Toward the Study of the Neural-Underpinnings of Dyslexia During Final-Phoneme Elision: A Machine Learning Approach

Abstract

Identifying the neural basis of dyslexia is a fundamental goal of developmental neuroscience. Final-phoneme elision (PE) test is a paradigm used for assessing phonological deficit (PD), which is widely considered a causal risk factor for dyslexia. However, the causal relationship between PD to dyslexia has been examined primarily based on behavioral observations. Towards facilitating the exploration of the neurophysiological origins of the theorized link between PD and dyslexia, we set out to isolate differential neural activation patterns in children with dyslexia during PE. Accordingly, we present a machine-learning-based approach to identifying differential brain activity in children with dyslexia and controls during the PE. Our method formulates an optimization problem to extract informative EEG components based on the ‘Neural-congruency hypothesis’, termed Phoneme-related Neural-congruency components. It then uses a machine-learning algorithm to optimally combine the resulting components to differentiate between the neural activity of children with dyslexia and controls. We apply our approach to a real EEG dataset involving children with dyslexia and controls. Our findings demonstrate that our method generates novel insights into the neural underpinnings of dyslexia and the potential neural origins of phonological deficits as a causal factor of dyslexia. Notably, our approach overcomes several methodological challenges in conventional EEG analysis methods; therefore, it could be utilized in studying the neural origins of other behaviorally defined developmental disorders previously overlooked because of such methodological constraints.