataprocessed until 31 August 2015. The final sample included 318 HR and 117 LR
children, each with 2–3 MRI scans (Extended Data Table 1).
Assessment protocols. Behavioural assessment. Infants were assessed at ages 6,
12 and 24 months and received a brain MRI scan in addition to a battery of behavioural and developmental tests. The tests included measurements of cognitive
development, adaptive functioning and behaviours associated with autism.
Developmental level and adaptive functioning were assessed at each time point
using the Mullen Scales of Early Learning36 and Vineland Scales of Adaptive
Behaviour37. Autism-oriented assessments included the Autism Diagnostic
Interview-Revised35, Autism Diagnostic Observation Scale10 (ADOS-WPS) at
24 months and Communication and Symbolic Behaviour Scales of Development
Profile11 (CSBS-DP) at 12 and 24 months. From the CSBS, the total raw score
and the social composite raw score were used in the brain–behaviouralanalyses. Raw scores were used to allow better representation of the distributionof the data.
Diagnostic (outcome) classification. Diagnostic classification was made by an expert
clinician at each site using all clinical, behavioural and questionnaire data available
at 24 months. A diagnosis of ASD was made using the DSM-IV-TR (diagnostic andstatistical manual of mental disorders, edition IV, text revision)38 criteria for autism
and pervasive developmental disorder, not otherwise specified (PDD-NOS)38 by
an expert clinician blind to the outcome of the imaging results. Across the IBIS
Network, the expert clinicians met quarterly for diagnostic reliability meetings(via video/telephone) using the DSM-IV-TR criteria independently. The rationale for this conservative approach was to maximize validity of diagnosis at
24 months of age39. Reliability between diagnostic raters was maintained throughout the project period.
HR subjects were classified as HR-neg (that is, negative for ASD) if they did notmeet either ASD or PDD-NOS criteria on the DSM-IV-TR. In order to have a LR
comparison group representing typically developing infants without autism, we
also assessed each LR subject at 24 months. The LR subjects included did not meet
ASD or PDD-NOS criteria on the DSM-IV-TR clinical best-estimate assessment
at 24 months. Three LR subjects met DSM-IV criteria for ASD at their 24-monthassessment (one for autism, two for PDD-NOS) and were excluded from the study(Extended Data Table 5). There is strong evidence of differences in the underlying
genetic architecture of multiple versus single incidence (or sporadic) cases, with thelatter more often being attributed to de novo events, that support our exclusion ofthese LR-ASD subjects from a combined analysis with the HR-ASD subject group,
who are siblings of HR infants. The final HR groups included 70 HR-ASD and
248 HR-neg children and the LR group consisted of 117 children.

