Certain Patterns of Brain Waves in Infants May Point to Autism
Can brain activity patterns in infants at high risk for autism, be a predictor of the condition? The answer is yes, according to a new study led by Charles Nelson, research director in the division of developmental medicine at Boston Children’s Hospital.
According to a report this month by Spectrum News, the study found that autistic children were distinguished by electroencephalography (EEG) signals at ages 3, 6, 9, and 12 months. EEG signals have been used in the past to distinguish infants with autism, including in an August 2019 study, which reported potential EEG markers of autism in children with tuberous sclerosis.
The new study focused on “baby sibs,” meaning infants at higher risk for autism due to having an older sibling with the condition. Nelson and his team used EEG to record brain activity in 102 “baby sibs” and 69 non-autistic infants. The brain waves were recorded at six frequencies for two to five minutes while the infants sat with their caregivers. Data was collected from each infant at 3, 6, 9, 12, 18, 24, and 36 months of age. At 36 months, the children were evaluated for autism using the Autism Diagnostic Observation Schedule. 31 of the “baby sibs” were diagnosed with autism, while none of the non-autistic children were diagnosed.
According to Spectrum’s report, the children with autism were distinguished by wave patterns in two frequency bands, delta and gamma, in the frontal lobe during their first year. The children with autism showed more significant increases in delta wave power over the first year than the children without autism, and slower increases in gamma wave power.
The patterns accurately identified autistic infants 72% of the time, and correctly identified those without autism 91% of the time. The study was published in September in the journal Nature Communications.
According to researchers, the patterns may be less predictive of autism after an infant’s first year due to the fact that the brain activity shows more pronounced individual differences, making the patterns specific to autism harder to see.
For their next step, the researchers will collect data from another 400 infants with related conditions, to see whether the method can distinguish autism from those conditions.