Abstract

Brain enlargement has been observed in children with autism spectrum disorder (ASD), but the timing of this phenomenon, and the relationship between ASD and the appearance of behavioural symptoms, are unknown. Retrospective head circumference and longitudinal 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 the second 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 were diagnosed 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 in which autistic behaviours are first emerging.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

    et al. Magnetic resonance imaging and head circumference study of brain size in autism: birth through age 2 years. Arch. Gen. Psychiatry 62, 1366–1376 (2005)

  2. 2.

    et al. Early brain overgrowth in autism associated with an increase in cortical surface area before age 2 years. Arch. Gen. Psychiatry 68, 467–476 (2011)

  3. 3.

    et al. A prospective study of the emergence of early behavioral signs of autism. J. Am. Acad. Child Adolesc. Psychiatry 49, 256–266.e1, 2 (2010)

  4. 4.

    et al. Behavioral manifestations of autism in the first year of life. Int. J. Dev. Neurosci. 23, 143–152 (2005)

  5. 5.

    et al. An MRI study of brain size in autism. Am. J. Psychiatry 152, 1145–1149 (1995)

  6. 6.

    et al. Unusual brain growth patterns in early life in patients with autistic disorder: an MRI study. Neurology 57, 245–254 (2001)

  7. 7.

    et al. Longitudinal magnetic resonance imaging study of cortical development through early childhood in autism. J. Neurosci. 30, 4419–4427 (2010)

  8. 8.

    et al. Brain structural abnormalities in young children with autism spectrum disorder. Neurology 59, 184–192 (2002)

  9. 9.

    et al. Early brain enlargement and elevated extra-axial fluid in infants who develop autism spectrum disorder. Brain 136, 2825–2835 (2013)

  10. 10.

    , , & The Autism Diagnostic Observation Schedule. (Western Psychological Services, 2000)

  11. 11.

    & Communication and Symbolic Behavior Scales Developmental Profile, First Normed Edition. (Paul H. Brookes, 2002)

  12. 12.

    et al. Mapping longitudinal hemispheric structural asymmetries of the human cerebral cortex from birth to 2 years of age. Cereb. Cortex 24, 1289–1300 (2014)

  13. 13.

    et al. White matter microstructure and atypical visual orienting in 7-month-olds at risk for autism. Am. J. Psychiatry 170, 899–908 (2013)

  14. 14.

    et al. Behavioral, cognitive, and adaptive development in infants with autism spectrum disorder in the first 2 years of life. J. Neurodev. Disord. 7, 24 (2015)

  15. 15.

    et al. Opposing brain differences in 16p11.2 deletion and duplication carriers. J. Neurosci. 34, 11199–11211 (2014)

  16. 16.

    et al. Disruptive CHD8 mutations define a subtype of autism early in development. Cell 158, 263–276 (2014)

  17. 17.

    et al. Distinct genetic influences on cortical surface area and cortical thickness. Cereb. Cortex 19, 2728–2735 (2009)

  18. 18.

    A small step for the cell, a giant leap for mankind: a hypothesis of neocortical expansion during evolution. Trends Neurosci. 18, 383–388 (1995)

  19. 19.

    & Regulation of cerebral cortical size by control of cell cycle exit in neural precursors. Science 297, 365–369 (2002)

  20. 20.

    et al. Minicolumnar abnormalities in autism. Acta Neuropathol. 112, 287–303 (2006)

  21. 21.

    et al. Similar patterns of cortical expansion during human development and evolution. Proc. Natl Acad. Sci. USA 107, 13135–13140 (2010)

  22. 22.

    Neocortical neurogenesis and the etiology of autism spectrum disorder. Neurosci. Biobehav. Rev. 64, 185–195 (2016)

  23. 23.

    et al. Overproduction of upper-layer neurons in the neocortex leads to autism-like features in mice. Cell Reports 9, 1635–1643 (2014)

  24. 24.

    et al. The 16p11.2 deletion mouse model of autism exhibits altered cortical progenitor proliferation and brain cytoarchitecture linked to the ERK MAPK pathway. J. Neurosci. 35, 3190–3200 (2015)

  25. 25.

    et al. Regulation of cerebral cortex size and folding by expansion of basal progenitors. EMBO J. 32, 1817–1828 (2013)

  26. 26.

    et al. CHD8 regulates neurodevelopmental pathways associated with autism spectrum disorder in neural progenitors. Proc. Natl Acad. Sci. USA 111, E4468–E4477 (2014)

  27. 27.

    et al. The autism-associated chromatin modifier CHD8 regulates other autism risk genes during human neurodevelopment. Nat. Commun. 6, 6404 (2015)

  28. 28.

    et al. Altered proliferation and networks in neural cells derived from idiopathic autistic individuals. Mol. Psychiatry (2016)

  29. 29.

