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

Journal of Neurodevelopmental Disorders

William Bosl¹  Helen Tager-Flusberg²  Carol L. Wilkinson³  Fleming C. Peck⁴  Laurel J. Gabard-Durnam⁵  Charles A. Nelson⁶ 

[1] Computational Health Informatics Program, Boston Children’s Hospital, Harvard Medical School, 02115, Boston, MA, USA;Health Informatics Program, University of San Francisco, 94117, San Francisco, CA, USA;Department of Psychological and Brain Sciences, Boston University, 02215, Boston, MA, USA;Division of Developmental Medicine, Boston Children’s Hospital, Harvard Medical School, 02115, Boston, MA, USA;Division of Developmental Medicine, Boston Children’s Hospital, Harvard Medical School, 02115, Boston, MA, USA;Department of Neurology, Boston Children’s Hospital, Harvard Medical School, 02115, Boston, MA, USA;Princeton Neuroscience Institute, Princeton University, 08544, Princeton, NJ, USA;Division of Developmental Medicine, Boston Children’s Hospital, Harvard Medical School, 02115, Boston, MA, USA;Department of Psychology, Northeastern University, 02118, Boston, MA, USA;Division of Developmental Medicine, Boston Children’s Hospital, Harvard Medical School, 02115, Boston, MA, USA;Harvard Graduate School of Education, 02138, Cambridge, MA, USA;

关键词: EEG;    Autism;    Language development;    Machine learning;    Infant;    Sensitive period;   

DOI  :  10.1186/s11689-021-09405-x

来源: Springer

【 摘 要 】

BackgroundEarly 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.MethodsUsing 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).ResultsUsing 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.ConclusionsThese 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.

【 授权许可】

CC BY