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Clustered sampling improves random subspace brain mapping (Malin Björnsdotter)
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 both approaches and demonstrate that utilizing spatial relationships yield dramatically improved voxel detection performances.