MRI acquisition. The brain MRI scans were completed on 3T Siemens Tim Trioscanners with 12-channel head coils and obtained while infants were naturally
sleeping. The imaging protocol included (1) a localizer scan; (2) 3D T1 MPRAGE:
TR =​ 2,400 ms, TE =​ 3.16 ms, 160 sagittal slices, FOV =​ 256, voxel size =​ 1 mm3;(3) 3D T2 FSE TR =​ 3,200 ms, TE =​ 499 ms, 160 sagittal slices, FOV =​  256, voxel
size =​ 1 mm3; and (4) a 25 direction DTI: TR =​ 12,800 ms, TE =​ 102 ms, slice thickness =​ 2 mm isotropic, variable b value =​ maximum of 1,000 s mm−2, FOV =​  190.
A number of quality control procedures were employed to assess scanner stability and reliability across sites, time and procedures. Geometry phantoms were
scanned monthly and human phantoms (two adult subjects) were scanned annually to monitor scanner stability at each site across the study period. Details on
the stability procedures for IBIS and scanner quality control checks are described
elsewhere34.
We further examined the three subject groups (HR-ASD, HR-neg, LR) for any
differences in scan success rates (that is, proportion of completed scans). We founda significant difference between groups (χ 22 = 16.9, n =​  1,305, P =​  0.02). Overall,the HR-ASD subjects had proportionately fewer successful scans (69%) comparedto the HR-neg (78%) and LR (76%) groups, particularly at the 12-month visit. We
hypothesize that this may be owing to more behavioural difficulties in the ASD
group (for example, problems with sleep disturbance).
Radiologic review. All scans were reviewed locally by a paediatric neuroradiologist
for radiologic findings that, if present, were communicated to the participant.
In addition, a board certified paediatric neuroradiologist (R.C.M., Washington
University) blindly reviewed all MRI scans across the IBIS network and rated theincidental findings. A third neuroradiologist (D.W.S., University of Washington)
provided a second blind review for the Washington University site, and contributed
to a final consensus rating if there were discrepancies between the local site reviews
and the network review. The final consensus review was used to evaluate whether
there were group differences in the number and/or type of incidental findings.
Scans were rated as either normal, abnormal, or with incidental findings. No scans
rated as abnormal were included in the analysis, and previous examinations of ourdata did not find group differences in incidental findings34. Scans rated as clinicallyabnormal by a site paediatric neuroradiologist, and independently confirmed by
two study paediatric neuroradiologists, were excluded (n =​  3).
Image processing. Image processing was performed to obtain global brain
tissue volumes, regional brain tissue volumes and cortical measurements (surface
area, cortical thickness). All image processing was conducted blind to the subject
group and diagnostic information. The brain volumes were obtained using aframework of atlas-moderated expectation maximization including co-registrationof multi-modal (T1w/T2w) MRI, bias correction, brain stripping, noise reduction
and multivariate classification with the AutoSeg toolkit40 (http://www.nitrc.org/
projects/autoseg/). Population average templates and corresponding probabilistic
brain tissue priors, for white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) were constructed for the 6–24-month-old brain. The following
brain volumes were generated at all ages: intracranial volume (ICV), total brain
volume =​  GM +​WM, total cerebrospinal fluid (CSF), cerebrum, cerebellum,
and lateral ventricles. Intracranial volume was defined as the sum of WM, GM
and CSF. Total brain tissue volume (TBV) was defined as the sum of all WM and
GM contained in the brain cavity (that is, cerebrum, cerebellum and a portion of
midbrain/brainstem). Subjects were included in the volumetric analyses if they
had successfully segmented scans at 6, 12 and 24 months and corresponding bodylength measurements.
Cortical thickness and surface area measurements for 12 and 24 month data wereobtained via a CIVET workflow41,42 adapted for this age using an age-corrected
automated anatomical labelling (AAL) atlas43,44. CIVET includes shrink-wrap
deformable surface evolution of WM, local Laplacian distance and local surface
area, mapping to spherical domain, co-registration using cortical sulcal featuresand extraction of regional measurements through a deformably co-registeredfine-scale lobar parcellation. Surface area was measured at the mid-cortical surface.
Cortical thickness and surface area measurements for data of 6-month olds wereextracted from surfaces propagated via deformable multi-modal, within-subject,co-registration44 of MRI data at 12 months.
Statistical analysis. We used χ2 tests to examine group differences in the categorical demographic variables, including race, gender, social economic income
categories and mother’s education (see Extended Data Table 1). For the continuous
variables, including birth weight, mother’s age at child birth, children’s age at visits
and behavioural measurements, we used an ANOVA to test group differences. A
random-coefficient, piecewise longitudinal mixed model was used as a coherent
framework to model brain growth trajectories in the first and second year and totest for group differences in growth trajectories. The three outcome variables investigated were total brain volume (TBV), surface area and cortical thickness. Eachmodel included random coefficients for the first year growth rate (6–12 months)© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.

letter RESEARCH
and change of growth rate in the second year (12–24 months), and random intercepts for each child to account for individual differences and correlated repeatedmeasures collected at 6, 12 and 24 months. For subject i from group k at month j,
the brain measure is:

