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 development. 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 life. 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.
Bibliographical noteFunding Information:
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.
© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.