Spatial independent component analysis of functional MRI time‐series: To what extent do results depend on the algorithm used?
- 19 April 2002
- journal article
- Published by Wiley in Human Brain Mapping
- Vol. 16 (3), 146-157
- https://doi.org/10.1002/hbm.10034
Abstract
Independent component analysis (ICA) has been successfully employed to decompose functional MRI (fMRI) time-series into sets of activation maps and associated time-courses. Several ICA algorithms have been proposed in the neural network literature. Applied to fMRI, these algorithms might lead to different spatial or temporal readouts of brain activation. We compared the two ICA algorithms that have been used so far for spatial ICA (sICA) of fMRI time-series: the Infomax (Bell and Sejnowski [1995]: Neural Comput 7:1004-1034) and the Fixed-Point (Hyvärinen [1999]: Adv Neural Inf Proc Syst 10:273-279) algorithms. We evaluated the Infomax- and Fixed Point-based sICA decompositions of simulated motor, and real motor and visual activation fMRI time-series using an ensemble of measures. Log-likelihood (McKeown et al. [1998]: Hum Brain Mapp 6:160-188) was used as a measure of how significantly the estimated independent sources fit the statistical structure of the data; receiver operating characteristics (ROC) and linear correlation analyses were used to evaluate the algorithms' accuracy of estimating the spatial layout and the temporal dynamics of simulated and real activations; cluster sizing calculations and an estimation of a residual gaussian noise term within the components were used to examine the anatomic structure of ICA components and for the assessment of noise reduction capabilities. Whereas both algorithms produced highly accurate results, the Fixed-Point outperformed the Infomax in terms of spatial and temporal accuracy as long as inferential statistics were employed as benchmarks. Conversely, the Infomax sICA was superior in terms of global estimation of the ICA model and noise reduction capabilities. Because of its adaptive nature, the Infomax approach appears to be better suited to investigate activation phenomena that are not predictable or adequately modelled by inferential techniques.Keywords
This publication has 21 references indexed in Scilit:
- Spatio-temporal accuracy of ICA for FMRINeuroImage, 2001
- Spatial and temporal independent component analysis of functional MRI data containing a pair of task‐related waveformsHuman Brain Mapping, 2001
- Fast and robust fixed-point algorithms for independent component analysisIEEE Transactions on Neural Networks, 1999
- ROC Analysis of Statistical Methods Used in Functional MRI: Individual SubjectsNeuroImage, 1999
- Analysis of fMRI data by blind separation into independent spatial componentsHuman Brain Mapping, 1998
- An Information-Maximization Approach to Blind Separation and Blind DeconvolutionNeural Computation, 1995
- Independent component analysis, A new concept?Signal Processing, 1994
- Analysis of functional MRI time‐seriesHuman Brain Mapping, 1994
- Processing strategies for time‐course data sets in functional mri of the human brainMagnetic Resonance in Medicine, 1993
- Time course EPI of human brain function during task activationMagnetic Resonance in Medicine, 1992