Practice-related changes in neural activation patterns investigated via wavelet-based clustering analysis

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dc.contributor.authorLee, Jinaeko
dc.contributor.authorPark, Cheolwooko
dc.contributor.authorDyckman, Kara A.ko
dc.contributor.authorLazar, Nicole A.ko
dc.contributor.authorAustin, Benjamin P.ko
dc.contributor.authorLi, Qingyangko
dc.contributor.authorMcDowell, Jennifer E.ko
dc.date.accessioned2021-06-11T01:50:18Z-
dc.date.available2021-06-11T01:50:18Z-
dc.date.created2021-06-11-
dc.date.created2021-06-11-
dc.date.issued2013-09-
dc.identifier.citationHUMAN BRAIN MAPPING, v.34, no.9, pp.2276 - 2291-
dc.identifier.issn1065-9471-
dc.identifier.urihttp://hdl.handle.net/10203/285775-
dc.description.abstractObjectives: To evaluate brain activation using functional magnetic resonance imaging (fMRI) and specifically, activation changes across time associated with practice-related cognitive control during eye movement tasks. Experimental design: Participants were engaged in antisaccade performance (generating a glance away from a cue) while fMR images were acquired during two separate test sessions: (1) at pre-test before any exposure to the task and (2) at post-test, after 1 week of daily practice on antisaccades, prosaccades (glancing toward a target), or fixation (maintaining gaze on a target). Principal observations: The three practice groups were compared across the two test sessions, and analyses were conducted via the application of a model-free clustering technique based on wavelet analysis. This series of procedures was developed to avoid analysis problems inherent in fMRI data and was composed of several steps: detrending, data aggregation, wavelet transform and thresholding, no trend test, principal component analysis (PCA), and K-means clustering. The main clustering algorithm was built in the wavelet domain to account for temporal correlation. We applied a no trend test based on wavelets to significantly reduce the high dimension of the data. We clustered the thresholded wavelet coefficients of the remaining voxels using PCA K-means clustering. Conclusion: Over the series of analyses, we found that the antisaccade practice group was the only group to show decreased activation from pre-test to post-test in saccadic circuitry, particularly evident in supplementary eye field, frontal eye fields, superior parietal lobe, and cuneus. Hum Brain Mapp 34:2276-2291, 2012. (c) 2012 Wiley Periodicals, Inc.-
dc.languageEnglish-
dc.publisherWILEY-
dc.titlePractice-related changes in neural activation patterns investigated via wavelet-based clustering analysis-
dc.typeArticle-
dc.identifier.wosid000323320800023-
dc.identifier.scopusid2-s2.0-84882659806-
dc.type.rimsART-
dc.citation.volume34-
dc.citation.issue9-
dc.citation.beginningpage2276-
dc.citation.endingpage2291-
dc.citation.publicationnameHUMAN BRAIN MAPPING-
dc.identifier.doi10.1002/hbm.22066-
dc.contributor.localauthorPark, Cheolwoo-
dc.contributor.nonIdAuthorLee, Jinae-
dc.contributor.nonIdAuthorDyckman, Kara A.-
dc.contributor.nonIdAuthorLazar, Nicole A.-
dc.contributor.nonIdAuthorAustin, Benjamin P.-
dc.contributor.nonIdAuthorLi, Qingyang-
dc.contributor.nonIdAuthorMcDowell, Jennifer E.-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorantisaccades-
dc.subject.keywordAuthorbootstrap-
dc.subject.keywordAuthorclustering-
dc.subject.keywordAuthorfMRI-
dc.subject.keywordAuthorplasticity-
dc.subject.keywordAuthorprincipal component analysis-
dc.subject.keywordAuthorsaccades-
dc.subject.keywordAuthorwavelets-
dc.subject.keywordPlusFALSE DISCOVERY RATE-
dc.subject.keywordPlusVOLITIONAL SACCADES-
dc.subject.keywordPlusTIME-SERIES-
dc.subject.keywordPlusFMRI-
dc.subject.keywordPlusSCHIZOPHRENIA-
dc.subject.keywordPlusBEHAVIOR-
dc.subject.keywordPlusMEMORY-
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