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EEG Used as Diagnostic Tool for Autism

Electroencephalography (EEG) testing can spot children with autism as early as age 2, according to a new study from Boston Children’s Hospital.

Researchers compared raw EEG data from 430 children with autism and 554 control subjects — all between the ages of 2 and 12 — and found that those with autism had consistent EEG patterns indicating reduced connectivity between brain regions.

Neurologist Frank H. Duffy, M.D., and Heidelise Als, PhD, of the Department of Psychiatry, focused on children with “classic” autism who had been referred for EEGs by neurologists, psychiatrists or pediatricians to rule out seizure disorders.

Those with diagnosed seizure disorders were excluded, as were children with Asperger’s syndrome and “high functioning” autism, who tend to dominate (and skew) the existing literature because they are relatively easy to study, the researchers said.

They also excluded children with genetic syndromes linked to autism, such as Fragile X or Rett syndrome, children being treated for other major illnesses, those with sensory disorders like blindness and deafness, and those taking medications.

“We studied the typical autistic child seeing a behavioral specialist — children who typically don’t cooperate well with EEGs and are very hard to study,” Duffy said. “No one has extensively studied large samples of these children with EEGs, in part because of the difficulty of getting reliable EEG recordings from them.”

The researchers used techniques developed at Boston Children’s Hospital to get clean waking EEG recordings from the children, such as allowing them to take breaks. They then used computer algorithms to adjust for the children’s body and eye movements and muscle activity, which can throw off EEG readings.

To measure connectivity in the brain, Duffy and Als compared EEG readings from multiple electrodes placed on the children’s scalps, and quantified the degree to which any two given EEG signals — in the form of waves — are synchronized, known as coherence. If two or more waves rise and fall together over time, it indicates that those brain regions are tightly connected.

Using computational techniques, the researchers generated coherence readings for more than 4,000 unique combinations of electrode signals, and looked for the ones that seemed to vary the most from child to child. From these, they identified 33 coherence “factors” that consistently distinguished the children with autism from the controls.

Duffy and Als repeated their analysis 10 times, splitting their study population in half different ways and using half to identify the factors, and the other half to test and validate them. Each time, the classification scheme was validated, the researchers report.

“These factors allowed us to make a discriminatory rule that was highly significant and highly replicable,” says Duffy. “It didn’t take anything more than an EEG — the rest was computational. Our choice of variables was completely unbiased. The data told us what to do.”

The researchers believe the findings could be the basis for a future objective diagnostic test of autism, particularly at younger ages when behavior-based measures are unreliable.

They plan to repeat their study in children with Asperger’s syndrome to see if its EEG patterns are different from autism. They also plan to evaluate children whose autism is associated with conditions such as tuberous sclerosis, fragile X syndrome, and extremely premature birth.

The researchers’ findings were published June 26 in the online open-access journal BMC Medicine.

Source: Children’s Hospital Boston

EEG Used as Diagnostic Tool for Autism

Janice Wood

Janice Wood is a long-time writer and editor who began working at a daily newspaper before graduating from college. She has worked at a variety of newspapers, magazines and websites, covering everything from aviation to finance to healthcare.

APA Reference
Wood, J. (2018). EEG Used as Diagnostic Tool for Autism. Psych Central. Retrieved on December 2, 2020, from
Scientifically Reviewed
Last updated: 8 Aug 2018 (Originally: 27 Jun 2012)
Last reviewed: By a member of our scientific advisory board on 8 Aug 2018
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