Researchers say they were able to identify with more than 90 percent accuracy which babies would go on to be diagnosed with the developmental disorder by age 2. They looked at infants with a high family risk of autism who were later diagnosed with the disorder, high-risk infants who did not develop autism and low-risk infants who did not develop the condition.
"The ability to accurately predict who will develop autism opens up tremendous new opportunities to develop effective therapies starting in the first year of life", said Robert T. Schultz, director of Children's Hospital of Philadelphia's Center for Autism Research, one of the study sites.
Researchers at the University of North Carolina have developed a method using magnetic resonance imaging (MRI) in infants with older siblings with autism to correctly predict whether infants would later meet the criteria for autism at two years old.
"But now we are entering the era of possibly detecting autism before the symptoms are even present", Piven said. "We're learning that there are biological changes that occur at [the time] or before the symptoms start to emerge", says Geraldine Dawson, a clinical psychologist and autism researcher at Duke University who was not involved in the new work. This means that these kids will have better social skills compared to those who were diagnosed when they were 5 or 6 years old. A computer algorithm was then used to predict autism before clinically diagnosable behaviors set in.
"These findings not only are significant for the field of autism, but they also could inform the broader field of psychiatry and prevention science as it relates to various psychiatric conditions", Elison said.
Immigrant protected under Obama program detained near Seattle, faces deportation
The government granted him a DACA card in 2014 and renewed it in 2016, finding that he was no threat to public safety. He paid a $150 fine and the case was deferred, according to court records.
However, by considering other factors as well including additional brain measurements and the child's sex, the researchers used a statistical approach known as machine learning to assess with near flawless accuracy who would develop autism. Its first author was Heather Cody Hazlett of the University of North Carolina in Chapel Hill.
In the US, there are more than 3 million people with ASD, and tens of millions across the globe.
It's known that earlier intervention produces better results in those with autism spectrum disorder, coauthor Stephen Dager of the University of Washington in Seattle said in a statement. At the same time, the technique employed nearly flawlessly predicted which babies at high risk of developing the disease would not suffer from autism.
A team of researchers has shown that measuring the growth of brains in babies can predict the onset of autism later in childhood. The study also showed that the rapid growth pattern originates in specific brain regions - particularly the cerebral cortex - long before the brain overall showed notable enlargement. Having a reliable tool for early diagnosis could help researchers to test interventions, because it would help them to determine whether a treatment is working or not, adds Piven. That change happened before the child's first birthday.
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