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- W2057962023 abstract "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 inactive pixels. If only active pixels are considered, the sensitivity of TCA will be improved greatly and it could be applied to the whole brain. In this study, some modifications were made to TCA to remove inactive pixels, and the applicability of the modified TCA to the whole brain was validated with a set of visual fMRI data. Based on the time series of the modified TCA, activations of the whole brain corresponding to the visual stimulation were detected. Compared with the previous TCA, the modified TCA method shows a significant improvement in the sensitivity to detect activation peaks of the whole brain." @default.
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- W2057962023 date "2007-01-01" @default.
- W2057962023 modified "2023-10-13" @default.
- W2057962023 title "An improved temporal clustering analysis method applied to whole-brain data in fMRI study" @default.
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- W2057962023 doi "https://doi.org/10.1016/j.mri.2006.09.034" @default.
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