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Identifying Dyslexia Earlier in Life.

Diagnosing dyslexia relies on a waiting-to-fail approach, i.e., the diagnosis relies on persistent struggles with reading and writing after some years of reading instruction. By earlier identifying children with a high risk for developing dyslexia, one could start earlier and more effective intervention so that the impact of dyslexia on an individual's life is reduced. Although dyslexia manifests as a reading and writing disorder, it is thought to be rooted in reduced auditory temporal processing. Therefore, investigating the neural responses to sounds might be key to identifying dyslexia earlier in life. We aim to distinguish children with dyslexia from neurotypical children using a wide set of neural metrics derived from two novel approaches, i.e., (1) neural tracking and (2) functional connectivity measures applied to M/EEG data of children listening to natural speech. These approaches allow us to investigate the temporal characteristics of the neural responses and how these are related to the neural connectivity networks across the hierarchical levels of neural speech processing ranging from purely acoustic processing to linguistic speech processing. Using the combination of these approaches on different unique datasets allows us to identify dyslexia objectively using neural metrics.
Date:1 Nov 2023 →  Today
Keywords:dyslexia, neural speech processing, neuro-imaging
Disciplines:Biomedical image processing, Biomedical signal processing, Audio and speech computing, Audiology