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EEG complexity as a biomarker for Autism Spectrum Disorder risk

by learningdiscoveries on March 22, 2011

Most assessment tools and research have focused on the diagnosis of Autism at the age of three, but what if we could diagnose autism before this age through the identification of subtle brain function signatures? Lead study author William Bosl, PhD, research scientist at Children’s Hospital Boston and instructor in pediatrics at Harvard Medical School, Boston, Massachusetts, told Medscape Medical News that they may have found a way to diagnose early developing characteristics of Autism Spectrum Disorder (ASD) in infants as young as 1 year, through the use of EEG (electroencephalogram) and basic algorithms.

It has been known for some time now that complex neurodevelopmental disorders are characterized by subtle brain function signatures early in life before behavioral symptoms are apparent. These EEG patterns or endophenotypes may prove to be measurable biomarkers for later cognitive impairments. EEG signals are thought to contain information about the architecture of neural networks in the brain on many scales. Therefore, detecting abnormalities in EEG signals as early as possible may turn out to be a useful biomarker for developmental cognitive disorders.

In this study, researchers set out to develop a biomarker for normal brain development.  Using resting EEG data and sophisticated algorithms (Multiscale Entropy or mMSE) to locate the density of neurons in particular areas of the brain and the connections between, they established a biomarker for normal brain function.. They then used this biomarker to distinguish typically developing children from a group of infants at high risk for autism spectrum disorder (ASD), defined on the basis of an older sibling with ASD.

The study collected data from 79 different infants, 46 of those were high risk for Autism (siblings diagnosed with autism) and 33 in the control group (no family history of neurodevelopmental disorders) and aimed to classify the children into high-risk or low-risk groups. Testing sessions included infants from ages 6 to 24 months. Resting EEG signals from the brain were recorded for all participants starting at 6 months and repeated when possible at the ages of 9,12,18 and/or 24 months.

Researchers were able to classify infants at the high-risk of Autism and in control group with 80% accuracy. Classification accuracy for boys rose to close to 100% at the age of 9 months and remained significantly high (70%-90%) at ages 12 and 18 months. For girls, the classification accuracy was highest at 80% at the age of 6 months, but declined thereafter to a non significant result at 12 months.

While EEG has predominantly been used with neurodevelopmental disorders such as epilepsy and seizures, the advances in technology has opened opportunities for EEG to be used to give more information about brain functioning. This study was the first demonstration of an information theoretic analysis of EEG data as a potential biomarker in infants at risk of a neurodevelopmental disorder.

The same researchers are currently testing 6-16 year-old children with autism and comparing their EEG patterns with the control. In future, they hope to follow up on the children who later develop autism and compare their EEG patterns. They encourage more studies to be conducted that will develop a greater understanding between neurological processes and cognitive function and hopefully develop a more clinically useful psychiatric biomarker for the identification of Autism and other neurodevelopmental disorders.


William Bosl, Adrienne Tierney, Helen Tager-Flusberg and Charles Nelson  (2011).EEG complexity as a biomarker for Autism Spectrum Disorder risk” BMC Med. Published online February 22, 2011.

The study was funded by grants from Autism Speaks, the National Institute on Deafness and Other Communication Disorders, and the Simons Foundation. Dr. Bosl reports being named on a provisional patent application that includes parts of the signal analysis methods used in the study. The other study authors have disclosed no relevant financial relationships.

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Rosemary Boon

Registered Psychologist

M.A. (Psych),
Grad. Dip. Ed. Studies (Sch. Counsel),
Grad Dip. Ed. B Sc, Dip. Nut.

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