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

By Heather Cody Hazlett, H. Gu, Brent C. Munsell, S. H. Kim, Martin Andreas Styner, J. Wolff, J. Elison, M. Swanson, Hongtu Zhu, K. Botteron and 16 more. Nature, 2017

998 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/77ab4ca4c04d451cf9204954b03525ef3c3d8f5f

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In Cognitive Neuroscience

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In Molecular Biology

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Citations in the corpus

• The UNC/UMN Baby Connectome Project (BCP): An Overview of the Study Design and Protocol Development (2018, positive): “Data is then rigidly aligned into ICBM space via a 1–2 year old pediatric template (Hazlett et al., 2017), followed by N4 based intensity inhomogeneity correction (Tustison et al.”
• Multi-Site Diagnostic Classification of Schizophrenia Using Discriminant Deep Learning with Functional Connectivity MRI (2018, background): “…fcMRI classi fi cation of schizophrenia patients vs. healthy controls with a small sample size ( n = 100) (Kim et al., 2016), illuminating the potential of deep learning in automatic diagnosis of clinical populations(Hazlett et al., 2017; Kawahara et al., 2017; Suk et al., 2013; Zhao et al., 2017).”
• Autism spectrum disorder: definition, epidemiology, causes, and clinical evaluation (2020, background): “Brain overgrowth both in terms of cortical size and additionally in terms of increased extra-axial fluid have been described in children with ASD and are areas of ongoing study both in terms of furthering our understanding of its etiology, but also as a potential biomarker (40,41).”
• Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning (2019, background): “In contrast, advanced artificial intelligence models can overcome these problems through a self-learning strategy such as deep learning methods [20 – 22].”
• Considerations for conducting developmental neuroimaging research on mental health topics (2025, unknown): “For example, in autism research, there is evidence that brains may show differences pre-symptomatically at early ages, suggesting that such differences presage measurable differences in behavior at later ages [106, 107].”