Analysis of correlated activity in fMRI data by artificial neural networks

M. Voultsidou, S. Dodel, J. M. Herrmann

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

Abstract

Clusters of correlated activity in fMRI data can identify regions of interest and indicate interacting brain areas. Because the extraction of clusters is computationally complex, we apply an approximative method which is based on Hopfield networks. It allows to find clusters of various degrees of connectivity ranging between the two extreme cases of cliques and connectivity components. Further we propose a criterion which allows to evaluate the relevance of such structures based on the robustness with respect to parameter variations.
Original languageEnglish
Title of host publicationBiomedical Imaging: Nano to Macro, 2004. IEEE International Symposium on
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages872-875
Number of pages4
Volume1
ISBN (Print)0-7803-8388-5
DOIs
Publication statusPublished - 1 Apr 2004

Keywords

  • Hopfield neural nets
  • biomedical MRI
  • brain
  • medical computing
  • statistical analysis
  • Hopfield networks
  • artificial neural networks
  • correlated activity
  • functional magnetic resonance imaging
  • interacting brain areas
  • Artificial intelligence
  • Artificial neural networks
  • Character generation
  • Chromium
  • Intelligent networks
  • Variable speed drives

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