Electroencephalography and eye-tracking measures as scalable biomarker-based predictors of ASD in high-risk infants

  • Awarded: 2019
  • Award Type: Research
  • Award #: 677835

Despite tremendous efforts by parents, researchers, clinicians and educators, autism spectrum disorder (ASD) continues to present a significant lifelong challenge to most affected individuals and their families. Studies of early development in infants at risk for ASD (such as infants with older siblings with ASD: high-risk [HR] infant siblings — who have an approximately 20 percent chance of developing ASD1) can identify early presymptomatic predictors of ASD that can then improve early screening and promote presymptomatic intervention. Behavioral studies of these HR infant siblings have identified atypical behaviors in the second year of life in the social domain, with some evidence of motor delays and differences in social attention within the first year. However, in part because of the limited behavioral repertoire of infants, investigators have struggled to identify consistent first-year-of-life behaviors that predict later ASD in a clinically actionable manner.

Shafali Spurling Jeste and her colleagues propose that the earliest measures of atypical development should come from direct assays of brain function. In support of this view, the Infant Brain Imaging Study (IBIS) Network (PIs: John Pruett and Joseph Piven; site PIs: Kelly Botteron, Stephen Dager, Jed Elison, Alan Evans and Robert Schultz) has used magnetic resonance imaging (MRI) methods to reveal functional and structural brain changes in the first year of life in HR infant siblings. These brain changes are present prior to the emergence of behavioral features of ASD and accurately predict ASD at 24 months of age (positive predictive value ≥ 80 percent)2. While scientifically promising, the cost and reduced availability of MRIs limit their potential scalability for use in HR infants to use as a general population screener in clinical settings. Conversely, electroencephalography (EEG) and eye tracking (ET) represent two scalable methods for measuring brain function. As EEG and ET are developmentally sensitive and accessible in real-world community settings, Jeste and colleagues argue they could be used to identify predictive biomarkers of ASD in early infancy.

The IBIS Network launched a new study in early 2019 (funded by the National Institutes of Health) of 250 HR infants recruited from five sites (Children’s Hospital of Philadelphia, University of Minnesota, the University of North Carolina, University of Washington and Washington University in St. Louis) that is designed to replicate and extend its predictive MRI findings. In the current project, Jeste and colleagues plan to add EEG and ET measures of brain function to this same cohort, testing HR infants at 6 and 12 months of age, and assessing clinical outcomes at 24 months of age. They will examine brain network function at rest, during low level sensory processing and during social information processing. Their hypothesis is that these scalable EEG/ET biomarkers will (aim 1) accurately identify infants with a later diagnosis of ASD and will (aim 2) relate to ASD-associated behaviors at 24 months of age. Capitalizing on this unprecedented opportunity to integrate EEG/ET with neuroimaging in the same cohort of infants, the team also proposes to (aim 3) explore the predictive power of these combined measures and the association between EEG/ET and MRI features.

The overarching goal of this project is to lower the age of detection of ASD to early infancy, making intervention before the emergence of ASD-specific behavioral features feasible and more effective. Positive findings in the planned study will also facilitate the future extension of presymptomatic predictive testing from HR infants to infants in the general population.

References

1.Ozonoff S. et al. Pediatrics 128, e488-e495 (2011) PubMed
2.Hazlett H.C. et al. Nature 542, 348-351 (2017) PubMed
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