Improved detection of time windows of brain responses in fMRI using modified temporal clustering analysis.
Temporal clustering analysis (TCA) has been proposed recently as a method to detect time windows of brain responses in functional MRI (fMRI) studies when the timing and location of the activation are completely unknown. Modifications to the TCA technique are introduced in this report to further improve the sensitivity in detecting brain activation. The modified TCA is based on the integrated signal intensity of a temporal cluster at each time point, while the original TCA is based only on the size of a temporal cluster at each time point. A temporal cluster at each time point is defined, in both TCA methods, as a group of pixels reaching their maximum (or minimum) values at the same time. Both computer simulation and in vivo fMRI experiments have been performed. Compared with the original TCA, the modified TCA shows a significant improvement in the sensitivity to detect activation peaks for determining time windows of brain responses.
Yee
Seong-Hwan
SH
Research Imaging Center, University of Texas Health Science Center, San Antonio, TX 78229, USA.
Gao
Jia-Hong
JH
D001921:Brain / Q000502:physiology*; D016000:Cluster Analysis; D003198:Computer Simulation; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008962:Models, Theoretical; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors