Grant: Project Grant

Neuroimaging-based characterization of typical brain development is of utmost importance to form a baseline for understanding cognitive development and its deviations in neurodevelopmental disorders. Many groups including ours have addressed this question by building prediction models of brain development. However, previous studies focused on 'static' brain measures although there is a need for considering trajectories as opposed to static 'final' brain measures. We need to leverage the availability of large-scale open longitudinal datasets to build prediction models of individual variations in brain and cognitive development. Our first aim is to explore whether developmental changes in individual brains can be used to predict future cognition. Using longitudinal data, we will use within-subject developmental changes in brain in prediction models of cognitive outcomes. Although generative models have been proposed for developmental changes in the brain. However, previous studies lack one important component: they have modeled developmental changes in brain connections, but not brain structure. Brain structure also changes during development; as such generative models need to take into account both brain structure as well as connections. Our second aim is to explore models used in complex systems, particularly co-evolving network models that consider changes in both nodes and edges with time. Furthermore, although previous studies indicate the utility of studying brain trajectories, little is known whether social factors such as the socioeconomic conditions influence these brain trajectories. By studying the epigenetic mechanisms behind the influence of children's environment on their neurodevelopmental trajectories, we can gain insights into the biological processes. In collaboration with Dr. Linda Booij, our third aim is to investigate epigenetic mechanisms underlying socioeconomic conditions and deviant neurodevelopmental trajectories in children.