Scalable group level probabilistic sparse factor analysis

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Abstract

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
Publication statusPublished - 2017
Event2017 IEEE International Conference on Acoustics, Speech and Signal Processing - Hilton New Orleans Riverside, New Orleans, United States
Duration: 5 Mar 20179 Mar 2017
Conference number: 42
http://www.ieee-icassp2017.org/

Conference

Conference2017 IEEE International Conference on Acoustics, Speech and Signal Processing
Number42
LocationHilton New Orleans Riverside
Country/TerritoryUnited States
CityNew Orleans
Period05/03/201709/03/2017
Internet address
SeriesI E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
ISSN1520-6149

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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