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. 2016 Sep;3(3):193-203.
doi: 10.1007/s40708-016-0048-0. Epub 2016 Apr 6.

Exploring stability-based voxel selection methods in MVPA using cognitive neuroimaging data: a comprehensive study

Miaolin Fan  1 , Chun-An Chou  2
Affiliations

Exploring stability-based voxel selection methods in MVPA using cognitive neuroimaging data: a comprehensive study

Miaolin Fan et al. Brain Inform. 2016 Sep.

Abstract

Feature selection plays a key role in multi-voxel pattern analysis because functional magnetic resonance imaging data are typically noisy, sparse, and high-dimensional. Although the conventional evaluation criterion is the classification accuracy, selecting a stable feature set that is not sensitive to the variance in dataset may provide more scientific insights. In this study, we aim to investigate the stability of feature selection methods and test the stability-based feature selection scheme on two benchmark datasets. Top-k feature selection with a ranking score of mutual information and correlation, recursive feature elimination integrated with support vector machine, and L1 and L2-norm regularizations were adapted to a bootstrapped stability selection framework, and the selected algorithms were compared based on both accuracy and stability scores. The results indicate that regularization-based methods are generally more stable in StarPlus dataset, but in Haxby dataset they failed to perform as well as others.

Keywords: Feature selection; Functional MRI; Multi-voxel pattern analysis; Stability.

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Figures

Fig. 1
Fig. 1
A demonstration of MVPA of fMRI data in cognitive experiments. Visual stimuli are presented to subjects during experiment tests and fMRI data are collected over time. Informative voxels are identified as a pattern used for classification among visual stimuli
Fig. 2
Fig. 2
The feature (beta values) matrix is extracted by applying a general linear model to fMRI BOLD signals. Subject 1 in Haxby dataset is used as an illustrative example
Fig. 3
Fig. 3
An illustration of the distribution of voxels selected by each method in the visual cortex area for (a) subject 1 in Haxby dataset and (b) subject 04820 in StarPlus dataset
See this image and copyright information in PMC

References

    1. Haynes JD, Rees G. Decoding mental states from brain activity in humans. Nat Rev Neurosci. 2006;7(7):523–534. doi: 10.1038/nrn1931. - DOI - PubMed
    1. Lemm S, Blankertz B, Dickhaus T, Müller KR. Introduction to machine learning for brain imaging. Neuroimage. 2011;56(2):387–399. doi: 10.1016/j.neuroimage.2010.11.004. - DOI - PubMed
    1. Pereira F, Mitchell T, Botvinick M. Machine learning classifiers and fmri: a tutorial overview. Neuroimage. 2009;45(1):S199–S209. doi: 10.1016/j.neuroimage.2008.11.007. - DOI - PMC - PubMed
    1. Haxby JV, Gobbini MI, Furey ML, Ishai A, Schouten JL, Pietrini P. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science. 2001;293(5539):2425–2430. doi: 10.1126/science.1063736. - DOI - PubMed
    1. Norman KA, Polyn SM, Detre GJ, Haxby JV. Beyond mind-reading: multi-voxel pattern analysis of fmri data. Trends Cognit Sci. 2006;10(9):424–430. doi: 10.1016/j.tics.2006.07.005. - DOI - PubMed

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