Researchers from the University of Virginia announced on July 16 that subtle patterns in brain electrical activity may help explain why some children with autism communicate more easily than others. The findings, published in Scientific Reports, analyzed brain activity in over 300 children and adolescents while they listened to speech and found links between these patterns and everyday communication abilities among autistic youths.
The study involved researchers from the University of Virginia's schools of Medicine and Data Science, Seattle Children's Research Institute, the University of Washington, Yale University, UCLA, and other institutions. Participants included 162 youths with autism and 144 typically developing peers aged 7 to 18. Each participant wore high-density electroencephalography (EEG) caps equipped with 128 sensors while listening to streams of spoken nonsense words designed to measure how the brain processes speech.
Rather than focusing only on traditional brain wave patterns, researchers examined a newer measure known as the brain's "aperiodic" signal. This signal reflects the balance between excitation and inhibition—processes that help distinguish meaningful information from background noise. The study found that autistic participants showed altered aperiodic signals consistent with increased neural "noise," suggesting their brains may process speech less efficiently.
More importantly, those whose EEG readings indicated noisier brain activity tended to score lower on measures of everyday verbal communication. However, these same signals were not associated with traditional language skills such as vocabulary or grammar. The researchers said this does not represent a diagnostic test for autism but could serve as a biological marker for monitoring changes in communication abilities or assessing whether therapies affect underlying brain function.
Jack Van Horn, coauthor and professor at UVA's School of Data Science, said: "The human brain generates an incredible amount of data every second. The challenge isn't collecting it anymore; it's making sense of it. Advances in computational analysis are allowing us to separate meaningful signals from background activity in ways that weren't possible just a few years ago."
While this research included one of the largest EEG datasets for this population so far, scientists say further studies are needed before clinical applications can be considered—especially since most participants had average or above-average verbal abilities. Researchers also noted that EEG is an indirect measure and should be combined with other imaging techniques for deeper understanding.
