EEG complexity as a biomarker for autism spectrum disorder risk
<p>Abstract</p> <p>Background</p> <p>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. Th...
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| Format: | Article |
| Language: | English |
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BMC
2011-02-01
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| Series: | BMC Medicine |
| Online Access: | http://www.biomedcentral.com/1741-7015/9/18 |
| _version_ | 1818563976673361920 |
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| author | Tierney Adrienne Bosl William Tager-Flusberg Helen Nelson Charles |
| author_facet | Tierney Adrienne Bosl William Tager-Flusberg Helen Nelson Charles |
| author_sort | Tierney Adrienne |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p> |
| first_indexed | 2024-12-14T01:23:22Z |
| format | Article |
| id | doaj.art-8ce152232d3c48f987f34c22c809f356 |
| institution | Directory Open Access Journal |
| issn | 1741-7015 |
| language | English |
| last_indexed | 2024-12-14T01:23:22Z |
| publishDate | 2011-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medicine |
| spelling | doaj.art-8ce152232d3c48f987f34c22c809f3562022-12-21T23:22:17ZengBMCBMC Medicine1741-70152011-02-01911810.1186/1741-7015-9-18EEG complexity as a biomarker for autism spectrum disorder riskTierney AdrienneBosl WilliamTager-Flusberg HelenNelson Charles<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p>http://www.biomedcentral.com/1741-7015/9/18 |
| spellingShingle | Tierney Adrienne Bosl William Tager-Flusberg Helen Nelson Charles EEG complexity as a biomarker for autism spectrum disorder risk BMC Medicine |
| title | EEG complexity as a biomarker for autism spectrum disorder risk |
| title_full | EEG complexity as a biomarker for autism spectrum disorder risk |
| title_fullStr | EEG complexity as a biomarker for autism spectrum disorder risk |
| title_full_unstemmed | EEG complexity as a biomarker for autism spectrum disorder risk |
| title_short | EEG complexity as a biomarker for autism spectrum disorder risk |
| title_sort | eeg complexity as a biomarker for autism spectrum disorder risk |
| url | http://www.biomedcentral.com/1741-7015/9/18 |
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