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

• Published in: Statistics in medicine Vol. 36; no. 4; pp. 618 - 642
• Main Authors: Koerner, F. Spencer, Anderson, John R., Fincham, Jon M., Kass, Robert E.
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 20.02.2017; Wiley Subscription Services, Inc
• Subjects: Algorithms; Bayesian inference; Brain; Brain - diagnostic imaging; Brain - physiology; change‐point detection; Clinical trials; Clinical Trials as Topic; Cognition & reasoning; Cognition - physiology; Data Interpretation, Statistical; functional magnetic resonance imaging; Functional Neuroimaging; Humans; Magnetic Resonance Imaging; Markov analysis; Models, Statistical; Monte Carlo simulation; Neuropsychology; NMR; Nuclear magnetic resonance; Principal Component Analysis; reversible‐jump MCMC; segmentation; Uncertainty; change-point detection; segmentation; reversible-jump MCMC; Bayesian inference; functional magnetic resonance imaging
• ISSN: 0277-6715; 1097-0258
• DOI: 10.1002/sim.7151

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. Copyright © 2016 John Wiley & Sons, Ltd.