Age-related changes in brain connectivity and cognition studied through machine learning

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This PhD project aims to study how the brain/cognition relationship changes across lifespan, using an open data archive containing neuroimaging data from 600+ participants (Cambridge Centre for Ageing Neuroscience). The project will combine information obtained from multiple brain imaging methods:1. Functional MRI (fMRI), which measures brain activity indirectly through its effects on blood vessels;2. Magnetoencephalography (MEG), which measures the magnetic field around the head generated by brain activity as a direct measure of neural activation;3. Diffusion-weighted MRI (DWI), which measures the anatomical "wiring" of the brain; and4. Structural MRI (sMRI), which measures shape-related properties of cortical folding and subcortical structure.To combine information from the various neuroimaging modalities, several approaches will be explored, among them methods based on recent theoretical advances in complex networks that employ multilayer networks to describe multiple interacting network structures simultaneously. Machine learning methods, including neural networks and relevance vector machines, will be used to determine which aspects of brain networks (across all imaging modalities studied) best predict individual cognitive ability. Finally, this approach will be used to test existing theories of how brain networks reorganize with age, with hypotheses about age-related changes of brain lateralization and about shifts between anterior and posterior brain activation.