Generative dynamical models for classification of rsfMRI data

Neuroimaging researchers have made substantial progress in using brain data to predict psychological and behavioral variables, like personality, cognitive abilities, and neurological and psychiatric diagnoses. In general, however, these prediction approaches do not take account of how brain activity changes over time. In this study, we use hidden Markov models, a simple and generic model for dynamic processes, to perform brain-based prediction. We show that hidden Markov models can successfully distinguish whether a single individual had eaten and consumed caffeine before his brain scan. These models also show some promise for "fingerprinting," or identifying individuals solely on the basis of their brain scans. This study demonstrates that hidden Markov models are a promising tool for neuroimaging-based prediction.