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Clustered sampling improves random subspace brain mapping

Journal article
Authors Malin Björnsdotter
Johan Wessberg
Published in Pattern recognition
Volume 45
Issue 6
Pages 2035-2040
ISSN 0031-3203
Publication year 2012
Published at Institute of Neuroscience and Physiology
Institute of Neuroscience and Physiology, Department of Physiology
Pages 2035-2040
Language en
Links dx.doi.org/10.1016/j.patcog.2011.04...
Keywords fMRI, Random subspace, Feature selection, Brain mapping
Subject categories Signal Processing, Medical technology, Clinical neurophysiology, Neurophysiology

Abstract

Intuitive and efficient, the random subspace ensemble approach provides an appealing solution to the problem of the vast dimensionality of functional magnetic resonance imaging (fMRI) data for maximal-accuracy brain state decoding. Recently, efforts to generate biologically plausible and interpretable maps of brain regions which contribute information to the ensemble decoding task have been made and two approaches have been introduced: globally multivariate random subsampling and locally multivariate Monte Carlo mapping. Both types of maps reflect voxel-wise decoding accuracies averaged across repeatedly randomly sampled voxel subsets, highlighting voxels which consistently participate in high-classification subsets. We compare the mapping sensitivities of the approaches on realistic simulated data containing both locally and globally multivariate information and demonstrate that utilizing the inherent volumetric nature of fMRI through clustered Monte Carlo mapping yields dramatically improved performances in terms of voxel detection sensitivity and efficiency. These results suggest that, unless a priori information specifically dictates a global search, variants of clustered sampling should be the priority for random subspace brain mapping.

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