Earlier diagnosis of children with autism could allow for earlier training that helps them cope with the developmental disorder.
MINNEAPOLIS — Measuring changes in infants’ brain growth can allow doctors to predict the likelihood that they will be diagnosed with autism in their toddler years, according to research published Wednesday by two University of Minnesota researchers.
The results, published in the journal Nature, could help reduce the severity of autism in children, because earlier diagnosis could allow for earlier training that helps them cope with the developmental disorder.
Children with autism often show hints of the disorder by their first birthdays, such as not responding to their names or making eye contact. But studies haven’t proved these early behaviors to be reliable predictors, said co-author Jed Elison, an assistant professor in the university’s Institute of Child Development. Most children aren’t diagnosed until they are 2 or older, when clear behavioral signs emerge.
“What really differentiates this work (is) the accuracy with which we can make a prediction,” Elison said. “We’re generating a prediction before the signs of autism can be observed, which is really groundbreaking.”
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Autism, or autism spectrum disorder (ASD), refers to a broad range of brain-development abnormalities that cause struggles with learning, social interaction and verbal and nonverbal communication. The U.S. Centers for Disease Control and Prevention estimate that one in 68 children meets criteria for ASD.
Studies have shown that earlier intervention can reduce its severity because of the “plasticity” of the brain at an early age, which can make it particularly receptive to cognitive and communication training, Elison said. “The earlier an intervention is implemented, the better the outcome for kids with autism … This has been a finding since the ’70s.”
Using magnetic resonance imaging (MRI), researchers took brain scans at 6 months, 12 months and 24 months of children who were at high risk for autism because an older sibling had the disorder. They then identified the physical differences in the brains of children in that group who developed autism, and applied those findings to a second group of high-risk children.
Identifying those physical differences correctly predicted 80 percent of the children in the second high-risk group who met the clinical criteria for autism.
Excessive growth in the size and surface area of the brain between six and 12 months of age proved to be a significant predictor, Elison said.
The study also found that focusing on this brain growth correctly predicted 89 percent of the children who didn’t go on to develop autism. That finding is equally important because tests can’t be used clinically if they wrongly frighten too many healthy people into believing they are sick.
Imaging scans for the study took place in other states, including North Carolina — where Elison first became involved in 2007 as a graduate student and then became a co-investigator before his move to Minnesota. Also participating was Jason Wolff, a University of Minnesota assistant professor of educational psychology, who said the study “offers the unprecedented possibility of predicting whether or not a child will develop autism based on neurobiological data.”
Elison has applied for a grant to conduct a new round of imaging studies in Minnesota, because he said the initial finding needs to be replicated with different children and different MRI techniques.
The end result likely wouldn’t be universal MRI scans for children, because of the extraordinary costs involved, but Elison said he could one day see brain scans being routinely used on the siblings of children with autism.
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.