Summary: Researchers monitored real-time auditory processing in more than 300 children and adolescents using high-density electroencephalography (EEG). By separating the brain’s rhythmic oscillations from the underlying background activity, the team isolated the brain’s aperiodic signal—a measure that reflects the balance between neural excitation and inhibition. Their results show that autistic youths with greater everyday communication challenges display altered aperiodic profiles consistent with elevated neural noise, which appears to interfere with efficient speech processing.
Key Facts
- Revealing the Aperiodic Background: Traditional EEG analyses emphasize periodic rhythms such as alpha, beta, or gamma waves. This study targeted the aperiodic component—the non-rhythmic background electrical activity often treated as noise. That component provides insight into the brain’s balance of excitation and inhibition, a fundamental mechanism that helps the brain prioritize meaningful signals over background activity.
- Neural Noise and Speech Processing: High-density EEG and advanced computational methods revealed altered aperiodic patterns in autistic participants. Those patterns are consistent with higher baseline neural noise, suggesting the auditory cortex receives inputs through a noisier internal background, reducing the fidelity of real-time speech processing.
- Functional Communication vs. Language Knowledge: Increased neural noise correlated with weaker everyday verbal communication—how well a young person uses language in social and interactive contexts—but did not predict core language measures like vocabulary size or grammatical knowledge. In other words, the problem appears to be processing in live contexts rather than stored language ability.
- Biomarker Potential, Not a Diagnostic Test: The researchers emphasize this aperiodic signature is not a diagnostic tool for autism. Rather, it represents an objective biological marker that could be tracked over time to monitor changes in communication or to evaluate whether interventions shift underlying brain dynamics.
- Computational Advances Made It Possible: Extracting these subtle, low-frequency features required modern data-science techniques applied to large, high-density EEG datasets. Improved algorithms made it possible to separate meaningful background signatures from other noise sources in ways that were previously infeasible.
- Sample Limits and Next Steps: Although the dataset is large, most participants had average or above-average verbal skills. Future studies will extend this approach to minimally verbal autistic individuals and combine EEG with other neuroimaging methods to build a fuller picture of the underlying biology.
Source: University of Virginia
Why do some children with autism communicate more easily than others, even when they hear the same words?
Scientists at the University of Virginia investigated whether differences in brain electrical activity during speech perception relate to everyday communication abilities in autistic youths. The study, published in Scientific Reports, analyzed EEG recordings from more than 300 children and adolescents while they listened to speech-like stimuli. The results point to subtle differences in the brain’s background electrical activity—the aperiodic signal—that may help explain why some autistic youths find conversational speech harder to process in real time.
The findings offer a clearer biological perspective on communication challenges in autism and suggest a measurable brain marker that could complement behavioral assessments as research on therapies and interventions advances.
The study recorded brain activity from 306 participants ages 7 to 18, including 162 youths with autism and 144 typically developing peers. Participants wore 128-channel EEG caps while listening to streams of spoken nonsense words crafted to probe speech perception. Instead of focusing only on classic oscillatory bands, the team quantified the aperiodic component, which reflects the relative balance of excitation and inhibition in cortical circuits.
Autistic participants showed altered aperiodic measures consistent with greater cortical “noisiness” and a broadband reduction in spectral power during speech perception. Those with higher neural noise tended to have lower scores on measures of everyday verbal communication, while traditional language skills—such as vocabulary and grammar—remained unrelated to the aperiodic signal.
The authors caution that EEG provides an indirect measure of neural activity and that these results are not a diagnostic marker for autism. Rather, the aperiodic profile may serve as an objective biomarker to monitor communication-related brain changes over time or to evaluate whether interventions are affecting underlying neural balance.
The study also demonstrates the growing role of advanced computational analytics in neuroscience. As Jack Van Horn, coauthor and professor in UVA’s School of Data Science, notes, modern algorithms are crucial for extracting subtle, meaningful signatures from the massive volumes of data produced by the human brain.
Future work will test whether these aperiodic patterns generalize to minimally verbal individuals and whether combining EEG with structural and functional neuroimaging can clarify the biological mechanisms that underlie communication differences across the autism spectrum.
Key Questions Answered:
A: The aperiodic signal is the continuous, non-rhythmic electrical activity underlying the brain’s oscillations. It reflects how well excitation and inhibition are balanced across circuits. When that balance is disrupted, the aperiodic component becomes flatter and noisier—akin to a higher background hum—making it harder for the brain to extract clear speech signals from incoming sound.
A: Vocabulary and grammar are stored, retrievable knowledge. Everyday communication relies on rapid, on-the-fly processing: decoding speech, filtering competing sounds, reading social cues, and producing timely responses. Increased neural noise appears to slow or degrade that real-time processing, so stored language knowledge remains intact while live conversational use becomes more challenging.
A: Currently, treatment effects are often measured through behavioral observations and rating scales. An objective EEG-based aperiodic marker could provide a biological readout of whether interventions reduce neural noise or restore excitation-inhibition balance, offering a faster and more directly measurable index of neural change alongside behavioral outcomes.
Editorial Notes:
- This article was edited by a Neuroscience News editor.
- The journal paper was reviewed in full.
- Additional context was provided by editorial staff.
About this ASD research news
Author: Josh Barney
Source: University of Virginia
Contact: Josh Barney – University of Virginia
Image credit: Neuroscience News
Original Research: Open access. “Altered aperiodic EEG spectral power during speech perception task is associated with verbal communication in youths with Autism Spectrum Disorder” by Vardan Arutiunian, Megha Santhosh, Emily Neuhaus, Heather Borland, Raphael A. Bernier, Susan Y. Bookheimer, Mirella Dapretto, Abha R. Gupta, Allison Jack, Shafali Jeste, James C. McPartland, Adam Naples, John D. Van Horn, Kevin A. Pelphrey & Sara Jane Webb. Scientific Reports. DOI: 10.1038/s41598-026-59415-9
Abstract
Altered aperiodic EEG spectral power during speech perception task is associated with verbal communication in youths with Autism Spectrum Disorder
Language and communication difficulties commonly co-occur with Autism Spectrum Disorder (ASD), yet their neural bases remain incompletely understood. An imbalance between cortical excitation and inhibition (E/I) is a leading neurobiological hypothesis for ASD. Using a speech perception task and high-density 128-channel EEG, the study compared sex- and age-matched groups of youths with ASD (N = 162) and typically developing controls (N = 144) aged 7–18 years.
Results indicated altered E/I-related measures in the ASD group, consistent with increased cortical excitation or elevated neural “noise,” along with broad reductions in spectral power during speech perception. A greater degree of neural noise—reflected by changes in the aperiodic exponent and offset—was associated with poorer everyday verbal communication but was not linked to standardized measures of language knowledge. These findings suggest that cortical noisiness may be a relevant marker for tracking and studying verbal communication differences in ASD.