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)
preprintposted on 2021-08-13, 18:47 authored by Patrick C. M. Wong, Ching Man Lai, Peggy H. Y. Chan, Ting Fan Leung, Hugh Simon Lam, Gangyi Feng, Akshay R. Maggu, Nikolay Novitskiy
This manuscript has been peer reviewed and accepted for publication in the American Journal of Speech-Language Pathology.
Purpose: 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 < 25th, 25th – 75th, and > 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 <.01 for all models).
Conclusions: Future language and communication variability can be predicted by an objective EEG method that indicates the function of the auditory neural pathway foundational to spoken language development, with precision sufficient for individual predictions. Longer-term research is needed to assess predictability of categorical diagnostic status.
Wong, P. C. M., Lai, C. M., Chan, P. H. Y., Leung, T. F., Lam, H. S., Feng, G., Maggu, A. R., & Novitskiy, N. (2021). Neural speech encoding in infancy predicts future language and communication difficulties. ASHA Journals: CSD Preprints. https://doi.org/10.23641/asha.14896809
This study was supported by the Innovation and Technology Fund of the Hong Kong SAR Government (ITS/067/18), including funding from its research talent program (InP/285/19, InP/286/19, PiH/030/19, and PiH/034/19). Support from the Dr. Stanley Ho Medical Development Foundation for the Stanley Ho Developmental Cohort Study is also acknowledged.
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neuralspeechencodinginfancylanguagecommunicationdifficultiesinfantpredictimpairmentdevelopmentbrainneurologyauditory brainstem responseearly detectionearly interventionprognosticidentifyelectroencephalographyEEGfundamental frequencyoutcomesMacArthur-Bates Communicative Development InventoriesMCDIChinesemachine learninggesturevocabularycomprehensionproductionLinguistic Processes (incl. Speech Production and Comprehension)Laboratory Phonetics and Speech ScienceNeurocognitive Patterns and Neural Networks