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Proceedings Paper

Robust ICA analysis for model-free functional connectivity detection
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Paper Abstract

Resting state oscillations have been detected in functional MRI studies, and appear to be synchronized between functionally related areas. It has also been shown that these synchronized oscillations decrease in some pathological states. Thus, these fluctuations are important as a potential signal of interest, which could indicate connectivity between functionally related areas of the brain. A current challenge is to detect these patterns without using an external reference. ICA analysis is a promising model-free technique that finds the independent components in a data set. A drawback to using ICA is the possibility of convergence problems in the presence of noise, and signal mixing across components. This work utilizes a recently developed denoising method as a preprocessing step to condition task and resting state functional MRI data for ICA analysis. The advantages of this approach include increased reliability of ICA results and allowing region specific signal patterns to be separated using a model-free analysis.

Paper Details

Date Published: 30 April 2004
PDF: 9 pages
Proc. SPIE 5369, Medical Imaging 2004: Physiology, Function, and Structure from Medical Images, (30 April 2004); doi: 10.1117/12.536065
Show Author Affiliations
Scott J. Peltier, Emory Univ./Georgia Tech Biomedical Engineering (United States)
Yasser Kadah, Emory Univ./Georgia Tech Biomedical Engineering (United States)
Stephen M. LaConte, Emory Univ./Georgia Tech Biomedical Engineering (United States)
Xiaoping Hu, Emory Univ./Georgia Tech Biomedical Engineering (United States)


Published in SPIE Proceedings Vol. 5369:
Medical Imaging 2004: Physiology, Function, and Structure from Medical Images
Amir A. Amini; Armando Manduca, Editor(s)

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