Abstract
BackgroundComplex 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.MethodsUsing 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.ResultsMultiscale 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.ConclusionsThis 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.
Highlights
Introduction to the Analysis of PhysiologicalSignals Burlington, MA: Academic Press; 2007.15
Participants Data were collected from 79 different infants: 46 who were at high risk for autism spectrum disorder (ASD), defined on the basis of having an older sibling with a confirmed diagnosis of ASD, and 33 controls, defined on the basis of a typically developing older sibling and no family history of neurodevelopmental disorders
Local neural network connectivity undergoes rapid change during early development, and this may be reflected in the multiscale entropy of EEG signals, which is one measure of signal complexity that has been associated with health and disease [23]
Summary
Introduction to the Analysis of PhysiologicalSignals Burlington, MA: Academic Press; 2007.15. 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 underlying brain dysfunction that results in the behavioral characteristics is not well understood Complex mental disorders such as autism cannot be described as being associated with underconnectivity or overconnectivity, but may involve some form of abnormal connectivity that varies between different brain regions [6]. Coarse-grained entropy synchronization between electroencephalography (EEG) electrodes revealed that synchronization was significantly lower in children with autism than in a group of typically developing children [8], supporting the theory that autistic brains exhibit low functional connectivity. Estimation of changes in neural connectivity might be an effective diagnostic marker for atypical connectivity development
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