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The use of linear mixed-effect models is becoming increasingly popular in neuroimaging. These models extend the simple linear models by allowing both fixed and random effects, and are particularly useful for clustered and hierarchical data. These are the materials from a session held at the MRC Cognition and Brain Sciences Unit, Cambridge, UK, where Delia introduced the concept of linear mixed-effect models and discussed when they should be used. Roni then demonstrated how these are implemented with R. Alex reviewed some advanced topics, including estimation of parameters (family parameters and random effects) as well as the use of Bayesian statistics.