Neural Speech Encoding in Infancy Predicts Future Language and Communication Difficulties

Abstract

Purpose This study aimed 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 and 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 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. Supplemental Material https://doi.org/10.23641/asha.15138546