Literature DB >> 15065162

Comparison of TCA and ICA techniques in fMRI data processing.

Xia Zhao1, David Glahn, Li Hai Tan, Ning Li, Jinhu Xiong, Jia-Hong Gao.   

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 Copyright 2004 Wiley-Liss, Inc.

Entities:  

Mesh:

Year:  2004        PMID: 15065162     DOI: 10.1002/jmri.20023

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  8 in total

1.  Development of 2dTCA for the detection of irregular, transient BOLD activity.

Authors:  Victoria L Morgan; Yong Li; Bassel Abou-Khalil; John C Gore
Journal:  Hum Brain Mapp       Date:  2008-01       Impact factor: 5.038

2.  Temporal clustering analysis: what does it tell us about the resting state of the brain?

Authors:  Victoria L Morgan; John C Gore; Jerzy P Szaflarski
Journal:  Med Sci Monit       Date:  2008-07

3.  The effect of model order selection in group PICA.

Authors:  Ahmed Abou-Elseoud; Tuomo Starck; Jukka Remes; Juha Nikkinen; Osmo Tervonen; Vesa Kiviniemi
Journal:  Hum Brain Mapp       Date:  2010-08       Impact factor: 5.038

4.  Cluster analysis detection of functional MRI activity in temporal lobe epilepsy.

Authors:  Victoria L Morgan; John C Gore; Bassel Abou-Khalil
Journal:  Epilepsy Res       Date:  2007-07-23       Impact factor: 3.045

Review 5.  Review of methods for functional brain connectivity detection using fMRI.

Authors:  Kaiming Li; Lei Guo; Jingxin Nie; Gang Li; Tianming Liu
Journal:  Comput Med Imaging Graph       Date:  2008-12-25       Impact factor: 4.790

6.  Neurometrics of intrinsic connectivity networks at rest using fMRI: retest reliability and cross-validation using a meta-level method.

Authors:  Krista M Wisner; Gowtham Atluri; Kelvin O Lim; Angus W Macdonald
Journal:  Neuroimage       Date:  2013-03-15       Impact factor: 6.556

7.  Toward a neurometric foundation for probabilistic independent component analysis of fMRI data.

Authors:  Andrew B Poppe; Krista Wisner; Gowtham Atluri; Kelvin O Lim; Vipin Kumar; Angus W Macdonald
Journal:  Cogn Affect Behav Neurosci       Date:  2013-09       Impact factor: 3.526

8.  A method to compare the discriminatory power of data-driven methods: Application to ICA and IVA.

Authors:  Yuri Levin-Schwartz; Vince D Calhoun; Tülay Adalı
Journal:  J Neurosci Methods       Date:  2018-10-30       Impact factor: 2.390

  8 in total

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