The autistic brain processes faces differently than scientists thought

James McPartland and colleagues at Yale's Child Study Center conducted a study of approximately 400 autistic children, published in Nature Mental Health, examining how their brains process faces using electroencephalography (EEG). The research analyzed electrical signals from 128 electrodes across the entire scalp rather than focusing on limited regions.

The researchers found that brain activity associated with face recognition was less clearly distinguishable in autistic participants compared to neurotypical children. Machine learning analysis of the EEG recordings showed that electrical signals associated with viewing faces were less distinct in autistic children.

A second key finding concerned development. In neurotypical children, the brain's response to faces became more specialized with age. Autistic children did not show this same pattern of increasing specialization.

By examining brain activity across a wider time period, the team discovered that differences in face processing begin before the N170 signal, a neural marker that typically appears about 170 milliseconds after viewing a face. This suggests autism-related differences arise at earlier stages of visual processing than previously established.

"The neural activity that we measure at the scalp is just not as distinct in autistic children," said Jason Griffin, first author and assistant professor at the University of Houston.

McPartland noted that the patterns identified suggest differences in face processing are connected to underlying developmental characteristics of autism rather than simply reflecting accumulated life experiences. The researchers propose that support for interpreting facial emotions could be particularly beneficial when introduced earlier in childhood, though they emphasize that autism does not necessarily create difficulties requiring intervention.

The identified brain activity patterns could eventually contribute to developing more objective diagnostic tools for autism, as clinicians currently diagnose the condition primarily through behavioral evaluation.