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Change‐point detection of cognitive states across multiple trials in functional neuroimaging
Author(s) -
Koerner F. Spencer,
Anderson John R.,
Fincham Jon M.,
Kass Robert E.
Publication year - 2016
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7151
Subject(s) - neuroimaging , computer science , functional magnetic resonance imaging , cognition , task (project management) , cognitive psychology , artificial intelligence , jump , machine learning , psychology , neuroscience , physics , management , quantum mechanics , economics
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.