Components of brain activity-data analysis for fMRI

S. Dodel, J. M. Herrmann, T. Geisel

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

Abstract

Functional magnetic resonance imaging (fMRI) is a promising method to determine noninvasively the spatial distribution of brain activity in a given situation, e.g. in response to a stimulus or during task solving. The fMRI signal is very small and often cannot be identified from the anatomical images. Thus data analysis methods are required to localize the activity. We discuss different data analysis methods, a simple correlation analysis, principal component analysis (PCA) and independent component analysis (ICA), in the context of a motor task experiment with predefined stimulus time course. We show how it is possible to detect even weak activity without prior knowledge about the stimulus time course with PCA and ICA. The stimulus time course is extracted and major components of the signal, e.g. head movements are also identified
Original languageEnglish
Title of host publicationArtificial Neural Networks, 1999. ICANN 99. Ninth International Conference on (Conf. Publ. No. 470)
PublisherIET
Pages1023-1028
Number of pages6
Volume2
ISBN (Print)0-85296-721-7
DOIs
Publication statusPublished - 1999

Keywords

  • neurophysiology
  • ICA
  • PCA
  • brain activity components
  • correlation analysis
  • data analysis
  • fMRI
  • functional magnetic resonance imaging
  • head movements
  • independent component analysis
  • motor task experiment
  • noninvasive measurement
  • principal component analysis
  • spatial distribution
  • stimulus response
  • task solving
  • thought processes

Fingerprint

Dive into the research topics of 'Components of brain activity-data analysis for fMRI'. Together they form a unique fingerprint.

Cite this