Abstract
Autism spectrum disorder (ASD) has its origins in the atypical development of brain networks. Infants who are at high familial risk for, and later diagnosed with ASD, show atypical activity in multiple electroencephalography (EEG) oscillatory measures. However, infant‐sibling studies are often constrained by small sample sizes. We used the International Infant EEG Data Integration Platform, a multi‐site dataset with 432 participants, including 222 at high‐risk for ASD, from whom repeated measurements of EEG were collected between the ages of 3–36 months. We applied a latent growth curve model to test whether familial risk status predicts developmental trajectories of spectral power across the first 3 years of life, and whether these trajectories predict ASD outcome. Change in spectral EEG power in all frequency bands occurred during the first 3 years of life. Familial risk, but not a later diagnosis of ASD, was associated with reduced power at 3 months, and a steeper developmental change between 3 and 36 months in nearly all absolute power bands. ASD outcome was not associated with absolute power intercept or slope. No associations were found between risk or outcome and relative power. This study applied an analytic approach not used in previous prospective biomarker studies of ASD, which was modeled to reflect the temporal relationship between genetic susceptibility, brain development, and ASD diagnosis. Trajectories of spectral power appear to be predicted by familial risk; however, spectral power does not predict diagnostic outcome above and beyond familial risk status. Discrepancies between current results and previous studies are discussed.Lay SummaryInfants with an older sibling who is diagnosed with ASD are at increased risk of developing ASD themselves. This article tested whether EEG spectral power in the first year of life can predict whether these infants did or did not develop ASD.
Highlights
Recent evidence suggests that autism spectrum disorder (ASD) has its origins in the atypical development of brain networks (O’Reilly et al, 2017; Wang et al, 2013)
Wileyonlinelibrary.com/journal/aur pathways and neuronal connections (Parikshak et al, 2013). These findings have been supported by studies of infants at high familial risk for ASD, which report brain overgrowth and atypical development of white matter pathways in the first year of life in infants who are later diagnosed with ASD (Hazlett et al, 2017; Wolff et al, 2012)
One longitudinal study of spectral power reported that infants at high-risk (HR) of developing ASD (HR; infants who have an older sibling with ASD) exhibit lower EEG spectral power in several frequency bands at 6 months compared to those at low-risk (LR; infants who have no siblings with an existing ASD diagnosis), and the two groups showed different trajectories over the first 2 years of life in several frequency bands (Tierney et al, 2012)
Summary
Recent evidence suggests that autism spectrum disorder (ASD) has its origins in the atypical development of brain networks (O’Reilly et al, 2017; Wang et al, 2013). Wileyonlinelibrary.com/journal/aur pathways and neuronal connections (Parikshak et al, 2013) These findings have been supported by studies of infants at high familial risk for ASD (by virtue of having an older sibling with ASD), which report brain overgrowth and atypical development of white matter pathways in the first year of life in infants who are later diagnosed with ASD (Hazlett et al, 2017; Wolff et al, 2012). There is evidence of early atypical spectral activity in ASD, as studies that use electroencephalography (EEG) to measure neural functioning have found reduced functional connectivity in gamma band oscillations, and greater functional connectivity in alpha band oscillations in infant-siblings who later develop ASD (Orekhova et al, 2014; Righi et al, 2014) This early atypical connectivity may be due to differences in rates of axonal remodeling, leading to brain overgrowth and weakened long distance connections (O’Reilly et al, 2017). Given that spectral power is associated with attention in infants and children (Orekhova et al, 2006), and is associated with language ability in the first years of life (Benasich et al, 2008; Levin et al, 2017, Wilkinson et al, 2019), understanding how spectral power relates to ASD risk and diagnosis will provide a better understanding of the developmental outcomes in this population
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