Autism is challenging to diagnose, especially early in life. A new study published online on May 1, 2018 in Scientific Reports shows that inexpensive EEGs, which measure brain electrical activity, accurately predict or rule out autism spectrum disorder (ASD) in infants, even in some as young as 3 months. The open-access article is titled “EEG Analytics for Early Detection of Autism Spectrum Disorder: A Data-Driven Approach.” "EEGs are low-cost, non-invasive and relatively easy to incorporate into well-baby checkups," says Charles Nelson, PhD, Director of the Laboratories of Cognitive Neuroscience at Boston Children's Hospital and co-author of the study. "Their reliability in predicting whether a child will develop autism raises the possibility of intervening very early, well before clear behavioral symptoms emerge. This could lead to better outcomes and perhaps even prevent some of the behaviors associated with ASD." The study analyzed data from the Infant Sibling Project (now called the Infant Screening Project), a collaboration between Boston Children's Hospital and Boston University that seeks to map early development and identify infants at risk for developing ASD and/or language and communication difficulties. William Bosl, PhD, Associate Professor of Health Informatics and Clinical Psychology at the University of San Francisco, also affiliated with the Computational Health Informatics Program (CHIP) at Boston Children's Hospital, has been working for close to a decade on algorithms to interpret EEG signals, the familiar squiggly lines generated by electrical activity in the brain. Dr. Bosl's research suggests that even an EEG that appears normal contains "deep" data that reflect brain function, connectivity patterns, and structure that can be found only with computer algorithms. The Infant Screening Project provided Dr.
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