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

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dc.contributor.author박해정-
dc.date.accessioned2018-07-20T08:09:36Z-
dc.date.available2018-07-20T08:09:36Z-
dc.date.issued2017-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/160908-
dc.description.abstractThis 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.-
dc.description.statementOfResponsibilityopen-
dc.languageEnglish-
dc.publisherPublic Library of Science-
dc.relation.isPartOfPLOS ONE-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/2.0/kr/-
dc.subject.MESHActivities of Daily Living-
dc.subject.MESHAlgorithms-
dc.subject.MESHBayes Theorem-
dc.subject.MESHBrain/physiology-
dc.subject.MESHBrain Mapping/methods-
dc.subject.MESHHumans-
dc.subject.MESHImage Processing, Computer-Assisted/methods-
dc.subject.MESHMagnetic Resonance Imaging/methods-
dc.subject.MESHMental Recall/physiology-
dc.subject.MESHModels, Statistical-
dc.subject.MESHReaction Time-
dc.subject.MESHSignal-To-Noise Ratio-
dc.titleMultivariate Bayesian decoding of single-trial event-related fMRI responses for memory retrieval of voluntary actions.-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine-
dc.contributor.departmentDept. of Nuclear Medicine-
dc.contributor.googleauthorDongha Lee-
dc.contributor.googleauthorSungjae Yun-
dc.contributor.googleauthorChangwon Jang-
dc.contributor.googleauthorHae-Jeong Park-
dc.identifier.doi10.1371/journal.pone.0182657-
dc.contributor.localIdA01730-
dc.relation.journalcodeJ02540-
dc.identifier.eissn1932-6203-
dc.identifier.pmid28777830-
dc.contributor.alternativeNamePark, Hae Jeong-
dc.contributor.affiliatedAuthorPark, Hae Jeong-
dc.citation.volume12-
dc.citation.number8-
dc.citation.startPagee0182657-
dc.identifier.bibliographicCitationPLOS ONE, Vol.12(8) : e0182657, 2017-
dc.identifier.rimsid60787-
dc.type.rimsART-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Nuclear Medicine (핵의학교실) > 1. Journal Papers

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