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Improved temporal clustering analysis method for detecting multiple response peaks in fMRI.

Na Lu·Bao-Ci Shan·Ke Li·Bin Yan·Wei Wang·Kun-Cheng Li
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Abstract

PURPOSE: To develop an improved temporal clustering analysis (TCA) method for detecting multiple active peaks by running the method once. MATERIALS AND METHODS: Two cases of simulation data and a set of actual fMRI data from nine subjects were used to compare the traditional TCA method with the new method, termed extremum TCA (ETCA). The first case of simulation data simulated event-related activation and block activation in one cerebral area, and the second case simulated event-related activation and block activation in two cerebral areas. An in vivo visual stimulating experiment was performed on a 1.5T MR scanner. All imaging data were processed using both traditional TCA and the new method. RESULTS: The results of both the simulated and actual fMRI data show that the new method is more sensitive and exact than traditional TCA in detecting multiple response peaks. CONCLUSION: The new method is effective in detecting multiple activations even when the timing and location of the brain activation are completely unknown.

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Brain MappingCluster AnalysisComputer SimulationHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingPhotic StimulationSignal Processing, Computer-AssistedJournal ArticleResearch Support, Non-U.S. Gov't

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Na Lu, Bao-Ci Shan, Ke Li, Bin Yan, Wei Wang, Kun-Cheng Li. 2006. Improved temporal clustering analysis method for detecting multiple response peaks in fMRI.. https://doi.org/10.1002/jmri.20523

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