Abstract
Multi-voxel pattern analysis (MVPA) is a common technique of pattern-information fMRI, which, through the process of feature selection and subsequent classification, can aid the detection of groups of informative voxels that can be used to discriminate between competing stimuli. Networks of features have been long extracted univariately but recently researchers have turned to the development of multivariate techniques that also move from being purely mathematical, to have a more physiological meaning. In this work, we demonstrate a multivariate feature selection method that uses information encoded in the 3D spatial distribution of activated voxels at each anatomical region of the brain, in order to extract networks of informative regions that can act as generic features for running MVPA across subjects.
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Markides, L., Gillies, D.F. (2012). On the Creation of Generic fMRI Feature Networks Using 3-D Moment Invariants. In: Wang, F., Shen, D., Yan, P., Suzuki, K. (eds) Machine Learning in Medical Imaging. MLMI 2012. Lecture Notes in Computer Science, vol 7588. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35428-1_17
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DOI: https://doi.org/10.1007/978-3-642-35428-1_17
Publisher Name: Springer, Berlin, Heidelberg
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