Robust feature selection in resting-state fMRI connectivity based on population studies
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Golland_Robust feature.pdf
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Author(s)
Venkataraman, Archana
Kubicki, Marek
Westin, Carl-Fredrik
Golland, Polina
Date Issued
June 2010
Journal
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Venkataraman, Archana et al. “Robust Feature Selection in Resting-state fMRI Connectivity Based on Population Studies.” IEEE, 2010. 63–70. © Copyright 2010 IEEE
Version
Final published version
Abstract
We propose an alternative to univariate statistics for identifying population differences in functional connectivity. Our feature selection method is based on a procedure that searches across subsets of the data to isolate a set of robust, predictive functional connections. The metric, known as the Gini Importance, also summarizes multivariate patterns of interaction, which cannot be captured by univariate techniques. We compare the Gini Importance with univariate statistical tests to evaluate functional connectivity changes induced by schizophrenia. Our empirical results indicate that univariate features vary dramatically across subsets of the data and have little classification power. In contrast, relevant features based on Gini Importance are considerably more stable and allow us to accurately predict the diagnosis of a test subject.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1109/CVPRW.2010.5543446
