Comparison of TCA and ICA Techniques in fMRI Data Processing

Xia Zhao, David Glahn, Li Hai Tan, Ning Li, Jinhu Xiong, Jia Hong Gao

Research output: Contribution to journalArticle

26 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)397-402
Number of pages6
JournalJournal of Magnetic Resonance Imaging
Volume19
Issue number4
DOIs
StatePublished - Apr 2004

Keywords

  • Data processing
  • Functional magnetic resonance imaging
  • Independent component analysis (ICA)
  • Magnetic resonance imaging
  • Temporal cluster analysis (TCA)

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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