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Multivariate Bayesian decoding of single-trial event-related fMRI responses for memory retrieval of voluntary actions.
DC Field | Value | Language |
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dc.contributor.author | 박해정 | - |
dc.date.accessioned | 2018-07-20T08:09:36Z | - |
dc.date.available | 2018-07-20T08:09:36Z | - |
dc.date.issued | 2017 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/160908 | - |
dc.description.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. | - |
dc.description.statementOfResponsibility | open | - |
dc.language | English | - |
dc.publisher | Public Library of Science | - |
dc.relation.isPartOf | PLOS ONE | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.rights | https://creativecommons.org/licenses/by-nc-nd/2.0/kr/ | - |
dc.subject.MESH | Activities of Daily Living | - |
dc.subject.MESH | Algorithms | - |
dc.subject.MESH | Bayes Theorem | - |
dc.subject.MESH | Brain/physiology | - |
dc.subject.MESH | Brain Mapping/methods | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Image Processing, Computer-Assisted/methods | - |
dc.subject.MESH | Magnetic Resonance Imaging/methods | - |
dc.subject.MESH | Mental Recall/physiology | - |
dc.subject.MESH | Models, Statistical | - |
dc.subject.MESH | Reaction Time | - |
dc.subject.MESH | Signal-To-Noise Ratio | - |
dc.title | Multivariate Bayesian decoding of single-trial event-related fMRI responses for memory retrieval of voluntary actions. | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine | - |
dc.contributor.department | Dept. of Nuclear Medicine | - |
dc.contributor.googleauthor | Dongha Lee | - |
dc.contributor.googleauthor | Sungjae Yun | - |
dc.contributor.googleauthor | Changwon Jang | - |
dc.contributor.googleauthor | Hae-Jeong Park | - |
dc.identifier.doi | 10.1371/journal.pone.0182657 | - |
dc.contributor.localId | A01730 | - |
dc.relation.journalcode | J02540 | - |
dc.identifier.eissn | 1932-6203 | - |
dc.identifier.pmid | 28777830 | - |
dc.contributor.alternativeName | Park, Hae Jeong | - |
dc.contributor.affiliatedAuthor | Park, Hae Jeong | - |
dc.citation.volume | 12 | - |
dc.citation.number | 8 | - |
dc.citation.startPage | e0182657 | - |
dc.identifier.bibliographicCitation | PLOS ONE, Vol.12(8) : e0182657, 2017 | - |
dc.identifier.rimsid | 60787 | - |
dc.type.rims | ART | - |
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