Robust ICA analysis for model-free functional connectivity detection

Scott J. Peltier; Yasser Kadah; Stephen M. LaConte; Xiaoping Hu

doi:10.1117/12.536065

30 April 2004 Robust ICA analysis for model-free functional connectivity detection

Scott J. Peltier, Yasser Kadah, Stephen M. LaConte, Xiaoping Hu

Proceedings Volume 5369, Medical Imaging 2004: Physiology, Function, and Structure from Medical Images; (2004) https://doi.org/10.1117/12.536065
Event: Medical Imaging 2004, 2004, San Diego, California, United States

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.

Citation Download Citation

Scott J. Peltier, Yasser Kadah, Stephen M. LaConte, and Xiaoping Hu "Robust ICA analysis for model-free functional connectivity detection", Proc. SPIE 5369, Medical Imaging 2004: Physiology, Function, and Structure from Medical Images, (30 April 2004); https://doi.org/10.1117/12.536065

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