Predicting autism: Researchers find autism biomarkers in infancy

Camille Francis
February 16, 2017

A team of researchers in the United States and Canada enrolled 106 infants in the study, some of whose siblings had been diagnosed with autism.

Research could then begin to examine the effect of interventions on children during a period before the syndrome is present and when the brain is most malleable. "Often we don't keep in mind the degree of work it takes to do this kind of study", Dawson says.

By showing scientists more about how brains develop prior to an autism diagnosis, the study may also offer insights into the genetic triggers of autism, says James McPartland, a psychologist at Yale University's Child Study Center who also did not take part in the research.

Researchers also will be looking for biomarkers such as rapid eye movement that could be associated with excessive brain growth, and could be easier for doctors to check. In addition, brain overgrowth correlated with the severity of social deficits that emerged by age two.

A team from several leading institutions in the USA and Canada published a paper Wednesday in the journal Nature, demonstrating an algorithm they created that improved early diagnosis of the condition among several children known to be at high risk.

Kerry Keller's oldest son was diagnosed with autism at the age of two.

The research was funded by the National Institutes of Health, Autism Speaks and the Simons Foundation.

In most cases, autism can't be diagnosed until children are two years old, but sometimes signs of the condition appear earlier. Schultz, PhD, who directs the Center for Autism Research and led the CHOP study site.

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Jed Elison, assistant professor of pediatrics at the U of M, co-authored a paper on the research published Wednesday in the scientific journal Nature, and said the results shed new light on autism.

It worked well. Using just three variables-brain surface area, brain volume, and gender (boys are more likely to have autism than girls)-the algorithm identified up eight out of 10 kids with autism.

A recent breakthrough by researchers at the Carolina Institute for Developmental Disabilities (CIDD) at the University of North Carolina could mean that doctors might be able to predict autism before age one.

Numerous measurements the algorithm relied on most are related to surface area, and came from 6-month-old children.

"These longitudinal imaging studies, which follow the same infants as they grow older, are really starting to hone in on critical brain developmental processes that can distinguish children who go on to develop ASD and those who do not", said Dager. To generate these predictive results, the team drew on machine learning, a statistical approach that uses pattern recognition to make very detailed predictions.

"We have wonderful, dedicated families involved in this study", said Stephen Dager, a UW professor of radiology and associate director of the CHDD, who led the study at the UW. The findings raise the prospect of diagnosing autism spectrum disorder (ASD) months before children develop symptoms, a goal that has proved elusive.

The molecular mechanisms underlying brain surface area expansion - many of which are known - may also provide clues to how autism unfolds, Piven says. Each was at a higher risk for the disorder. "Using brain imaging, we were able to pinpoint areas of the brain where atypical development contributes to autism". "In Parkinson's, for instance, we know that once a person is diagnosed, they've already lost a substantial portion of the dopamine receptors in their brain, making treatment less effective".

"We view this is, particularly in this high-familial risk sample, as a very real possibility of pre-symptomatic detection", Piven said.

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