Brain-State Extraction Algorithm Based on the State Transition (BEST): A Dynamic Functional Brain Network Analysis in fMRI Study

Cited 1 time in webofscience Cited 0 time in scopus
  • Hit : 30
  • Download : 0
DC FieldValueLanguage
dc.contributor.authorLee, Young Beomko
dc.contributor.authorYoo, Kwangsunko
dc.contributor.authorRoh, Jee Hoonko
dc.contributor.authorMoon, Won-Jinko
dc.contributor.authorJeong, Yongko
dc.date.accessioned2019-10-02T10:21:10Z-
dc.date.available2019-10-02T10:21:10Z-
dc.date.created2019-10-01-
dc.date.issued2019-09-
dc.identifier.citationBRAIN TOPOGRAPHY, v.32, no.5, pp.897 - 913-
dc.identifier.issn0896-0267-
dc.identifier.urihttp://hdl.handle.net/10203/267743-
dc.description.abstractSpatial pattern of the brain network changes dynamically. This change is closely linked to the brain-state transition, which vary depending on a dynamic stream of thoughts. To date, many dynamic methods have been developed for decoding brain-states. However, most of them only consider changes over time, not the brain-state transition itself. Here, we propose a novel dynamic functional connectivity analysis method, brain-state extraction algorithm based on state transition (BEST), which constructs connectivity matrices from the duration of brain-states and decodes the proper number of brain-states in a data-driven way. To set the duration of each brain-state, we detected brain-state transition time-points using spatial standard deviation of the brain activity pattern that changes over time. Furthermore, we also used Bayesian information criterion to the clustering method to estimate and extract the number of brain-states. Through validations, it was proved that BEST could find brain-state transition time-points and could estimate the proper number of brain-states without any a priori knowledge. It has also shown that BEST can be applied to resting state fMRI data and provide stable and consistent results.-
dc.languageEnglish-
dc.publisherSPRINGER-
dc.titleBrain-State Extraction Algorithm Based on the State Transition (BEST): A Dynamic Functional Brain Network Analysis in fMRI Study-
dc.typeArticle-
dc.identifier.wosid000484523500011-
dc.identifier.scopusid2-s2.0-85067083553-
dc.type.rimsART-
dc.citation.volume32-
dc.citation.issue5-
dc.citation.beginningpage897-
dc.citation.endingpage913-
dc.citation.publicationnameBRAIN TOPOGRAPHY-
dc.identifier.doi10.1007/s10548-019-00719-7-
dc.contributor.localauthorJeong, Yong-
dc.contributor.nonIdAuthorYoo, Kwangsun-
dc.contributor.nonIdAuthorRoh, Jee Hoon-
dc.contributor.nonIdAuthorMoon, Won-Jin-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorBrain-state-
dc.subject.keywordAuthorTransition time-point-
dc.subject.keywordAuthorSpatial standard deviation-
dc.subject.keywordAuthorNumber of components-
dc.subject.keywordAuthorBayesian information criterion-
dc.subject.keywordAuthorFunctional MRI-
dc.subject.keywordPlusINDEPENDENT COMPONENT ANALYSIS-
dc.subject.keywordPlusINDIVIDUAL-DIFFERENCES-
dc.subject.keywordPlusALZHEIMERS-DISEASE-
dc.subject.keywordPlusMOTOR CORTEX-
dc.subject.keywordPlusCONNECTIVITY-
dc.subject.keywordPlusPATTERNS-
dc.subject.keywordPlusCOMMON-
Appears in Collection
BiS-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 1 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0