    Early identification and intervention in autism spectrum disorders: some progress but not as much as we hoped. Int. J. Speech Lang Pathol. 16, 15–18 (2014)

  30. 30.

    , , & Developmental trajectories in children with and without autism spectrum disorders: the first 3 years. Child Dev. 84, 429–442 (2013)

  31. 31.

    et al. Beyond autism: a baby siblings research consortium study of high-risk children at three years of age. J. Am. Acad. Child Adolesc. Psychiatry 52, 300–308.e1 (2013)

  32. 32.

    et al. A prospective study of autistic-like traits in unaffected siblings of probands with autism spectrum disorder. JAMA Psychiatry 70, 42–48 (2013)

  33. 33.

    et al. The broader autism phenotype in infancy: when does it emerge? J. Am. Acad. Child Adolesc. Psychiatry 53, 398–407.e2 (2014)

  34. 34.

    et al. Brain volume findings in 6-month-old infants at high familial risk for autism. Am. J. Psychiatry 169, 601–608 (2012)

  35. 35.

    , & Autism Diagnostic Interview-Revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. J. Autism Dev. Disord. 24, 659–685 (1994)

  36. 36.

    Mullen Scales of Early Learning: AGS edn (American Guidance Service, 1995)

  37. 37.

    , & Vineland Scales of Adaptive Behavior: A Survey Form Manual. (American Guidance Service, 1984)

  38. 38.

    American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (4th edn) (Washington, 2000)

  39. 39.

    , , & Early diagnosis of autism spectrum disorder: stability and change in clinical diagnosis and symptom presentation. J. Child Psychol. Psychiatry 54, 582–590 (2013)

  40. 40.

    et al. Multi-atlas segmentation of subcortical brain structures via the AutoSeg software pipeline. Front. Neuroinform. 8, 7 (2014)

  41. 41.

    et al. Development of cortical surface area and gyrification in attention-deficit/hyperactivity disorder. Biol. Psychiatry 72, 191–197 (2012)

  42. 42.

    et al. Neurodevelopmental trajectories of the human cerebral cortex. J. Neurosci. 28, 3586–3594 (2008)

  43. 43.

    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. 44.

    et al. Adaptive prior probability and spatial temporal intensity change estimation for segmentation of the one-year-old human brain. J. Neurosci. Methods 212, 43–55 (2013)

  45. 45.

    et al. Longitudinal development of cortical and subcortical gray matter from birth to 2 years. Cereb. Cortex 22, 2478–2485 (2012)

  46. 46.

    et al. Early generalized overgrowth in boys with autism. Arch. Gen. Psychiatry 68, 1021–1031 (2011)

  47. 47.

    WHO Multicentre Growth Reference Study Group. WHO Child Growth Standards: Growth Velocity based on Weight, Length and Head Circumference: Methods and Development. (WHO, 2009)

  48. 48.

    et al. Brain size and cortical structure in the adult human brain. Cereb. Cortex 18, 2181–2191 (2008)

  49. 49.

    et al. Regional gray matter growth, sexual dimorphism, and cerebral asymmetry in the neonatal brain. J. Neurosci. 27, 1255–1260 (2007)

  50. 50.

    , & Adaptive linear step-up procedures that control the false discovery rate. Biometricka 93, 491–507 (2006)

  51. 51.

    & Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006)

Download references

Acknowledgements

The IBIS (Infant Brain Imaging Study) Network is an NIH funded Autism Center of Excellence (HDO55741) and consists of a consortium of 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 and Participant 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 and effort 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 information

Affiliations

  1. Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina 27599, USA

    • Heather Cody Hazlett
    • , Hongbin Gu
    • , Sun Hyung Kim
    • , Martin Styner
    •  & Joseph Piven
  2. Carolina Institute for Developmental Disabilities, Chapel Hill, North Carolina 27599, USA

    • Heather Cody Hazlett
    • , Meghan R. Swanson
    •  & Joseph Piven
  3. College of Charleston, Charleston, South Carolina 29424, USA

    • Brent C. Munsell
  4. Department of Educational Psychology, University of Minnesota, Minneapolis, Minnesota 55455, USA

    • Jason J. Wolff
  5. Institute of Child Development, University of Minnesota, Minneapolis, Minnesota 55455, USA

