Early detection of developmental dyslexia (DD), a specific reading disorder, will enable interventions at an early age, before the onset of formal reading and spelling instruction. Although deviations in early speech/language development have frequently been related to (risk of) DD, none of these markers (vocabulary; auditory brain responses (EEG)) have been successfully used to predict later language/literacy performance at the individual level.
Machine learning (ML) is a technique capable of discovering patterns in data to make such predictions. In the past decade ML has been successfully employed to predict psychosis, disease-course, age, IQ, etc. from measurements such as MRI and EEG. Our project will use ML to explore if EEG data on speech processing in infancy can predict the occurrence of later literacy difficulties in individual children.
Application of ML to EEG recordings in infants at risk of DD and low-risk controls requires unconventional, beyond state-of-the-art eScience solutions. An important step of this project is validation of the discriminating patterns in an independent sample from our international collaborators. If successful, this research will open the way to further investigate the problem of prediction of (ab)normal development, ranging from DD, Specific Language Impairment, to autism and psychotic disorders such as schizophrenia.