Comparison of TCA and ICA techniques in fMRI data processing

• Published in: Journal of magnetic resonance imaging Vol. 19; no. 4; pp. 397 - 402
• Main Authors: Zhao, Xia, Glahn, David, Tan, Li Hai, Li, Ning, Xiong, Jinhu, Gao, Jia-Hong
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.04.2004
• Subjects: Brain Mapping; Cluster Analysis; Data Interpretation, Statistical; data processing; functional magnetic resonance imaging; Humans; Image Processing, Computer-Assisted - methods; independent component analysis (ICA); magnetic resonance imaging; Magnetic Resonance Imaging - methods; Photic Stimulation; Signal Processing, Computer-Assisted; temporal cluster analysis (TCA); Visual Cortex - physiology
• ISSN: 1053-1807; 1522-2586
• DOI: 10.1002/jmri.20023

Purpose To make a quantitative comparison of temporal cluster analysis (TCA) and independent component analysis (ICA) techniques in detecting brain activation by using simulated data and in vivo event‐related functional MRI (fMRI) experiments. Materials and Methods A single‐slice MRI image was replicated 150 times to simulate an fMRI time series. An event‐related brain activation pattern with five different levels of intensity and Gaussian noise was superimposed on these images. Maximum contrast‐to‐noise ratio (CNR) of the signal change ranged from 1.0 to 2.0 by 0.25 increments. In vivo visual stimulation fMRI experiments were performed on a 1.9 T magnet. Six human volunteers participated in this study. All imaging data were analyzed using both TCA and ICA methods. Results Both simulated and in vivo data have shown that no statistically significant difference exists in the activation areas detected by both ICA and TCA techniques when CNR of fMRI signal is larger than 1.75. Conclusion TCA and ICA techniques are comparable in generating functional brain maps in event‐related fMRI experiments. Although ICA has richer features in exploring the spatial and temporal information of the functional images, the TCA method has advantages in its computational efficiency, repeatability, and readiness to average data from group subjects. J. Magn. Reson. Imaging 2004;19:397–402. © 2004 Wiley‐Liss, Inc.