Graphical models with latent variables and their application in neuropsychology

In this talk we propose a strategy of statistical inference for graphical models with latent Gaussian variables, and observed variables that follow non-standard sampling distributions. We restrict our attention to those graphs in which the latent variables have a substantive interpretation. In addition, we adopt the assumption that the distribution of the observed variables may be meaningfully interpreted as arising after marginalising over the latent variables. We provide two examples of longitudinal studies that investigate developmental changes in cognitive functions of young children in one case and of cognitive decline of Alzheimer s patients in the other. These studies involve the assessment of competing causal models for several psychological constructs; and the observed measurements are gathered from the administration of batteries of tasks subject to complicated sampling protocols. Finally, we extend our strategy to the analysis of EEG (electroencephalogram) brain responses. As a motivating example, we discuss a longitudinal study that investigates interrelations between cortical regions that are thought to underlie the neural mechanisms of resistance to peer influence during adolescence.