Scalable group level probabilistic sparse factor analysis

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2017

DOI

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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 scalable group level 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.
Original languageEnglish
Title of host publicationProceedings of 2017 IEEE International Conference on Acoustics, Speech and Signal Processing
PublisherIEEE
Publication date2017
Pages6314-6318
ISBN (print)9781509041169
DOIs
StatePublished - 2017
Event19th International Conference on Acoustics, Speech and Signal Processing - Kyoto, Japan

Conference

Conference19th International Conference on Acoustics, Speech and Signal Processing
LocationKyoto Brighton Hotel
CountryJapan
CityKyoto
Period05/03/201709/03/2017
SeriesI E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
ISSN1520-6149
CitationsWeb of Science® Times Cited: No match on DOI

    Keywords

  • Probabilistic logic, Principal component analysis, Analytical models, Data models, Correlation, Indexes, Graphics processing units, sparsity, Probabilistic factor analysis, Neuroimaging, Variational Bayes, Independent components
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