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Early brain development in infants at high risk for autism spectrum disorder

Nature volume 542, pages 348–351 (2017)Cite this article

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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.

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Figure 1: Longitudinal trajectories of TBV, surface area and cortical thickness from 6 to 24 months.
Figure 2: Cortical regions that show significant expansion in surface area from 6 to 12 months in HR-ASD.
Figure 3: Visualization of cortical regions with surface area measurements among the top 40 features contributing to the reduction in deep learning dimensionality.

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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

Authors and Affiliations

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

    Heather Cody Hazlett, Hongbin Gu, Sun Hyung Kim, Martin Styner & Joseph Piven

  2. Carolina Institute for Developmental Disabilities, Chapel Hill, 27599, North Carolina, USA

    Heather Cody Hazlett, Meghan R. Swanson & Joseph Piven

  3. College of Charleston, Charleston, 29424, South Carolina, USA

    Brent C. Munsell

  4. Department of Educational Psychology, University of Minnesota, Minneapolis, 55455, Minnesota, USA

    Jason J. Wolff

  5. Institute of Child Development, University of Minnesota, Minneapolis, 55455, Minnesota, USA

    Jed T. Elison

  6. Department of Biostatistics, University of North Carolina, Chapel Hill, 27599, North Carolina, USA

    Hongtu Zhu

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

    Kelly N. Botteron, John N. Constantino & John R. Pruett

  8. Department of Radiology, University of Washington, Seattle, 98105, Washington, USA

    Stephen R. Dager & Dennis W. Shaw

  9. Center on Human Development and Disability, University of Washington, Seattle, 98105, Washington, USA

    Stephen R. Dager, Annette M. Estes & Dennis W. Shaw

  10. Department of Speech and Hearing Sciences, University of Washington, Seattle, 98105, Washington, USA

    Annette M. Estes

  11. Montreal Neurological Institute, McGill University, Montreal, H3A 0G4, Quebec, Canada

    D. Louis Collins, Alan C. Evans, Vladimir S. Fonov & Penelope Kostopoulos

  12. Tandon School of Engineering, New York University, New York, 10003, New York, USA

    Guido Gerig

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

    Robert C. McKinstry

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

    Juhi Pandey & Robert T. Schultz

  15. Department of Psychology, Temple University, Philadelphia, 19122, Pennsylvania, USA

    Sarah Paterson

  16. Department of Pediatrics, University of Alberta, Edmonton, T6G 2R3, Alberta, 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

Authors
  1. Heather Cody Hazlett
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  2. Hongbin Gu
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  4. Sun Hyung Kim
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Consortia

The IBIS Network

  • Clinical Sites

    • J. Piven
    • , H. C. Hazlett
    • , C. Chappell
    • , S. R. Dager
    • , A. M. Estes
    • , D. W. Shaw
    • , K. N. Botteron
    • , R. C. McKinstry
    • , J. N. Constantino
    • , J. R. Pruett Jr
    • , R. T. Schultz
    • , S. Paterson
    • , L. Zwaigenbaum
    • , J. T. Elison
    •  & J. J. Wolff

  • Data Coordinating Center

    • A. C. Evans
    • , D. L. Collins
    • , G. B. Pike
    • , V. S. Fonov
    • , P. Kostopoulos
    •  & S. Das

  • Image Processing Core

    • G. Gerig
    •  & M. Styner

  • Statistical Analysis

    • Core H. Gu

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.

Corresponding author

Correspondence to Heather Cody Hazlett.

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Competing interests

The authors declare no competing financial interests.

Additional information

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 figures and tables

Extended Data Figure 1 Visualization of cortical regions with surface area measurements among the top 40 features contributing to the linear sparse learning 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.

Extended Data Figure 2 Trajectories of TBV for males (left) and females (right).

For illustrative purposes, we provide plots for TBV for males 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 the pattern we see in the full sample (Fig. 1). The female HR-ASD group is quite small (n = 2), which makes the pattern of trajectory difficult to interpret. 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 corrected by length (body size, in cm).

Extended Data Table 1 Subject demographics (including tests for group differences)
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Extended Data Table 2 Group differences in developmental trajectories and cross-sectional volumes by age
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Extended Data Table 3 Raw means and standard deviations for TBV and surface area group comparisons showing effect size and confidence intervals
Full size table
Extended Data Table 4 Prediction model using cortical data to classify groups at 24 months
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Extended Data Table 5 Clinical characteristics for LR subjects who met ASD criteria at 24 months
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Extended Data Table 6 Subject demographics (including tests for group differences) for subjects with all 3 longitudinal visits and those with 1–2 visits completed
Full size table
Extended Data Table 7 Group differences in developmental level, TBV and surface area
Full size table
Extended Data Table 8 Group differences in developmental trajectories and cross-sectional volumes by age for males
Full size table

Supplementary information

Supplementary Information

This file contains Supplementary Text and Data, Supplementary Figures 1-10, Supplementary Tables 1-3 and Supplementary References. (PDF 1540 kb)

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Hazlett, H., Gu, H., Munsell, B. et al. Early brain development in infants at high risk for autism spectrum disorder. Nature 542, 348–351 (2017). https://doi.org/10.1038/nature21369

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