Sparse Probabilistic Parallel Factor Analysis for the Modeling of PET and Task-fMRI Data

Vincent Beliveau, Georgios Papoutsakis, Jesper Løve Hinrich, Morten Mørup

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Modern datasets are often multiway in nature and can contain patterns common to a mode of the data (e.g. space, time, and subjects). Multiway decomposition such as parallel factor analysis (PARAFAC) take into account the intrinsic structure of the data, and sparse versions of these methods improve interpretability of the results. Here we propose a variational Bayesian parallel factor analysis (VB-PARAFAC) model and an extension with sparse priors (SP-PARAFAC). Notably, our formulation admits time and subject specific noise modeling as well as subject specific offsets (i.e., mean values). We confirmed the validity of the models through simulation and performed exploratory analysis of positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) data. Although more constrained, the proposed models performed similarly to more flexible models in approximating the PET data, which supports its robustness against noise. For fMRI, both models correctly identified task-related components, but were not able to segregate overlapping activations.
Original languageEnglish
Title of host publicationMedical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging
Volume10081
PublisherSpringer
Publication date2017
Pages186-198
ISBN (Print)9783319611877
DOIs
Publication statusPublished - 2017
Event19th International Conference on Medical Image Computing and Computer Assisted Intervention - InterContinental Athenaeum Athens, Athens, Greece
Duration: 17 Oct 201621 Oct 2016

Conference

Conference19th International Conference on Medical Image Computing and Computer Assisted Intervention
LocationInterContinental Athenaeum Athens
CountryGreece
CityAthens
Period17/10/201621/10/2016
SeriesLecture Notes in Computer Science
Volume10081
ISSN0302-9743

Keywords

  • Computer Science
  • Image Processing and Computer Vision
  • Health Informatics
  • Artificial Intelligence (incl. Robotics)
  • Probability and Statistics in Computer Science
  • Math Applications in Computer Science
  • Pattern Recognition

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