Neural Speech Encoding in Infancy Predicts Future Language and Communication Difficulties (Patrick C. M. Wong, Ching Man Lai, Peggy H. Y. Chan, Ting Fan Leung, Hugh Simon Lam, Gangyi Feng, Akshay R. Maggu, & Nikolay Novitskiy)

Abstract

This manuscript has been peer reviewed and accepted for publication in the American Journal of Speech-Language Pathology.AbstractPurpose: To construct an objective, and cost-effective prognostic tool to forecast the future language and communication abilities of individual infants.Method: Speech-evoked electroencephalography (EEG) data were collected from 118 infants during the first year of life during the exposure to speech stimuli that differed principally in fundamental frequency. Language and communication outcomes, namely four subtests of the MacArthur-Bates Communicative Development Inventories (MCDI)—Chinese version, were collected between 3 to 16 months after initial EEG testing. In the two-way classification, children were classified into those with future MCDI scores below the 25th percentile for their age group and those above the same percentile, while the three-way classification classified them into 75th percentile groups. Machine learning (support vector machine classification) with cross-validation was used for model construction. Statistical significance was assessed.Results: Across the four MCDI measures of early gestures, later gestures, vocabulary comprehension, and vocabulary production, the areas under the receiver-operating characteristic curve (AUC) of the predictive models were respectively .92 ± .031, .91 ± .028, .90 ± .035, and .89 ± .039 for the two-way classification, and .88 ± .041, .89 ± .033, .85 ± .047 and .85 ± .050 for the three-way classification (p