Brain signals explain speech communication differences in autistic children

Why do some children with autism communicate more easily than others, even when they hear the same words?

The findings offer new clues about the biology behind autism and could one day help researchers objectively measure communication challenges and evaluate new therapies.

The research analyzed brain activity in more than 300 children and adolescents while they listened to speech. The findings suggest subtle differences in brain electrical activity may help explain why some autistic youths have greater difficulty with verbal communication than others.

The study included researchers from the University of Virginia's schools of Medicine and Data Science, along with colleagues from Seattle Children's Research Institute, the University of Washington, Yale University, UCLA and several other institutions.

Researchers have long known that many autistic individuals experience challenges with language and communication, but the underlying brain mechanisms have remained difficult to measure. Most clinical assessments rely on behavioral observations, rather than biological indicators.

To investigate those mechanisms, the research team recorded brain activity from 306 participants aged 7 to 18, including 162 youths with autism and 144 typically developing peers. Participants wore high-density electroencephalography, or EEG, caps equipped with 128 sensors while listening to streams of spoken nonsense words designed to measure how the brain processes speech.

The study found that autistic participants showed altered patterns in these signals, consistent with increased neural "noise," suggesting the brain may process speech less efficiently.

More importantly, youths whose brain activity appeared noisier also tended to score lower on measures of everyday verbal communication. Those same brain signals were not associated with traditional language skills, such as vocabulary or grammar.

The researchers caution that the findings do not represent a diagnostic test for autism. Instead, they point to a promising biological marker that could eventually help researchers monitor changes in communication abilities over time, or measure whether therapies are affecting underlying brain function.

The work also highlights the growing role of advanced data science techniques in neuroscience, allowing researchers to uncover subtle patterns in complex brain data that were previously difficult to detect.

The authors also note that EEG provides an indirect measure of brain activity and should ultimately be combined with other imaging techniques to better understand the underlying biology.

Still, the findings move scientists closer to a longstanding goal in autism research: developing objective biological measures that complement behavioral evaluations.

Source:

University of Virginia Health System

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