A Biomarker to Predict Autism?

In a small but promising study, researchers were able to predict which human subjects had an autism spectrum disorder and which did not following three different measures of the brain.

Researchers from the University of Alabama at Birmingham (UAB) and Auburn University conducted medical imaging exams of 19 high-functioning adults with ASD and 18 typically developing peers, who were matched for age and intelligence, according to results distributed by the university and published in the journal Cortex.

They used magnetic resonance imaging to measure brain cortical thickness (volume data), connectivity of white-matter fibers of the brain, and brain neurotransmitters such as N-acetylaspartate. White matter in the brain acts as electrical cables to link regions. The same MRI machine did all three measures with different settings for each, according to UAB.

Results showed significant differences in some measures among each of the three imaging exams. After combining differences in a decision tree, the researchers—who did not known whose measurements they were considering—were able to distinguish subjects with autism from subjects in the control group with 92 percent accuracy. Another decision tree that was built with five significant findings sorted ASD participants by disease severity.

Researchers acknowledge the findings are just a starting point ad must be validated using a larger subject sample. The findings are a step toward a possible biomarker for autism and even diagnosing it at earlier ages when the brain is very “plastic” and intervention may be more effective, Rejesh Kana, associate professor of psychology at UAB, said in a statement. He would like larger studies to include lower-functioning ASD subjects and younger ASD subjects, as well as more female participants.

The published paper is available here.

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