The human brain is a highly complex organ that integrates functionally specialised subunits. Underpinning this complexity and functional specialisation is a network of structural connections, which may be probed using diffusion tractography, a unique, powerful and non-invasive MRI technique. Estimates of brain connectivity derived through diffusion tractography allow for explorations of how the brain’s functional subunits are inter-linked to subsequently produce experiences and behaviour.
This thesis develops new diffusion tractography methodology for mapping brain connectivity, both across individuals and also across species; and explores frameworks for discovering associations of such brain connectivity features with behavioural traits. We build upon the hypothesis that connectional patterns can probe regions of functional equivalence across brains. To test this hypothesis we develop standardised and automated frameworks for mapping these patterns in very diverse brains, such as from human and non-human primates. We develop protocols to extract homologous fibre bundles across two species (human and macaque monkeys). We demonstrate robustness and generalisability of these protocols, but also their ability to capture individual variability. We also present investigations into how structural connectivity profiles may be used to inform us of how functionally-related features can be linked across different brains. Further, we explore how fully data-driven tractography techniques may be utilised for similar purposes, opening the door for future work on data-driven connectivity mapping.
Subsequently, we explore how such individual variability in features that probe brain organisation are associated with differences in human behaviour. One approach to performing such explorations is the use of powerful multivariate statisitical techniques, such as canonical correlation analysis (CCA). After identifying issues in out-of-sample replication using multi-modal connectivity information, we perform comprehensive explorations into the robustness of such techniques and devise a generative model for forward predictions, demonstrating significant challlenges and limitations in their current applications. Specifically, we predict that the stability and generalisability of these techniques requires an order of magnitude more subjects than typically used to avoid overfitting and mis-interpretation of results. Using population-level data from the UK Biobank and confirmations from independent imaging modalities from the Human Connectome Project, we validate this prediction and demonstrate the direct link of CCA stability and generalisability with the number of subjects used per considered feature
