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A feature-selective independent component analysis method for functional MRI.

Li YO, Adali T, Calhoun VD - Int J Biomed Imaging (2007)

Bottom Line: 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.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 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA.

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.

No MeSH data available.


Related in: MedlinePlus

Simulated source superimposed on a resting state fMRIdataset: (a) activation region, (b) time course.
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Related In: Results  -  Collection


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fig1: Simulated source superimposed on a resting state fMRIdataset: (a) activation region, (b) time course.

Mentions: To generate the hybrid fMRI data, a 30 30 25 mm3 activation region with irregular shape is created at a chosenanatomical location in the brain, together with a time course simulating thehemodynamic response to a box-car task paradigm. The imposed activation regionand the time course are shown in Figure 1.


A feature-selective independent component analysis method for functional MRI.

Li YO, Adali T, Calhoun VD - Int J Biomed Imaging (2007)

Simulated source superimposed on a resting state fMRIdataset: (a) activation region, (b) time course.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2233814&req=5

fig1: Simulated source superimposed on a resting state fMRIdataset: (a) activation region, (b) time course.
Mentions: To generate the hybrid fMRI data, a 30 30 25 mm3 activation region with irregular shape is created at a chosenanatomical location in the brain, together with a time course simulating thehemodynamic response to a box-car task paradigm. The imposed activation regionand the time course are shown in Figure 1.

Bottom Line: 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.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 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA.

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.

No MeSH data available.


Related in: MedlinePlus