An independent component analysis based tool for exploring functional connections in the brain

S. M. Rolfe; L. Finney; R. F. Tungaraza; J. Guan; L. G. Shapiro; J. F. Brinkley M.D.; A. Poliakov; N. Kleinhans; E. Alyward

doi:10.1117/12.811648

27 March 2009 An independent component analysis based tool for exploring functional connections in the brain

S. M. Rolfe, L. Finney, R. F. Tungaraza, J. Guan, L. G. Shapiro, J. F. Brinkley M.D., A. Poliakov, N. Kleinhans, E. Alyward

Author Affiliations +

Proceedings Volume 7259, Medical Imaging 2009: Image Processing; 725921 (2009) https://doi.org/10.1117/12.811648
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

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.

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S. M. Rolfe, L. Finney, R. F. Tungaraza, J. Guan, L. G. Shapiro, J. F. Brinkley M.D., A. Poliakov, N. Kleinhans, and E. Alyward "An independent component analysis based tool for exploring functional connections in the brain", Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 725921 (27 March 2009); https://doi.org/10.1117/12.811648

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