For the first time, a new study suggests it's possible to predict within the first year of life if a child will develop autism. The ability to identify autism risk during infancy could set the stage for developing very early preventive treatments when the brain is most malleable.
After feeding the scans of the babies at six, 12, and 24 months into an algorithm, the researchers were able to identify, with an 80 percent degree of accuracy, which babies would subsequently receive an autism diagnosis. "We do expect roughly the same prediction accuracy when more subjects are added", said co-author Brent Munsell, an assistant professor at College of Charleston, in an email to IEEE. The study's lead site was based at University of North Carolina-Chapel Hill.
"One of the things that has been shown over and over again is, the earlier you can begin an intervention", such as behavioral therapy for children with autism, "the greater the chance of limiting some of the characteristics of autism", Pletcher said.
What scientists found was that these high-risk children, who went on to develop ASD, experienced a hyper-expansion of the brain from six to 12 months of age.
If parents have a child with autism and then have a second child, such a test might be clinically useful in identifying infants at highest risk for developing this condition.
As the name autism spectrum disorder suggests, autism can manifest in many different ways among patients, but in general it can be characterized by certain social difficulties and a tendency toward highly repetitive and ritualistic behaviors.
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On average, children aren't diagnosed with autism until they are four years old - once their brain has begun to expand, and once they begin behaving differently than neurotypical children - though some are diagnosed as early as their second birthday, Pletcher noted.
The study looked at 106 infants considered to be at high risk for autism because they had an older sibling with the developmental disorder and 42 low-risk infants. Now about 3 million people of all ages live with autism in the U.S.
Over the last 12 years, more than 100 children were scanned at 6, 12 and 24 months old. Researchers built a computer program using the brain scans from those children to search for the same changes in other children. At that point, the overall brain volume increases faster in children with autism than in controls. This most recent study shows this pattern of rapid growth originates in specific brain regions long before brain size itself shows significant enlargement. The subsequent overgrowth was then linked to the emergence of autistic social deficits in the child's toddler years.
The researchers made measurements of cortical surface areas and cortical thickness at 6 and 12 months of age and studied the rate of growth between 6 and 12 months of age.
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. "So for children like my Padric who's the youngest of four boys and the oldest has Autism, I mean that would be great if he were an infant and we had something we could go to that 'we will follow your child through these scans and developmental tests, ' and then be able to get you the earliest intervention we can if we see markers for Autism, that's awesome".
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.
The authors emphasize that the effectiveness of the algorithm needs to be reproduced in future studies in order to be ready for clinical use.