A tool to detect cognitive abnormalities in the first year of life based on electroencephalography (EEG)

Due to motor immaturity and a lack of language, it is difficult to assess cognitive functions in the first year of life, making it difficult to properly and carefully follow up on at-risk children such as premature infants (8-10% of births), thus delaying diagnosis and adequate support.

Through the babylearn project, I developed a panel of innovant EEG procedures and experimental paradigms to explore the infant developing brain circuits and learning skills.

I therefore propose to extend this expertise to a clinical setting and provide a tool based on EEG for a comprehensive assessment of infant cognitive development.

After building an effective prototype to test key cognitive functions (syllable and face perception, temporal anticipation) and a critical learning mechanism (statistical learning) in infants while recording EEG, we will assess in the lab the stability and robustness of our measures (TRL level 4), then extend the test in a clinical setting with a normal (full-term neonates) and at-risk population (premature neonates) (TRL level 5-6).

We will validate the diagnostic utility of the device through follow-up of infants in neonatology departments.

Our proposed tool aims to significantly improve the diagnosis of neurodevelopmental disorders (NDD) in infants by allowing earlier identification in the clinically silent period of the first year.

With accurate and objective measures of infant cognitive development, this tool will enable health care professionals to provide targeted and effective care, giving children the best chance of success.