Applied to fMRI, these algorithms might lead to different spatial or temporal readouts of brain activation. Several ICA algorithms have been proposed in the neural network literature.
Independent component analysis (ICA) has been successfully employed to decompose functional MRI (fMRI) time‐series into sets of activation maps and associated time‐courses. 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. Conversely, the Infomax sICA was superior in terms of global estimation of the ICA model and 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. : 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. 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. 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 : Neural Comput 7:1004-1034) and the Fixed-Point (Hyvärinen : Adv Neural Inf Proc Syst 10:273-279) algorithms.
Independent component analysis (ICA) has been successfully employed to decompose functional MRI (fMRI) time-series into sets of activation maps and associated time-courses.
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