Conference paper
Scalable group level probabilistic sparse factor analysis
Department of Applied Mathematics and Computer Science, Technical University of Denmark1
Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark2
Visual Computing, Department of Applied Mathematics and Computer Science, Technical University of Denmark3
Technical University of Denmark4
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.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2017 |
| Pages: | 6314-6318 |
| Proceedings: | 2017 IEEE International Conference on Acoustics, Speech and Signal Processing |
| Series: | I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings |
| ISBN: | 1509041168 , 1509041176 , 1509041184 , 9781509041169 , 9781509041176 and 9781509041183 |
| ISSN: | 2379190x and 15206149 |
| Types: | Conference paper |
| DOI: | 10.1109/ICASSP.2017.7953371 |
| ORCIDs: | Hinrich, Jesper Løve , Nielsen, Søren Føns Vind , Riis, Nicolai Andre Brogaard , Schmidt, Mikkel Nørgaard , Madsen, Kristoffer Hougaard and Mørup, Morten |
