DMCA
Probabilistic Independent Component Analysis (2003)
| Citations: | 208 - 13 self |
BibTeX
@MISC{Beckmann03probabilisticindependent,
author = {Christian F. Beckmann and Christian F. Beckmann*t and Stephen M. Smith},
title = {Probabilistic Independent Component Analysis},
year = {2003}
}
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Abstract
Independent Component Analysis is becoming a popular exploratory method for analysing complex data such as that from FMRI experiments. The application of such 'model-free' methods, however, has been somewhat restricted both by the view that results can be uninterpretable and by the lack of ability to quantify statistical significance. We present an integrated approach to Probabilistic ICA for FMRI data that allows for non-square mixing in the presence of Gaussian noise. We employ an objective estimation of the amount of Gaussian noise through Bayesian analysis of the true dimensionality of the data, i.e. the number of activation and non-Gaussian noise sources. Reduction of the data to this 'true' subspace before the ICA decomposition automatically results in an estimate of the noise, leading to the ability to assign significance to voxels in ICA spatial maps. Estimation of the number of intrinsic sources not only enables us to carry out probabilistic modelling, but also achieves an asymptotically unique decomposition of the data. This reduces problems of interpretation, as each final independent component is now much more likely to be due to only one physical or physiological process. We also describe other improvements to standard ICA, such as temporal pre-whitening and variance normafisation of timeseries, the latter being particularly useful in the context of dimensionality reduction when weak activation is present. We discuss the use of prior information about the spatiotemporal nature of the source processes, and an alternative-hypothesis testing approach for inference, using Gaussian mixture models. The performance of our approach is illustrated and evaluated on real and complex artificial FMRI data, and compared to the spatio-temporal accuracy of restfits obtaine...
Keyphrases
probabilistic independent component analysis gaussian noise final independent component true dimensionality popular exploratory method spatio-temporal accuracy independent component analysis integrated approach statistical significance probabilistic modelling dimensionality reduction intrinsic source physiological process fmri experiment bayesian analysis non-square mixing ica spatial map weak activation complex data probabilistic ica alternative-hypothesis testing approach ica decomposition prior information spatiotemporal nature temporal pre-whitening gaussian mixture model non-gaussian noise source fmri data complex artificial fmri data objective estimation variance normafisation unique decomposition
