Association between spectral electroencephalography power and autism risk and diagnosis in early development.
EEG power trajectories in infancy mirror familial autism risk yet fail to improve early diagnosis beyond knowing the family history.
01Research in Context
What this study did
Huberty et al. (2021) tracked EEG power changes in babies who had a big brother or sister with autism. They wanted to know if the speed of power change, or the power itself, could flag later autism.
Researchers recorded brain waves several times from infancy to toddlerhood. They compared kids who later got an ASD diagnosis with those who did not.
What they found
Steeper EEG power slopes across infancy matched family autism risk, but they did not boost prediction beyond knowing the family history alone.
In plain words: the brain-wave trend echoed genetic risk, yet it gave no extra diagnostic punch.
How this fits with other research
Mulder et al. (2020) saw the opposite. In toddlers with tuberous sclerosis, higher alpha power during sleep at 24 months did predict later ASD symptoms. The clash is useful: their sample had a specific genetic syndrome and used sleep EEG, while Scott’s used broad familial risk and waking EEG. Method makes the difference.
Cornew et al. (2012) earlier found kids with ASD already showed extra resting alpha power. Scott extends that work backward in time, asking whether those levels show up in infancy and whether they forecast diagnosis. The answer: not well enough to matter beyond family history.
Brittenham et al. (2022) also mined frequency-domain EEG and got positive diagnostic signal, yet they used brief visual flashes, not long-term power growth. Task type, not just age or diagnosis, shapes the signal.
Why it matters
If you screen infants because they have an older sibling with ASD, do not lean on EEG power slopes for yes-or-no answers. Keep the family-history question in your intake forms; skip the pricey routine EEG unless you are in a research study. Spend your clinical hours on skill-building interventions, not on chasing a biomarker that adds no new predictive value.
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02At a glance
03Original 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 SUMMARY: Infants 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.
Autism research : official journal of the International Society for Autism Research, 2021 · doi:10.1017/S0954579417000980