    • Jed T. Elison
  6. Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina 27599, USA

    • Hongtu Zhu
  7. Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri 63110, USA

    • Kelly N. Botteron
    • , John N. Constantino
    •  & John R. Pruett
  8. Department of Radiology, University of Washington, Seattle, Washington 98105, USA

    • Stephen R. Dager
    •  & Dennis W. Shaw
  9. Center on Human Development and Disability, University of Washington, Seattle, Washington 98105, USA

    • Stephen R. Dager
    • , Annette M. Estes
    •  & Dennis W. Shaw
  10. Department of Speech and Hearing Sciences, University of Washington, Seattle, Washington 98105, USA

    • Annette M. Estes
  11. Montreal Neurological Institute, McGill University, Montreal, Quebec H3A 0G4, Canada

    • D. Louis Collins
    • , Alan C. Evans
    • , Vladimir S. Fonov
    •  & Penelope Kostopoulos
  12. Tandon School of Engineering, New York University, New York, New York 10003, USA

    • Guido Gerig
  13. Mallinckrodt Institute of Radiology, Washington University, St. Louis, Missouri 63110, USA

    • Robert C. McKinstry
  14. Center for Autism Research, The Children’s Hospital of Philadelphia and University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA

    • Juhi Pandey
    •  & Robert T. Schultz
  15. Department of Psychology, Temple University, Philadelphia, Pennsylvania 19122, USA

    • Sarah Paterson
  16. Department of Pediatrics, University of Alberta, Edmonton, Alberta T6G 2R3, Canada

    • Lonnie Zwaigenbaum
  17. University of North Carolina, Chapel Hill, North Carolina 27599, USA.

    • J. Piven
    • , H. C. Hazlett
    • , C. Chappell
    • , M. Styner
    •  & Core H. Gu
  18. University of Washington, Seattle, Washington 98105, USA.

    • S. R. Dager
    • , A. M. Estes
    •  & D. W. Shaw
  19. Washington University, St. Louis, Missouri 63130, USA.

    • K. N. Botteron
    • , R. C. McKinstry
    • , J. N. Constantino
    •  & J. R. Pruett Jr
  20. Children's Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, USA.

    • R. T. Schultz
    •  & S. Paterson
  21. University of Alberta, Edmonton, Alberta T6G 2R3, Canada.

    • L. Zwaigenbaum
  22. University of Minnesota, Minneapolis, Minnesota 55455, USA.

    • J. T. Elison
    •  & J. J. Wolff
  23. Montreal Neurological Institute, Montreal, Quebec H3A 0G4, Canada.

    • A. C. Evans
    • , D. L. Collins
    • , G. B. Pike
    • , V. S. Fonov
    • , P. Kostopoulos
    •  & S. Das
  24. New York University, New York, New York 10003, USA.

    • G. Gerig

Consortia

  1. The IBIS Network

    Clinical Sites

    Data Coordinating Center

    Image Processing Core

    Statistical Analysis

Authors

  1. Search for Heather Cody Hazlett in:

  2. Search for Hongbin Gu in:

  3. Search for Brent C. Munsell in:

  4. Search for Sun Hyung Kim in:

  5. Search for Martin Styner in:

  6. Search for Jason J. Wolff in:

  7. Search for Jed T. Elison in:

  8. Search for Meghan R. Swanson in:

  9. Search for Hongtu Zhu in:

  10. Search for Kelly N. Botteron in:

  11. Search for D. Louis Collins in:

  12. Search for John N. Constantino in:

  13. Search for Stephen R. Dager in:

  14. Search for Annette M. Estes in:

  15. Search for Alan C. Evans in:

  16. Search for Vladimir S. Fonov in:

  17. Search for Guido Gerig in:

  18. Search for Penelope Kostopoulos in:

  19. Search for Robert C. McKinstry in:

  20. Search for Juhi Pandey in:

  21. Search for Sarah Paterson in:

  22. Search for John R. Pruett in:

  23. Search for Robert T. Schultz in:

  24. Search for Dennis W. Shaw in:

  25. Search for Lonnie Zwaigenbaum in:

  26. Search for Joseph Piven in:

Contributions

All co-authors discussed the results, made critical contributions 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 manuscript with 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.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Heather Cody Hazlett.

Reviewer Information Nature thanks M. Johnson, G. Ramsay, T. Yarkoni and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Extended data

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    This file contains Supplementary Text and Data, Supplementary Figures 1-10, Supplementary Tables 1-3 and Supplementary References.

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nature21369

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.