Change-point detection of cognitive states across multiple trials in functional neuroimaging.

Many functional neuroimaging-based studies involve repetitions of a task that may require several phases, or states, of mental activity. An appealing idea is to use relevant brain regions to identify the states. We developed a novel change-point methodology that adapts to the repeated trial structure of such experiments by assuming the number of states stays fixed across similar trials while allowing the timing of change-points to change across trials. Model fitting is based on reversible-jump MCMC. Simulation studies verified its ability to identify change-points successfully. We applied this technique to data collected via functional magnetic resonance imaging (fMRI) while each of 20 subjects solved unfamiliar arithmetic problems. Our methodology supplies both a summary of state dimensionality and uncertainty assessments about number of states and the timing of state transitions.