Yikj = (β 0k + b 0ik) + (β1k + b1ik)tikj + (β 2k + b 2ik)(tikj − 12) + eikj
The mean group growth rate in the first year will be β1k, and the growth rate forthe 12 months beyond will be β1k + β 2k. The inclusion of the change of slope after
12 months is to capture the change in growth rate from the first to the second year.
The first two years of life is a period of rapid brain development, with growth rate
being faster in the first than the second year45. The two-piece linear mixed model
was chosen to capture the change in growth rate from the first to the second year.
We required all subjects in this analysis to have 3 completed scans at 6, 12, and24 months. This reduced the HR-ASD sample from 70 to 15 subjects. This requirement is to ensure that we captured the individual growth rate change from the firstto the second year without the potential bias caused by partial visits and changes
in study cohorts at different visits. We examined possible bias in the HR-ASD
subjects with three completed visits versus those with only one or two visits andfound no significant differences in demographic (for example, sex, age) or outcome
measure (for example, TBV, surface area). Results are shown in Extended Data
Tables 6 and 7.
To model the unique brain overgrowth separate from the general body growth,
we modelled the brain growth relative to normative body growth in the first two
years45,46. Normative age based on body length was used instead of chronologicalage in order to capture brain overgrowth in the context of body growth. The normative age for each infant’s body size (tij, corrected age) was used in the model as
the continuous growth variable. The corrected age correlates highly with chronological age while taking into account the infant’s sex and body size, which isnecessary to determine the relative brain overgrowth. Sex-specific WHO heightnorms47 were used to determine the corrected age based on an infant’s sex andheight (length).
We addressed potential sex-related brain differences in two ways. First, in orderto account for sex-related body size differences and their effects on brain volume48,
we normalized differences in body size by using the sex-specific WHO heightnorms. Second, we included sex as a covariate in the analysis model to accountfor remaining sex-related differences49. The approach to include sex as a model
covariate will account for a linear, fixed effect of sex differences in brain volume.
However, for developmental studies, the sex differences in body size and brain
volume may be nonlinear, with an unknown function form. Using a body size
standardization based on normative sex-specific height data are more likely to
account for nonlinear sex-related differences.
The final model covariates include site and sex. Despite regular cross-site calibration in both behavioural and imaging protocols, a site covariate was included
to account for the possibility of cohort differences or potential administrative differences in a multi-site study. Sex was included as a covariate in the analysis modelto account for remaining sex-related differences not accounted for by sex-specificbody growth. However, when we analysed only males for group differences, ourresults remain unchanged (Extended Data Fig. 2 and Extended Data Table 8).
As a sensitivity analysis, we also tested the model with other demographic,familial and child birth-related covariates (race, social economic status, mother’s
education, mother’s age at birth, birth weight and gestational age), and only thesite and sex remained in the model with P <​  0.01.
The association of 24-month clinical outcome (ADOS, CSBS) with brain growth
rates (TBV) from 6–12 and 12–24 month intervals was assessed among HR subjects
using a Pearson correlation. Family income, mother’s education, subject sex andbirth weight were examined as potential covariates, but none contributed significantly and were excluded from the final analysis.
Multiple-comparison adjustments were performed for all pairwise comparisonsand the correlation analyses, which followed the tests for overall group differences(F test, reported in Extended Data Table 2). All pairwise comparisons and correlation analyses used adaptive Hochberg multiple-comparison adjustments50.
Only those comparisons that remained significant after the multiple comparison
adjustment are reported (Fig. 1 and Extended Data Table 2).
The machine learning analysis used a nonlinear prediction model based on astandard three-stage deep-learning network and included the following unbiased/
unweighted information: sex, age-corrected intracranial volume, and age-correctedsurface area and cortical thickness measurements from 39 left and 39 right cortical

