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Proceedings Paper

An independent component analysis based tool for exploring functional connections in the brain
Author(s): S. M. Rolfe; L. Finney; R. F. Tungaraza; J. Guan; L. G. Shapiro; J. F. Brinkley; A. Poliakov; N. Kleinhans; E. Alyward
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Paper Abstract

This work introduces a MATLAB-based tool developed for investigating functional connectivity in the brain. Independent component analysis (ICA) is used as a measure of voxel similarity which allows the user to find and view statistically independent maps of correlated voxels. These maps of correlated voxel activity may indicate functionally connected regions. Specialized clustering and feature extraction techniques have been designed to find and characterize clusters of activated voxels, which allows comparison of the spatial maps of correlation across subjects. This method is also used to compare the ICA generated images to fMRI images showing statistically significant activations generated by Statistical Parametric Mapping (SPM). The capability of querying specific coordinates in the brain supports integration and comparison with other data modalities such as Cortical Stimulation Mapping and Single Unit Recordings.

Paper Details

Date Published: 27 March 2009
PDF: 9 pages
Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 725921 (27 March 2009); doi: 10.1117/12.811648
Show Author Affiliations
S. M. Rolfe, Univ. of Washington (United States)
L. Finney, Univ. of Washington (United States)
R. F. Tungaraza, Univ. of Washington (United States)
J. Guan, Univ. of Washington (United States)
L. G. Shapiro, Univ. of Washington (United States)
J. F. Brinkley, Univ. of Washington (United States)
A. Poliakov, Seattle Children's Hospital (United States)
N. Kleinhans, Univ. of Washington (United States)
E. Alyward, Univ. of Washington (United States)


Published in SPIE Proceedings Vol. 7259:
Medical Imaging 2009: Image Processing
Josien P. W. Pluim; Benoit M. Dawant, Editor(s)

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