Open AccessSpatiotemporal analysis of event‐related fMRI to reveal cognitive states
Author(s) -
Fincham Jon M.,
Lee Hee Seung,
Anderson John R.
Publication year - 2020
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.24831
Subject(s) - functional magnetic resonance imaging , cognition , neuroimaging , task (project management) , artificial intelligence , computer science , cognitive psychology , hidden markov model , cognitive neuroscience , psychology , machine learning , pattern recognition (psychology) , neuroscience , engineering , systems engineering
Cognitive science has a rich history of developing theories of processing that characterize the mental steps involved in performance of many tasks. Recent work in neuroimaging and machine learning has greatly improved our ability to link cognitive processes with what is happening in the brain. This article analyzes a hidden semi‐Markov model‐multivoxel pattern‐analysis (HSMM‐MVPA) methodology that we have developed for inferring the sequence of brain states one traverses in the performance of a cognitive task. The method is applied to a functional magnetic resonance imaging (fMRI) experiment where task boundaries are known that should separate states. The method is able to accurately identify those boundaries. Then, applying the method to synthetic data, we explore more fully those factors that influence performance of the method: signal‐to‐noise ratio, numbers of states, state sojourn times, and numbers of underlying experimental conditions. The results indicate the types of experimental tasks where applications of the HSMM‐MVPA method are likely to yield accurate and insightful results.