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
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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