Rolfe, S. M. and Finney, L. and Tungaraza, R. F. and Guan, J. and Shapiro, L. G. and Brinkley, James F and Poliakov, A. and Kleinhans, N. and Alyward, E. (2009) An independent component analysis based tool for exploring functional connections in the brain. In: Proc. SPIE 7259, Medical Imaging 2009: Image Processing. p. 725921.
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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.
| Item Type: | Book Section |
|---|---|
| Subjects: | All Projects > Brain Mapping |
| Divisions: | University of Washington > Department of Biological Structure University of Washington > Department of Computer Science and Engineering University of Washington > Department of Electrical Engineering |
| Depositing User: | Jim Brinkley |
| Date Deposited: | 17 Jul 2018 22:48 |
| Last Modified: | 09 May 2019 23:43 |
| URI: | http://sigpubs.si.washington.edu/id/eprint/307 |
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