Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months

Abstract Background Early identification of autism spectrum disorder (ASD) provides an opportunity for early intervention and improved developmental outcomes. The use of electroencephalography (EEG) in infancy has shown promise in predicting later ASD diagnoses and in identifying neural mechanisms u...

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Autores principales:	Fleming C. Peck, Laurel J. Gabard-Durnam, Carol L. Wilkinson, William Bosl, Helen Tager-Flusberg, Charles A. Nelson
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	EEG Autism Language development Machine learning Infant Sensitive period Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/c9ef04b660b94503944c06ab407cdf9b
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spelling	oai:doaj.org-article:c9ef04b660b94503944c06ab407cdf9b2021-12-05T12:04:17ZPrediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months10.1186/s11689-021-09405-x1866-19471866-1955https://doaj.org/article/c9ef04b660b94503944c06ab407cdf9b2021-11-01T00:00:00Zhttps://doi.org/10.1186/s11689-021-09405-xhttps://doaj.org/toc/1866-1947https://doaj.org/toc/1866-1955Abstract Background Early identification of autism spectrum disorder (ASD) provides an opportunity for early intervention and improved developmental outcomes. The use of electroencephalography (EEG) in infancy has shown promise in predicting later ASD diagnoses and in identifying neural mechanisms underlying the disorder. Given the high co-morbidity with language impairment, we and others have speculated that infants who are later diagnosed with ASD have altered language learning, including phoneme discrimination. Phoneme learning occurs rapidly in infancy, so altered neural substrates during the first year of life may serve as early, accurate indicators of later autism diagnosis. Methods Using EEG data collected at two different ages during a passive phoneme task in infants with high familial risk for ASD, we compared the predictive accuracy of a combination of feature selection and machine learning models at 6 months (during native phoneme learning) and 12 months (after native phoneme learning), and we identified a single model with strong predictive accuracy (100%) for both ages. Samples at both ages were matched in size and diagnoses (n = 14 with later ASD; n = 40 without ASD). Features included a combination of power and nonlinear measures across the 10‑20 montage electrodes and 6 frequency bands. Predictive features at each age were compared both by feature characteristics and EEG scalp location. Additional prediction analyses were performed on all EEGs collected at 12 months; this larger sample included 67 HR infants (27 HR-ASD, 40 HR-noASD). Results Using a combination of Pearson correlation feature selection and support vector machine classifier, 100% predictive diagnostic accuracy was observed at both 6 and 12 months. Predictive features differed between the models trained on 6- versus 12-month data. At 6 months, predictive features were biased to measures from central electrodes, power measures, and frequencies in the alpha range. At 12 months, predictive features were more distributed between power and nonlinear measures, and biased toward frequencies in the beta range. However, diagnosis prediction accuracy substantially decreased in the larger, more behaviorally heterogeneous 12-month sample. Conclusions These results demonstrate that speech processing EEG measures can facilitate earlier identification of ASD but emphasize the need for age-specific predictive models with large sample sizes to develop clinically relevant classification algorithms.Fleming C. PeckLaurel J. Gabard-DurnamCarol L. WilkinsonWilliam BoslHelen Tager-FlusbergCharles A. NelsonBMCarticleEEGAutismLanguage developmentMachine learningInfantSensitive periodNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENJournal of Neurodevelopmental Disorders, Vol 13, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	EEG Autism Language development Machine learning Infant Sensitive period Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	EEG Autism Language development Machine learning Infant Sensitive period Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Fleming C. Peck Laurel J. Gabard-Durnam Carol L. Wilkinson William Bosl Helen Tager-Flusberg Charles A. Nelson Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months
description	Abstract Background Early identification of autism spectrum disorder (ASD) provides an opportunity for early intervention and improved developmental outcomes. The use of electroencephalography (EEG) in infancy has shown promise in predicting later ASD diagnoses and in identifying neural mechanisms underlying the disorder. Given the high co-morbidity with language impairment, we and others have speculated that infants who are later diagnosed with ASD have altered language learning, including phoneme discrimination. Phoneme learning occurs rapidly in infancy, so altered neural substrates during the first year of life may serve as early, accurate indicators of later autism diagnosis. Methods Using EEG data collected at two different ages during a passive phoneme task in infants with high familial risk for ASD, we compared the predictive accuracy of a combination of feature selection and machine learning models at 6 months (during native phoneme learning) and 12 months (after native phoneme learning), and we identified a single model with strong predictive accuracy (100%) for both ages. Samples at both ages were matched in size and diagnoses (n = 14 with later ASD; n = 40 without ASD). Features included a combination of power and nonlinear measures across the 10‑20 montage electrodes and 6 frequency bands. Predictive features at each age were compared both by feature characteristics and EEG scalp location. Additional prediction analyses were performed on all EEGs collected at 12 months; this larger sample included 67 HR infants (27 HR-ASD, 40 HR-noASD). Results Using a combination of Pearson correlation feature selection and support vector machine classifier, 100% predictive diagnostic accuracy was observed at both 6 and 12 months. Predictive features differed between the models trained on 6- versus 12-month data. At 6 months, predictive features were biased to measures from central electrodes, power measures, and frequencies in the alpha range. At 12 months, predictive features were more distributed between power and nonlinear measures, and biased toward frequencies in the beta range. However, diagnosis prediction accuracy substantially decreased in the larger, more behaviorally heterogeneous 12-month sample. Conclusions These results demonstrate that speech processing EEG measures can facilitate earlier identification of ASD but emphasize the need for age-specific predictive models with large sample sizes to develop clinically relevant classification algorithms.
format	article
author	Fleming C. Peck Laurel J. Gabard-Durnam Carol L. Wilkinson William Bosl Helen Tager-Flusberg Charles A. Nelson
author_facet	Fleming C. Peck Laurel J. Gabard-Durnam Carol L. Wilkinson William Bosl Helen Tager-Flusberg Charles A. Nelson
author_sort	Fleming C. Peck
title	Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months
title_short	Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months
title_full	Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months
title_fullStr	Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months
title_full_unstemmed	Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months
title_sort	prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related eeg at 6 and 12 months
publisher	BMC
publishDate	2021
url	https://doaj.org/article/c9ef04b660b94503944c06ab407cdf9b
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Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months

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