Diagnosing autism spectrum disorder (ASD) is still a daunting challenge because of the degree of complexity involved, requiring highly specialized professionals. Autism is a multifactorial neurodevelopment disorder with widely varying symptoms. In the United States, about 1 in 36 children have been diagnosed with ASD, according to the Centers for Disease Control and Prevention (CDC), and yet there are no biochemical markers to identify it with precision. A quantitative diagnostic method is proposed by Brazilian researchers in an article published in the journal Scientific Reports.
imaging data for 500 people, about half of whom had been diagnosed with ASD. Machine learning techniques were applied to the data.imaging [fMRI] and electroencephalogram [EEG] data," said Francisco Rodrigues, last author of the article. He is a professor at the University of São Paulo's Institute of Mathematics and Computer Science in São Carlos, Brazil.
The researchers fed a machine learning algorithm with these maps. Based on the learned examples, the system was able to determine which brain alterations were associated with ASD with above 95% accuracy. "Until a few years ago, little was known about the alterations that lead to the symptoms of ASD. Now, however, brain alterations in ASD patients are known to be associated with certain behaviors, although anatomical research shows that the alterations are hard to see, making diagnosis of mild ASD much harder. Our study is an important step in the development of novel methodologies that can help us obtain a deeper understanding of this neurodivergence," Rodrigues said.
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