Scans predict autism in infants

Posted February 16, 2017

The study looked at 148 children including those at high risk of autism because they had older siblings with the disorder. A computer algorithm was then used to predict autism before clinically diagnosable behaviors set in.

Piven and his team report their findings in the February 15 issue of Nature. Ultimately, they were able to correctly identify 80 percent of high-risk babies who were later given a diagnosis of autism at 24 months.

In most cases, autism can't be diagnosed until children are two years old, but sometimes signs of the condition appear earlier.

Previously, researchers were only able to identify behavioral changes that met the autistic criteria at age 2.

Researchers have known for a while that children with autism tend to have bigger brains, but they hadn't figured out when the brain got bigger or how it changes in early childhood.

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The group will be publishing several more papers in the future, Piven said. This analytic approach was also nearly ideal in predicting which high-risk babies would not develop autism by age 2 years.

The analysis was most accurate in predicting the high-risk babies that did not develop autism, they added.

The study was funded by the National Institutes of Health and led by a team of researchers at the University of North Carolina-Chapel Hill. Other key collaborators are McGill University, the University of Alberta, the College of Charleston, and New York University. "The fact that they're not consistent suggests that some of the expansion in surface area may actually not be relevant to the detection of autism", he says. "We could not have made these discoveries without their wholehearted participation".

Between 12 and 24 months of age, the surface area growth rate evens out in the two groups. They assessed the children's cognition, daily-living abilities and communication skills.

Previous behavioral studies of infants who later developed autism - who had older siblings with autism - revealed that social behaviors typical of autism emerge during the second year of life. "We see an increased rate of growth of sort of the outer surface of the brain, the folds, the sort of waviness of the surface that's followed by an overgrowth of the brain in the second year", said Dr. Joseph Piven, the study's author.

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Researchers at multiple sites across the country and Canada studied MRI scans of babies at age six months and one year. "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 fantastic". "Before the consolidation of symptoms and brain deficits and at a time when the brain is most malleable, giving us the greatest chance of having an impact with early intervention".

But the diagnostic breakthrough addresses a key problem that has confounded efforts to effectively screen for autism as quickly as possible: Babies typically don't show clear outward signs of the disorder until almost the end of their second year of life. "Once you've missed those developmental milestones, catching up is a struggle for many and almost impossible for some". At that point, the overall brain volume increases faster in children with autism than in controls.

And he added, "There are no treatments agreed upon by the field for infants [deemed to be] presymptomatic for autism". Whereas it might be okay to leave a neurotypical child to play with a toy, a child headed for autism might benefit from more interaction, he says, with a parent cooing, laughing and singing.

The researchers input measures of surface area, thickness and volume at 6 and 12 months into a machine-learning algorithm to predict which infants would later be diagnosed with autism. A large follow-up study would be needed to test whether autism can be predicted in the general population, she says. "We hope these ongoing efforts will lead to additional biomarkers, which could provide the basis for early, pre-symptomatic diagnosis and serve also to guide individualized interventions to help these kids from falling behind their peers".

Adapted from a release by the UNC news office.

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