Bayesian approaches for capturing the heterogeneity of neuroimaging experiments

In the neurosciences, it is now widely established that brain processes are characterized by heterogeneity at several levels. For example, neuronal processes differ by external stimuli, and patterns of brain activations vary across subjects. In this paper, we will discuss a few Bayesian strategies for characterizing heterogeneity in the neurosciences, where time-series data are assumed to be organized in differ- ent, but related, units (e.g., neurons and/or regions of interest) and some sharing of information is required to learn distinctive features of the units. First, we will discuss models for multi-subject analysis that will identify population subgroups character- ized by similar brain activity patterns, also by integrating available subject informa- tion. Then, we will look at how novel techniques in intracellular calcium signals may be used to analyze neuronal responses to external stimuli in awake animals. Finally, we will discuss a mixture framework for identifying differentially activated brain regions that can classify the brain regions into several tiers with varying degrees of relevance. The performance of the models will be demonstrated by applications to data from human fMRI and animal fluorescence microscopy experiments.