New research suggests that subtle patterns of background electrical brain activity are associated with everyday verbal communication skills in young people with autism. Scientists have found that high levels of background electrical activity, often described as neural noise, tend to make real-world communication more difficult for children with autism spectrum disorders. The study was published in a scientific journal scientific report.
Autism spectrum disorder is a neurodevelopmental disorder characterized by unique social interactions, communication differences, and repetitive behaviors. Although structural language abilities vary widely across the autism spectrum, everyday communication challenges remain common and significantly impact social outcomes.
To understand why these differences in communication occur, researchers are examining the underlying neurobiology of the brain. One of the main areas of focus concerns the balance between neural excitation and inhibition within the brain’s central networks.
Neural excitation occurs when brain cells send signals that encourage other cells to fire, while neural inhibition occurs when cells release signals that dampen electrical activity. If the balance between excitation and inhibition shifts too much toward excitation, it can generate noisy background signals within the cortex and reduce the efficiency of information processing.
Lead author Vardan Artiunyan, a researcher at the Seattle Children’s Research Institute, along with lead author Sarah Jane Webb, a professor at the University of Washington and the Seattle Children’s Research Institute, sought to observe how these electrical patterns unfold during speech processing. The authors aimed to determine whether specific EEG features recorded while children listened to speech sounds reflected this signal balance and correlated with actual communication skills.
To conduct the study, the authors evaluated 306 children and adolescents between the ages of 7 and 18. The sample consisted of 162 youth diagnosed with an autism spectrum disorder and 144 of their neurotypical peers as a comparison group. The two groups were matched by age and sex assigned at birth.
During the experiment, participants wore an electroencephalography (EEG) cap fitted with 128 sensors that measure electrical impulses in the scalp. Electroencephalography is a non-invasive technique that records the rhythmic electrical activity produced by networks of brain cells. The young people watched a still image of the robot on a monitor and listened to audio recordings of meaningless three-syllable words such as “pavik.”
The researchers recorded electrical signals generated across nine different regions of interest on the scalp. They separated these complex raw signals into periodic oscillations, which are rhythmically repeating brain waves, and aperiodic components, which represent non-repetitive background electrical activity.
Aperiodic signals produce two specific measures known as exponents and offsets. The index indicates how quickly the power decreases over higher frequencies and provides evidence about the balance between excitation and inhibition. The offset, on the other hand, reflects the total amount of background neuronal firing.
The scientists also measured periodic gamma-ray activity, which refers to fast-frequency brain electrical waves in the 35-55 hertz range. Fast gamma oscillations are known to rely heavily on specialized inhibitory neurons that help coordinate brain signaling during sensory tasks.
Behavioral assessments were administered to all participants to assess various aspects of language and cognition. Overall structural language ability, including formal grammar and vocabulary, was assessed using the Clinical Assessment of Language Fundamentals.
Everyday functional communication skills were measured using the Vineland Adaptive Behavior Scale, an assessment tool that assesses actual daily verbal interactions based on parent report. Cognition was assessed using a standard cognitive test battery to determine verbal and nonverbal intelligence quotients.
Statistical analysis revealed significant differences in background brain signals between the two participant groups. Autistic youth had lower aperiodic index and aperiodic offset compared to neurotypical youth.
Autistic participants also showed increased levels of periodic gamma intensity during a speech listening task. Together, these three brain patterns indicate functional changes in the autistic brain, characterized by increased baseline arousal and increased overall background neural noise.
When assessing how these brain signals were related to behavioral traits, the authors observed specific relationships. We found that lower aperiodic index and offset were associated with lower daily verbal communication scores on the Vineland assessment of youth with autism.
This pattern suggests that children whose brain signals reflect higher neural noise tend to experience greater difficulties in everyday practical communication. These same electrical signals showed no significant association with formal structural language scores such as vocabulary or grammatical knowledge.
The researchers also tracked age-related changes in these brain patterns from childhood to adolescence. Gamma oscillations tended to increase with age in both groups, whereas aperiodic index and offset steadily decreased as participants grew older.
To interpret these results, several important study parameters and limitations must be considered. The majority of autistic participants in the sample had average or above average intellectual and language abilities.
Because of this sample profile, the results may not directly generalize to autistic individuals with nonverbal or minimal language skills. Future research should include participants with a broader range of cognitive and communication profiles to confirm these patterns.
Additionally, scalp electroencephalography provides indirect system-level estimates of brain excitation and inhibition rather than direct physical measurements of specific brain chemicals. This technique measures broad electrical field changes across the scalp, rather than individual microscopic connections between nerve cells.
Future studies may combine electroencephalography with advanced imaging techniques such as magnetic resonance spectroscopy. Magnetic resonance spectroscopy is an imaging technique that allows scientists to measure the concentration of certain chemical messengers in brain tissue, such as gamma-aminobutyric acid and glutamate.
Despite these limitations, this study suggests that measuring background electrical signals is a promising tool for understanding communication differences in autism. Tracking these broad electrical properties could help researchers assess how developmental therapies affect underlying brain function over time.
The study, “Aperiodic EEG spectral power changes during speech recognition tasks are associated with verbal communication in youth with autism spectrum disorders,” was authored by Vardan Arutiunian, Megha Santhosh, Emily Neuhaus, Heather Borland, Raphael A. Bernier, Susan Y. Bookheimer, Mirella Dapretto, Abha R. Gupta, Allison Jack, Shafali Jeste, and James. C. McPartland, Adam Naples, John D. Horn, Kevin A. Pelfrey, Sarah Jane Webb.
