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

Journal name:
Nature
Volume:
542,
Pages:
348–351
Date published:
DOI:
doi:10.1038/nature21369
Received
Accepted
Published online

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.

At a glance

Figures

  1. Longitudinal trajectories of TBV, surface area and cortical thickness from 6 to 24 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 differences in 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 differences in trajectories for cortical thickness, with all groups showing a pattern of a decrease in cortical thickness over time. No group differences were observed 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).

  2. Cortical regions that show significant expansion in surface area from 6 to 12 months in HR-ASD.
    Figure 2: Cortical regions that show significant expansion in surface area from 6 to 12 months in HR-ASD.

    A map of significant group differences 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 method of 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).

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

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

  5. Trajectories of TBV for males (left) and females (right).
    Extended Data Fig. 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).

Tables

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

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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

  2. 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
  3. Data Coordinating Center

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

    • G. Gerig &
    • M. Styner
  5. 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.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to:

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.

Author details

Extended data figures and tables

Extended Data Figures

  1. Extended Data Figure 1: Visualization of cortical regions with surface area measurements among the top 40 features contributing to the linear sparse learning classification. (508 KB)

    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.

  2. Extended Data Figure 2: Trajectories of TBV for males (left) and females (right). (169 KB)

    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 Tables

  1. Extended Data Table 1: Subject demographics (including tests for group differences) (149 KB)
  2. Extended Data Table 2: Group differences in developmental trajectories and cross-sectional volumes by age (207 KB)
  3. Extended Data Table 3: Raw means and standard deviations for TBV and surface area group comparisons showing effect size and confidence intervals (299 KB)
  4. Extended Data Table 4: Prediction model using cortical data to classify groups at 24 months (95 KB)
  5. Extended Data Table 5: Clinical characteristics for LR subjects who met ASD criteria at 24 months (45 KB)
  6. 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 (199 KB)
  7. Extended Data Table 7: Group differences in developmental level, TBV and surface area (103 KB)
  8. Extended Data Table 8: Group differences in developmental trajectories and cross-sectional volumes by age for males (193 KB)

Supplementary information

PDF files

  1. Supplementary Information (1.5 MB)

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

Additional data