hemisphere regions at 6 and 12 months (approximately 312 measurements). This
analysis included 34 HR-ASD and 145 HR-neg subjects. The model was evaluated via a standard tenfold cross-validation. The core of the prediction model
is a weighted three-stage neural/deep-learning network51, where the first stage
reduces 315 measurements to 100, the second stage reduces 100 to 10, and thethird stage reduces 10 to only 2 measurements. At each stage, the measurements(in the progressively smaller sets) are the weighted combination of input measurements from the previous stage. In general, the training process determines (1) those
network weights that retain information that are capable of distinguishing theaffected condition (for example, HR-ASD) from the unaffected condition(HR-neg), as well as (2) the linear support vector machine based classification decision that separates the group label (HR-ASD and HR-neg) in the two-dimensional
final network space. Thus to apply the prediction model, the data are first inserted
into the two-dimension final network space using the trained deep-learning network, and then classified in the final network space using the trained support
vector machine. All training was performed purely on the training data in eachfold. Once training was achieved, this prediction model was applied to the testingdata in each fold. Classification measurements of accuracy, sensitivity, specificity,
positive predictive value and negative predictive value are combined and reported
across the 10 folds. Details of our machine learning procedures and validity tests
are provided in the Supplementary Information.
Data availability. The raw data that support the findings from this study are publically available from the NIH National Database for Autism Research (NDAR). Any
additional data may be available from the corresponding author upon reasonable
request. Source data for Figs 1–3 are provided with the paper.
Information on the following tools used in our analyses (AutoSeg, HeadCircand ITK-SNAP) is freely available for download: http://www.med.unc.edu/psych/
research/niral/download/download-software and http://www.nitrc.org. All of the
Matlab source code used to construct, train and test the prediction pipeline is alsoavailable at https://github.com/munsellb/dl_autism.git.
34. Hazlett, H. C. et al. Brain volume findings in 6-month-old infants at high familial
risk for autism. Am. J. Psychiatry 169, 601–608 (2012).
35. Lord, C., Rutter, M. & Le Couteur, A. Autism Diagnostic Interview-Revised: arevised version of a diagnostic interview for caregivers of individuals withpossible pervasive developmental disorders. J. Autism Dev. Disord. 24,
659–685 (1994).
36. Mullen, E. M. Mullen Scales of Early Learning: AGS edn (American Guidance
Service, 1995).
37. Sparrow, S., Balla, D. & Cicchetti, D. Vineland Scales of Adaptive Behavior: A
Survey Form Manual. (American Guidance Service, 1984).
38. American Psychiatric Association. Diagnostic and Statistical Manual of Mental
Disorders (4th edn) (Washington, 2000).
39. Guthrie, W., Swineford, L. B., Nottke, C. & Wetherby, A. M. Early diagnosis ofautism spectrum disorder: stability and change in clinical diagnosis andsymptom presentation. J. Child Psychol. Psychiatry 54, 582–590 (2013).
40. Wang, J. et al. Multi-atlas segmentation of subcortical brain structures via the
AutoSeg software pipeline. Front. Neuroinform. 8, 7 (2014).
41. Shaw, P. et al. Development of cortical surface area and gyrification inattention-deficit/hyperactivity disorder. Biol. Psychiatry 72, 191–197 (2012).
42. Shaw, P. et al. Neurodevelopmental trajectories of the human cerebral cortex.
J. Neurosci. 28, 3586–3594 (2008).
43. Tzourio-Mazoyer, N. et al. Automated anatomical labeling of activations in SPM
using a macroscopic anatomical parcellation of the MNI MRI single-subject
brain. Neuroimage 15, 273–289 (2002).
44. Kim, S. H. et al. Adaptive prior probability and spatial temporal intensitychange estimation for segmentation of the one-year-old human brain.
J. Neurosci. Methods 212, 43–55 (2013).
45. Gilmore, J. H. et al. Longitudinal development of cortical and subcortical graymatter from birth to 2 years. Cereb. Cortex 22, 2478–2485 (2012).
46. Chawarska, K. et al. Early generalized overgrowth in boys with autism. Arch.
Gen. Psychiatry 68, 1021–1031 (2011).
47. WHO Multicentre Growth Reference Study Group. WHO Child GrowthStandards: Growth Velocity based on Weight, Length and Head Circumference:
Methods and Development. (WHO, 2009).
48. Im, K. et al. Brain size and cortical structure in the adult human brain. Cereb.
Cortex 18, 2181–2191 (2008).
49. Gilmore, J. H. et al. Regional gray matter growth, sexual dimorphism, andcerebral asymmetry in the neonatal brain. J. Neurosci. 27, 1255–1260(2007).
50. Benjamini, Y., Krieger, A. M. & Yekutieli, D. Adaptive linear step-up proceduresthat control the false discovery rate. Biometricka 93, 491–507 (2006).
51. Hinton, G. E. & Salakhutdinov, R. R. Reducing the dimensionality of data withneural networks. Science 313, 504–507 (2006).

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RESEARCH letter

Extended Data Figure 1 | Visualization of cortical regions with surface area measurements among the top 40 features contributing to the linear sparselearning classification. The cortical features produced by the deep learning approach (Fig. 3) are highly consistent with those observed using an alternative
approach (linear sparse learning) shown here. Results from this alternative approach are included for comparison in Supplementary Tables 2 and 3.

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TBV - Males

TBV – Females

letter RESEARCH

Corrected age (months)
Extended Data Figure 2 | Trajectories of TBV for males (left) andfemales (right). For illustrative purposes, we provide plots for TBV formales and females from the same sample. The longitudinal trajectories
of total brain volume (TBV) from 6 to 24 months for the three groups
examined are shown with males and females displayed separately. The
trajectory of TBV for males among the three groups is similar to theCorrected age (months)
pattern we see in the full sample (Fig. 1). The female HR-ASD group isquite small (n =​ 2), which makes the pattern of trajectory difficult tointerpret. These figures support the general similarity of the findings in
the combined sample and the male-only sample. Red, HR-ASD; green,
HR-neg; blue, LR. TBV is shown in mm3. The age (in months) is correctedby length (body size, in cm).

