EEG complexity as a biomarker for autism spectrum disorder risk

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EEG complexity as a biomarker for autism spectrum disorder risk

Bosl email, Adrienne Tierney email, Helen Tager-Flusberg email and

email

BMC Medicine 2011, 9:18doi:10.1186/1741-7015-9-18

Published: 22 February 2011

Abstract (provisional)

Background

Complex neurodevelopmental disorders may be characterized by subtle brain

function signatures early in life before behavioral symptoms are apparent. Such

endophenotypes may be measurable biomarkers for later cognitive impairments. The

nonlinear complexity of electroencephalography (EEG) signals is believed to

contain information about the architecture of the neural networks in the brain

on many scales. Early detection of abnormalities in EEG signals may be an early

biomarker for developmental cognitive disorders. The goal of this paper is to

demonstrate that the modified multiscale entropy (mMSE) computed on the basis of

resting state EEG data can be used as a biomarker of normal brain development

and 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.

Methods

Using mMSE as a feature vector, a multiclass support vector machine algorithm

was used to classify typically developing and high-risk groups. Classification

was computed separately within each age group from 6 to 24 months.

Results

Multiscale entropy appears to go through a different developmental trajectory in

infants at high risk for autism (HRA) than it does in typically developing

controls. Differences appear to be greatest at ages 9 to 12 months. Using

several machine learning algorithms with mMSE as a feature vector, infants were

classified with over 80% accuracy into control and HRA groups at age 9 months.

Classification accuracy for boys was close to 100% at age 9 months and remains

high (70% to 90%) at ages 12 and 18 months. For girls, classification accuracy

was highest at age 6 months, but declines thereafter.

Conclusions

This proof-of-principle study suggests that mMSE computed from resting state EEG

signals may be a useful biomarker for early detection of risk for ASD and

abnormalities in cognitive development in infants. To our knowledge, this is the

first demonstration of an information theoretic analysis of EEG data for

biomarkers in infants at risk for a complex neurodevelopmental disorder.