Hierarchical topographic factor analysis

Jeremy R. Manning, Rajesh Ranganath, Waitsang Keung, Nicholas B. Turk-Browne, Jonathan D. Cohen, Kenneth A. Norman, David M. Blei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recent work has revealed that cognitive processes are often reflected in patterns of functional connectivity throughout the brain (for review see [16]). However, examining functional connectivity patterns using traditional methods carries a substantial computational burden (of computing time and memory). Here we present a technique, termed Hierarchical topographic factor analysis, for efficiently discovering brain networks in large multi-subject neuroimaging datasets.

Original languageEnglish (US)
Title of host publicationProceedings - 2014 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2014
PublisherIEEE Computer Society
ISBN (Print)9781479941506
DOIs
StatePublished - 2014
Event4th International Workshop on Pattern Recognition in Neuroimaging, PRNI 2014 - Tubingen, Germany
Duration: Jun 4 2014Jun 6 2014

Publication series

NameProceedings - 2014 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2014

Other

Other4th International Workshop on Pattern Recognition in Neuroimaging, PRNI 2014
CountryGermany
CityTubingen
Period6/4/146/6/14

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Biomedical Engineering

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  • Cite this

    Manning, J. R., Ranganath, R., Keung, W., Turk-Browne, N. B., Cohen, J. D., Norman, K. A., & Blei, D. M. (2014). Hierarchical topographic factor analysis. In Proceedings - 2014 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2014 [6858530] (Proceedings - 2014 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2014). IEEE Computer Society. https://doi.org/10.1109/PRNI.2014.6858530