File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Comparison of TCA and ICA Techniques in fMRI Data Processing

TitleComparison of TCA and ICA Techniques in fMRI Data Processing
Authors
Issue Date2004
PublisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://www.interscience.wiley.com/jpages/1053-1807/
Citation
Journal Of Magnetic Resonance Imaging, 2004, v. 19 n. 4, p. 397-402 How to Cite?
AbstractPurpose: To make a quantitative comparison of temporal cluster analysis (TCA) and independent component analysis (ICA) techniques in detecting brain activation by using simulated data and in vivo event-related functional MRI (fMRI) experiments. Materials and Methods: A single-slice MRI image was replicated 150 times to simulate an fMRI time series. An event-related brain activation pattern with five different levels of intensity and Gaussian noise was superimposed on these images. Maximum contrast-to-noise ratio (CNR) of the signal change ranged from 1.0 to 2.0 by 0.25 increments. In vivo visual stimulation fMRI experiments were performed on a 1.9 T magnet. Six human volunteers participated in this study. All imaging data were analyzed using both TCA and ICA methods. Results: Both simulated and in vivo data have shown that no statistically significant difference exists in the activation areas detected by both ICA and TCA techniques when CNR of fMRI signal is larger than 1.75. Conclusion: TCA and ICA techniques are comparable in generating functional brain maps in event-related fMRI experiments. Although ICA has richer features in exploring the spatial and temporal information of the functional images, the TCA method has advantages in its computational efficiency, repeatability, and readiness to average data from group subjects.
Persistent Identifierhttp://hdl.handle.net/10722/179503
ISSN
2015 Impact Factor: 3.25
2015 SCImago Journal Rankings: 1.683
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorZhao, Xen_US
dc.contributor.authorGlahn, Den_US
dc.contributor.authorTan, LHen_US
dc.contributor.authorLi, Nen_US
dc.contributor.authorXiong, Jen_US
dc.contributor.authorGao, JHen_US
dc.date.accessioned2012-12-19T09:58:01Z-
dc.date.available2012-12-19T09:58:01Z-
dc.date.issued2004en_US
dc.identifier.citationJournal Of Magnetic Resonance Imaging, 2004, v. 19 n. 4, p. 397-402en_US
dc.identifier.issn1053-1807en_US
dc.identifier.urihttp://hdl.handle.net/10722/179503-
dc.description.abstractPurpose: To make a quantitative comparison of temporal cluster analysis (TCA) and independent component analysis (ICA) techniques in detecting brain activation by using simulated data and in vivo event-related functional MRI (fMRI) experiments. Materials and Methods: A single-slice MRI image was replicated 150 times to simulate an fMRI time series. An event-related brain activation pattern with five different levels of intensity and Gaussian noise was superimposed on these images. Maximum contrast-to-noise ratio (CNR) of the signal change ranged from 1.0 to 2.0 by 0.25 increments. In vivo visual stimulation fMRI experiments were performed on a 1.9 T magnet. Six human volunteers participated in this study. All imaging data were analyzed using both TCA and ICA methods. Results: Both simulated and in vivo data have shown that no statistically significant difference exists in the activation areas detected by both ICA and TCA techniques when CNR of fMRI signal is larger than 1.75. Conclusion: TCA and ICA techniques are comparable in generating functional brain maps in event-related fMRI experiments. Although ICA has richer features in exploring the spatial and temporal information of the functional images, the TCA method has advantages in its computational efficiency, repeatability, and readiness to average data from group subjects.en_US
dc.languageengen_US
dc.publisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://www.interscience.wiley.com/jpages/1053-1807/en_US
dc.relation.ispartofJournal of Magnetic Resonance Imagingen_US
dc.subject.meshBrain Mappingen_US
dc.subject.meshCluster Analysisen_US
dc.subject.meshData Interpretation, Statisticalen_US
dc.subject.meshHumansen_US
dc.subject.meshImage Processing, Computer-Assisted - Methodsen_US
dc.subject.meshMagnetic Resonance Imaging - Methodsen_US
dc.subject.meshPhotic Stimulationen_US
dc.subject.meshSignal Processing, Computer-Assisteden_US
dc.subject.meshVisual Cortex - Physiologyen_US
dc.titleComparison of TCA and ICA Techniques in fMRI Data Processingen_US
dc.typeArticleen_US
dc.identifier.emailTan, LH: tanlh@hku.hken_US
dc.identifier.authorityTan, LH=rp01202en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1002/jmri.20023en_US
dc.identifier.pmid15065162-
dc.identifier.scopuseid_2-s2.0-1842433643en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-1842433643&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume19en_US
dc.identifier.issue4en_US
dc.identifier.spage397en_US
dc.identifier.epage402en_US
dc.identifier.isiWOS:000220636800003-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridZhao, X=7407577900en_US
dc.identifier.scopusauthoridGlahn, D=6603114543en_US
dc.identifier.scopusauthoridTan, LH=7402233462en_US
dc.identifier.scopusauthoridLi, N=36014373300en_US
dc.identifier.scopusauthoridXiong, J=7202010007en_US
dc.identifier.scopusauthoridGao, JH=7404475674en_US

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats