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PLoS One. 2017 Aug 4;12(8):e0182657. doi: 10.1371/journal.pone.0182657. eCollection 2017.

Multivariate Bayesian decoding of single-trial event-related fMRI responses for memory retrieval of voluntary actions.

Lee D1,2, Yun S3, Jang C3, Park HJ2,3,4,5.

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Faculty of Psychology and Education Sciences, University of Coimbra, Coimbra, Portugal.
Center for Systems and Translational Brain Sciences, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea.
BK21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea.
Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
Department of Cognitive Science, Yonsei University, Seoul, Republic of Korea.


This study proposes a method for classifying event-related fMRI responses in a specialized setting of many known but few unknown stimuli presented in a rapid event-related design. Compared to block design fMRI signals, classification of the response to a single or a few stimulus trial(s) is not a trivial problem due to contamination by preceding events as well as the low signal-to-noise ratio. To overcome such problems, we proposed a single trial-based classification method of rapid event-related fMRI signals utilizing sparse multivariate Bayesian decoding of spatio-temporal fMRI responses. We applied the proposed method to classification of memory retrieval processes for two different classes of episodic memories: a voluntarily conducted experience and a passive experience induced by watching a video of others' actions. A cross-validation showed higher classification performance of the proposed method compared to that of a support vector machine or of a classifier based on the general linear model. Evaluation of classification performances for one, two, and three stimuli from the same class and a correlation analysis between classification accuracy and target stimulus positions among trials suggest that presenting two target stimuli at longer inter-stimulus intervals is optimal in the design of classification experiments to identify the target stimuli. The proposed method for decoding subject-specific memory retrieval of voluntary behavior using fMRI would be useful in forensic applications in a natural environment, where many known trials can be extracted from a simulation of everyday tasks and few target stimuli from a crime scene.

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