Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by differences in social communication and interaction, alongside restricted and repetitive patterns of behavior. Electroencephalography (EEG) is a non-invasive technique that records the brain’s electrical activity, providing a direct measurement of neural function. The current diagnosis for ASD relies on behavioral observation, which is subjective and often results in a delayed diagnosis. Researchers are investigating EEG as a potential objective tool to identify measurable biomarkers for earlier and more accurate detection.
How EEG Works
Electroencephalography measures the collective electrical impulses generated by millions of communicating neurons within the brain. This technique involves placing multiple small metal discs, known as electrodes, on the scalp to detect faint voltage fluctuations. These electrical charges are amplified and recorded as waveforms, or brain waves, reflecting the synchronized activity of large neuron groups. These patterns are categorized into distinct frequency bands (delta, theta, alpha, and gamma), each associated with different states or information processing. For example, delta waves occur during deep sleep, while faster gamma waves are linked to higher-level cognitive tasks.
The Standard Approach to Autism Diagnosis
The current standard method for diagnosing ASD is based on clinical judgment and behavioral assessment, as outlined by diagnostic manuals. The primary tool is the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2), a standardized, semi-structured assessment. Clinicians administer planned activities to elicit behaviors related to communication, social interaction, and restricted or repetitive interests. These observations are combined with a detailed developmental history, often gathered through structured interviews like the Autism Diagnostic Interview-Revised (ADI-R). This process requires trained specialists and relies heavily on the interpretation of symptom presentation.
Identifying Specific EEG Biomarkers
Research efforts are focused on detecting specific, quantifiable differences in the electrical activity of the brain that correlate with ASD.
Altered Power Spectrum
One area of focus is the altered power spectrum, which measures the strength of various brain wave frequencies during resting states. Studies frequently observe a “U-shaped” profile in individuals with ASD, showing excess power in the slower theta and fast gamma bands, paired with decreased power in the mid-range alpha band. This pattern suggests an imbalance in the excitation and inhibition of neuronal activity across the brain.
Functional Connectivity
Another promising avenue involves measuring functional connectivity, or how different brain regions communicate, using a metric called coherence. Findings suggest a complex pattern where individuals with ASD often exhibit local over-connectivity and long-range under-connectivity. This difference in neural network architecture is thought to underlie the social and cognitive differences seen in the condition.
Event-Related Potentials (ERPs)
Researchers also analyze Event-Related Potentials (ERPs), which are tiny voltage spikes that occur in response to a specific sensory or cognitive event. A well-replicated finding in ASD is an atypical response in the N170 component, linked to the early stages of face processing. Studies report a prolonged or delayed latency of the N170 when individuals with ASD view faces, suggesting inefficient neural processing of social information. Furthermore, the P300 ERP, associated with attention and working memory, shows differences in amplitude and latency that correlate with symptom severity.
Barriers to Clinical Use and Standardization
Despite the scientific evidence, EEG is not currently used as a stand-alone diagnostic test for ASD in clinical practice. The primary hurdle is the high degree of heterogeneity within the autistic population, making it difficult to find a single, universal EEG biomarker. The subtle nature of the observed abnormalities means the technique lacks the necessary sensitivity or specificity to replace established behavioral assessments. A lack of standardized protocols across research laboratories further complicates the translation of findings into clinical tools, resulting in inconsistent study outcomes. While the primary clinical use of EEG is currently to rule out co-occurring conditions like epilepsy, future efforts are exploring advanced machine learning algorithms to analyze complex EEG data, aiming to identify clinically meaningful subgroups and enhance diagnostic accuracy.