Transforming neonatal neurodevelopmental care: Precision prediction through eeg-based digital twins.

Neurodevelopmental disorders (NDs) pose a significant global public health challenge, affecting millions of children worldwide, with premature infants being particularly vulnerable. Current care pathways often involve delayed diagnosis and missed opportunities for early intervention. To address this, we propose an innovative approach using electroencephalography (EEG)-based digital twins (DTs) to transform neonatal neurodevelopmental care through quantitative, precision prediction. Our vision is to develop mathematical models (DTs) derived from EEG that can quantify, predict, and understand neurodevelopment. These DTs will simulate individual infants' brain maturation trajectories, enabling fine-scale quantitative prediction of typical and atypical neurodevelopmental outcomes. This predictive capability will guide follow-up assessments, inform resource allocation, and facilitate personalised interventions. The project has three key aims: Foster a dynamic, cross-disciplinary team to advance the construction and clinical use of DTs for childhood brain development, while developing future leaders in AI for healthcare. Collect longitudinal EEG data to calibrate models at different time points, ensuring model validity and understanding practical DT deployment. Develop DTs capable of simulating brain developmental trajectories with quantified uncertainty. To achieve this we need to develop new mathematical models of infant brains at different stages of neurodevelopment, addressing a current gap in this field. Another crucial component of our work involves matching model output to EEG (model calibration), which presents challenges due to the complex nature of the brain itself and models of the brain, which are nonlinear, non-identifiable, and stochastic. Crucially, we will develop novel calibration methods that enable us to quantify uncertainty in predictions. Our interdisciplinary team combines expertise in mathematics, paediatrics, statistics, data ethics, and industry. We will collaborate with Huru, an industrial partner developing innovative wireless EEG recording solutions, to facilitate data collection and explore potential clinical applications. Our work has significant potential for societal impact. By enabling early diagnosis, individualised prognosis, and personalised treatment planning, our DT technology will provide a powerful tool for precision medicine in neonatal care. This could lead to improved outcomes, enhanced quality of life, and substantial societal and economic benefits through optimised neurodevelopmental outcomes at a population level. In conclusion, our project aims to transform neonatal neurodevelopmental care through the development of EEG-based digital twins. By combining mathematical modelling, clinical expertise, and innovative technology, we seek to enable earlier identification of at-risk infants, provide quantitative predictions of developmental trajectories, and support informed decision-making and personalised interventions. This groundbreaking approach has the potential to significantly improve outcomes for infants at risk of neurodevelopmental disorders and their families.