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RESEARCH letter
Extended Data Table 1 | Subject demographics (including tests for group differences)

No significant group differences (between HR-ASD, HR-neg and LR) were observed in race/ethnicity, family income, maternal age at birth, infant birth weight, gestational age at birth, or age at visit. As. We also observed that the
expected, on the basis of the disproportionately higher rates of ASD in males, the HR-ASD group contained significantly more males than the LR group (χ2
2 = 15.7, P < 0.01)

LR group had higher maternal education compared to the other two groups (χ2. As expected, on the basis of the association between intellectual disability and ASD, the HR-ASD group2 = 36.4, P < 0.01)
had significantly lower Mullen and Vineland scores at 24 months than the other two groups. Mullen ELC, Early Learning Composite standard score; Vineland ABC, Vineland Adaptive Behaviour
Composite standard score; SD, standard deviation.

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letter RESEARCH
Extended Data Table 2 | Group differences in developmental trajectories and cross-sectional volumes by age

Brain volume measurements for trajectory and cross-sectional analyses are in mm3, surface area measurements are in mm2 and cortical thickness measurements are in mm. The slope is presented aschange/months. Adjusted group mean is the model estimated group mean at the specified time point. Year 1, 6–12 month period; year 2, 12–24 month period; pairwise group comparison (P <​  0.05);
the sample for the piecewise linear model included subjects with complete data at all three visits (6, 12 and 24 months).

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RESEARCH letter
Extended Data Table 3 | Raw means and standard deviations for TBV and surface area group comparisons showing effect size andconfidence intervals

Brain volume measurements are in mm3, surface area measurements are in mm2. Visit age is shown in months. Raw mean is the group mean.

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letter RESEARCH
Extended Data Table 4 | Prediction model using cortical data to classify groups at 24 months

A nonlinear prediction model included the following unbiased/unweighted information: sex, age-corrected intracranial volume and age-corrected surface area and cortical thickness measurementsfrom 39 left and 39 right cortical hemisphere regions at 6 months and 12 months. The prediction model was evaluated using a standard tenfold cross-validation approach. Classification performanceof the prediction model is at 94% overall accuracy, 88% sensitivity, 95% specificity, 81% positive predictive value and 97% negative prediction value.
TP, true positive; FP, false positive; PPV, positive predictive value; NPV, negative predictive value; diagnosis, outcome on the basis of DSM-IV-TR criteria38.

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RESEARCH letter
Extended Data Table 5 | Clinical characteristics for LR subjects who met ASD criteria at 24 months

All data presented is for the visit at 24 months old. DSM, DSM-IV diagnostic criteria; PDD, pervasive developmental disorder, not otherwise specified; AUT, autism; Mullen ELC, Mullen Early Learning
Composite standard score; Vineland ABC, Vineland Adaptive Behaviour Composite standard score, ADOS SA, ADOS social affective total score; ADOS RBx, ADOS repetitive behaviour total; ADOS Sev,
ADOS severity score; SA, surface area; NA, no MRI data at 24 months.

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letter RESEARCH
Extended Data Table 6 | Subject demographics (including tests for group differences) for subjects with all 3 longitudinal visits and thosewith 1–2 visits completed© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.

RESEARCH letter
Extended Data Table 7 | Group differences in developmental level, TBV and surface area

We further tested whether there were any group differences in developmental function (Mullen) and TBV and surface area. Groups did not differ in developmental function at any visit age, indicatingthat the subjects in the 3-visit and 1–2-visit subgroups are similar in their developmental capabilities. No group differences were observed for either TBV or surface area at any visit age, suggestinggroups appear to have similar profiles for the brain measurements. Age is the visit age (in months), brain volume measurements are in mm3 and surface area measurements are mm2.

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letter RESEARCH
Extended Data Table 8 | Group differences in developmental trajectories and cross-sectional volumes by age for males

The primary analysis of brain volume trajectories included only those male and female study participants with three completed visits (6, 12 and 24 months), to best depict longitudinal trajectories
over time. Separate analyses on males and females are likely to be inadequately powered owing to small subsample size (males =​  13, females =​ 2) and therefore provide inconclusive results. With thatcaveat, we provide the results of our male-only analysis for the three groups for total brain volume. We do not see any group differences in the first year (6–12 months). The HR-ASD males show a pattern of TBV brain enlargement by the end of the second year, compared to the LR and HR-neg groups. Brain volume measurements for trajectory and cross-sectional analyses are in mm3, surface area
measurements are in mm2 and cortical thickness measurements are in mm. Slope is presented as change/months. The sample for the piecewise linear model included subjects with complete data at
all three visits (6, 12 and 24 months). 1st year, 6–12 month period; 2nd year, 12–24 month period; LSM, least square means; pairwise group comparison (P <​ 0.05); SE, standard error.

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