Independent component analysis of functional MRI: what is signal and what is noise?

Curr Opin Neurobiol. 2003 Oct;13(5):620-9. doi: 10.1016/j.conb.2003.09.012.


Many sources of fluctuation contribute to the functional magnetic resonance imaging (fMRI) signal, complicating attempts to infer those changes that are truly related to brain activation. Unlike methods of analysis of fMRI data that test the time course of each voxel against a hypothesized waveform, data-driven methods, such as independent component analysis and clustering, attempt to find common features within the data. This exploratory approach can be revealing when the brain activation is difficult to predict beforehand, such as with complex stimuli and internal shifts of activation that are not time-locked to an easily specified sensory or motor event. These methods can be further improved by incorporating prior knowledge regarding the temporal and spatial extent of brain activation.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.
  • Review

MeSH terms

  • Animals
  • Electric Conductivity / adverse effects*
  • Humans
  • Magnetic Resonance Imaging / methods*
  • Principal Component Analysis / methods*