Decoding the infant brain: Neural predictors of language and learning difficulties in neurodiverse population

Given the central role of spoken language in human life, difficulties with speech understanding can have profound negative consequences for daily functioning. Therapeutic intervention can mitigate speech and language problems in many clinical groups, but their success depends on timely and accurate diagnosis. A promising avenue for such early diagnosis lies in analyzing the brain’s responses to speech. Recent advances made in the field of computational neuroscience that leverage machine learning techniques to analyze neural data reveals that human brain activity can track the dynamic patterns of incoming speech sounds. However, this line of research has largely centered on neurotypical adults, leaving a critical gap in our understanding of speech perception in pre-verbal infants and especially in infants with neurodevelopmental conditions. Our study addresses this gap by measuring brain responses to naturally produced speech in infants from 3 groups: those with Down syndrome, those with a high-likelihood of developing autism, and those with a low-likelihood of developing autism. Infant-directed speech materials were presented to these infants at 6 months and 12 months of age as we recorded their neural responses using electroencephalography (EEG) – a tool that has excellent temporal resolution and is relatively affordable and scalable to clinical settings. Applying machine learning techniques to the EEG data, we aim to obtain objective brain-based measures of speech processing and language understanding. The ultimate goal of this project is to develop a brain-based predictive diagnostic to identify infants that are at increased likelihood of language and learning delays. Such early identification is critical for maximizing the effectiveness of treatment options and for assessing the effectiveness of novel therapeutics.