Bayesian approaches in Neuroimaging

Simon White (MRC Biostatistics Unit, University of Cambridge)

Frank Adams 2,

Modern neuroimaging studies are recruiting greater numbers of individuals and the amount of data generated is truly 'big data'; and it will only grow with the next generation of high resolution scanners. However, the many uncertainties in how to analyse and model these data are also significant. We consider Bayesian approaches to three neuroscience questions: the so-called compensation hypothesis, mapping functional connectivity, and linking neuroimaging and behavioural data. There are computational issues to consider, but we hope to better capture uncertainty.

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