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

Authors
 Dongha Lee  ;  Sungjae Yun  ;  Changwon Jang  ;  Hae-Jeong Park 
Citation
 PLoS One, Vol.12(8) : e0182657, 2017 
Journal Title
 PLoS One 
Issue Date
2017
MeSH
Activities of Daily Living ; Algorithms ; Bayes Theorem ; Brain/physiology ; Brain Mapping/methods ; Humans ; Image Processing, Computer-Assisted/methods ; Magnetic Resonance Imaging/methods ; Mental Recall/physiology ; Models, Statistical ; Reaction Time ; Signal-To-Noise Ratio
Abstract
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.
URI
http://ir.ymlib.yonsei.ac.kr/handle/22282913/160908
DOI
10.1371/journal.pone.0182657
Appears in Collections:
1. Journal Papers (연구논문) > 1. College of Medicine (의과대학) > Dept. of Nuclear Medicine (핵의학교실)
Yonsei Authors
박해정(Park, Hae Jeong)
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