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

✍ Scribed by Na Lu; Bao-Ci Shan; Ke Li; Bin Yan; Wei Wang; Kun-Cheng Li


Book ID
102373459
Publisher
John Wiley and Sons
Year
2006
Tongue
English
Weight
402 KB
Volume
23
Category
Article
ISSN
1053-1807

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✦ Synopsis


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. J. Magn. Reson. Imaging 2006. © 2006 Wiley‐Liss, Inc.


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An improved temporal clustering analysis
✍ Na Lu; Bao-Ci Shan; Jian-Yang Xu; Wei Wang; Kun-Cheng Li 📂 Article 📅 2007 🏛 Elsevier Science 🌐 English ⚖ 776 KB

Temporal clustering analysis (TCA) has been proposed as a method to detect the brain responses of an fMRI time series when the time and location of the activation are completely unknown. But TCA is still incompetent in dealing with the time series of the whole brain due to the existence of many inac