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...

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Bibliographic Details
Published inarXiv.org
Main Authors Hinrich, Jesper L, Nielsen, Søren F V, Riis, Nicolai A B, Eriksen, Casper T, Frøsig, Jacob, Kristensen, Marco D F, Schmidt, Mikkel N, Madsen, Kristoffer H, Mørup, Morten
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 14.12.2016
Subjects
Bayesian analysis
Design of experiments
Factor analysis
Independent component analysis
Magnetic resonance imaging
Modelling
Noise
Parameter uncertainty
Pruning
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Summary: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.
ISSN:2331-8422