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Article: Investigating the enhancement of template-free activation detection of event-related fMRI data using wavelet shrinkage and figures of merit

TitleInvestigating the enhancement of template-free activation detection of event-related fMRI data using wavelet shrinkage and figures of merit
Authors
KeywordsActivation detection
Event-related
Functional magnetic resonance imaging
Template-free
Wavelet shrinkage
Issue Date2011
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/artmed
Citation
Artificial Intelligence In Medicine, 2011, v. 51 n. 3, p. 187-198 How to Cite?
AbstractObjective: We propose a method for preprocessing event-related functional magnetic resonance imaging (fMRI) data that can lead to enhancement of template-free activation detection. The method is based on using a figure of merit to guide the wavelet shrinkage of a given fMRI data set. Background: Several previous studies have demonstrated that in the root-mean-square error setting, wavelet shrinkage can improve the signal-to-noise ratio of fMRI time courses. However, preprocessing fMRI data in the root-mean-square error setting does not necessarily lead to enhancement of template-free activation detection. Motivated by this observation, in this paper, we move to the detection setting and investigate the possibility of using wavelet shrinkage to enhance template-free activation detection of fMRI data. Methodology: The main ingredients of our method are (i) forward wavelet transform of the voxel time courses, (ii) shrinking the resulting wavelet coefficients as directed by an appropriate figure of merit, (iii) inverse wavelet transform of the shrunk data, and (iv) submitting these preprocessed time courses to a given activation detection algorithm. Two figures of merit are developed in the paper, and two other figures of merit adapted from the literature are described. Results: Receiver-operating characteristic analyses with simulated fMRI data showed quantitative evidence that data preprocessing as guided by the figures of merit developed in the paper can yield improved detectability of the template-free measures. We also demonstrate the application of our methodology on an experimental fMRI data set. Conclusions: The proposed method is useful for enhancing template-free activation detection in event-related fMRI data. It is of significant interest to extend the present framework to produce comprehensive, adaptive and fully automated preprocessing of fMRI data optimally suited for subsequent data analysis steps. © 2010 Elsevier B.V.
Persistent Identifierhttp://hdl.handle.net/10722/139125
ISSN
2015 Impact Factor: 2.142
2015 SCImago Journal Rankings: 0.884
ISI Accession Number ID
Funding AgencyGrant Number
City University of Hong Kong7002493
Funding Information:

SCN was supported by a City University of Hong Kong Strategic Research Grant (7002493). We thank the anonymous reviewers for their very helpful suggestions and comments for improving the content of this article.

References

 

DC FieldValueLanguage
dc.contributor.authorNgan, SCen_HK
dc.contributor.authorHu, Xen_HK
dc.contributor.authorKhong, PLen_HK
dc.date.accessioned2011-09-23T05:45:31Z-
dc.date.available2011-09-23T05:45:31Z-
dc.date.issued2011en_HK
dc.identifier.citationArtificial Intelligence In Medicine, 2011, v. 51 n. 3, p. 187-198en_HK
dc.identifier.issn0933-3657en_HK
dc.identifier.urihttp://hdl.handle.net/10722/139125-
dc.description.abstractObjective: We propose a method for preprocessing event-related functional magnetic resonance imaging (fMRI) data that can lead to enhancement of template-free activation detection. The method is based on using a figure of merit to guide the wavelet shrinkage of a given fMRI data set. Background: Several previous studies have demonstrated that in the root-mean-square error setting, wavelet shrinkage can improve the signal-to-noise ratio of fMRI time courses. However, preprocessing fMRI data in the root-mean-square error setting does not necessarily lead to enhancement of template-free activation detection. Motivated by this observation, in this paper, we move to the detection setting and investigate the possibility of using wavelet shrinkage to enhance template-free activation detection of fMRI data. Methodology: The main ingredients of our method are (i) forward wavelet transform of the voxel time courses, (ii) shrinking the resulting wavelet coefficients as directed by an appropriate figure of merit, (iii) inverse wavelet transform of the shrunk data, and (iv) submitting these preprocessed time courses to a given activation detection algorithm. Two figures of merit are developed in the paper, and two other figures of merit adapted from the literature are described. Results: Receiver-operating characteristic analyses with simulated fMRI data showed quantitative evidence that data preprocessing as guided by the figures of merit developed in the paper can yield improved detectability of the template-free measures. We also demonstrate the application of our methodology on an experimental fMRI data set. Conclusions: The proposed method is useful for enhancing template-free activation detection in event-related fMRI data. It is of significant interest to extend the present framework to produce comprehensive, adaptive and fully automated preprocessing of fMRI data optimally suited for subsequent data analysis steps. © 2010 Elsevier B.V.en_HK
dc.languageengen_US
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/artmeden_HK
dc.relation.ispartofArtificial Intelligence in Medicineen_HK
dc.subjectActivation detectionen_HK
dc.subjectEvent-relateden_HK
dc.subjectFunctional magnetic resonance imagingen_HK
dc.subjectTemplate-freeen_HK
dc.subjectWavelet shrinkageen_HK
dc.subject.meshAlgorithms-
dc.subject.meshComputer Simulation-
dc.subject.meshMagnetic Resonance Imaging - methods-
dc.subject.meshROC Curve-
dc.subject.meshWavelet Analysis-
dc.titleInvestigating the enhancement of template-free activation detection of event-related fMRI data using wavelet shrinkage and figures of meriten_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0933-3657&volume=51&issue=3&spage=187&epage=198&date=2011&atitle=Investigating+the+enhancement+of+template-free+activation+detection+of+event-related+fMRI+data+using+wavelet+shrinkage+and+figures+of+merit-
dc.identifier.emailKhong, PL:plkhong@hkucc.hku.hken_HK
dc.identifier.authorityKhong, PL=rp00467en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.artmed.2010.09.006en_HK
dc.identifier.pmid21216572-
dc.identifier.scopuseid_2-s2.0-79954575963en_HK
dc.identifier.hkuros192041en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79954575963&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume51en_HK
dc.identifier.issue3en_HK
dc.identifier.spage187en_HK
dc.identifier.epage198en_HK
dc.identifier.isiWOS:000290281900004-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridNgan, SC=6701902948en_HK
dc.identifier.scopusauthoridHu, X=34770364200en_HK
dc.identifier.scopusauthoridKhong, PL=7006693233en_HK
dc.identifier.citeulike8643200-

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