Unifying structural and functional brain development with genetic influences and cognition is an overarching aim of developmental neuroscience. Structural and functional organisation, using diffusion-weighed and functional magnetic resonance imaging, respectively, can be inferred by considering each as graph-theory-based networks (connectomes) made up of nodes (brain regions) connected by edges (vertices). However, such descriptive approaches lack accounts of how organisation arises. Generative network models (GNM) may address this, using pre-defined wiring rules to simulate connectivity by adding a new connection stochastically at each iteration from a sparse network, until the target is reached. Whilst GNMs have a major advantage of explaining how brain connectivity arises in the first instance, its biological plausibility is questionable, and the extent to which such models are more useful than simpler alternatives is unclear. Therefore, whilst a useful first-year project, she hopes to use this placement to expand upon the above by creating a comparison of the predictive validity of different brain-modelling approaches for cognition, such as GNMs against simpler graph theory metrics. Second, she hopes to improve GNM biological plausibility by incorporating the possibility of multiple connections being added at a single time point, biasing connection formation using neurochemical gradient data, and using longitudinal models. Third, she will explore how structure-function relationships differ across different childhood developmental timepoints and attempt to extend existing GNMs to structure-function GNMs. In doing so, I hope to capture structure-function interactions as an additional metric of brain development. Fourth, she will incorporate transcriptomic and epigenetic insights into structural and functional brain development, such as combining genetic data from the Human Brain Transcriptome Project with brain imaging datasets.