Abstract
In this work, we propose a simple and effective scheme to incorporate prior knowledge about the sources of interest (SOIs) in independent component analysis (ICA) and apply the method to estimate brain activations from functional magnetic resonance imaging (fMRI) data. We name the proposed method as feature-selective ICA since it incorporates the features in the sample space of the independent components during ICA estimation. The feature-selective scheme is achieved through a filtering operation in the source sample space followed by a projection onto the demixing vector space by a least squares projection in an iterative ICA process. We perform ICA estimation of artificial activations superimposed into a resting state fMRI dataset to show that the feature-selective scheme improves the detection of injected activation from the independent component estimated by ICA. We also compare the task-related sources estimated from true fMRI data by a feature-selective ICA algorithm versus an ICA algorithm and show evidence that the feature-selective scheme helps improve the estimation of the sources in both spatial activation patterns and the time courses.
Highlights
Independent component analysis is an exploratory data analysis technique to extract statistically independent sources from a given set of linear mixtures
Motivated by [12], in this work, we show how a regulation on the sample space features of the independent sources can be imposed in an iterative manner to improve the independent component analysis (ICA) estimation and we introduce an effective implementation of the scheme
We propose a method to incorporate a priori knowledge about the source signals into ICA estimation by a feature-selective filtering-projection scheme
Summary
Independent component analysis is an exploratory data analysis technique to extract statistically independent sources from a given set of linear mixtures. In the applications of ICA to fMRI data, the underlying sources are assumed to be either spatially independent activation patterns (spatial ICA) or independent temporal waveforms of blood oxygen level dependent (BOLD) signals (temporal ICA) [4]. The time courses of the physiologically interesting sources are more likely to assume a slow varying temporal pattern caused by the blood oxygen level dependent (BOLD) effect [8]. These contextual features encoded in the signal sample space, that is, the spatial or temporal space in which the source signals are represented, are not exploited in a standard ICA framework. When the contextual features of the sources are available a priori, it is desirable to incorporate this knowledge into the ICA procedure to achieve better estimation of the underlying fMRI sources of practical interest
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