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Scalable Group Level Probabilistic Sparse Factor Analysis
Many data-driven approaches exist to extract neural representations of
functional magnetic resonance imaging (fMRI) data, but most of them lack a
proper probabilistic formulation. We propose a group level scalable
probabilistic sparse factor analysis (psFA) allowing spatially sparse maps,
component pruning using automatic relevance determination (ARD) and subject
specific heteroscedastic spatial noise modeling. For task-based and resting
state fMRI, we show that the sparsity constraint gives rise to components
similar to those obtained by group independent component analysis. The noise
modeling shows that noise is reduced in areas typically associated with
activation by the experimental design. The psFA model identifies sparse
components and the probabilistic setting provides a natural way to handle
parameter uncertainties. The variational Bayesian framework easily extends to
more complex noise models than the presently considered.Comment: 10 pages plus 5 pages appendix, Submitted to ICASSP 1
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