Journal
CURRENT OPINION IN NEUROBIOLOGY
Volume 13, Issue 5, Pages 620-629Publisher
CURRENT BIOLOGY LTD
DOI: 10.1016/j.conb.2003.09.012
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Funding
- Howard Hughes Medical Institute Funding Source: Medline
- NIBIB NIH HHS [P20 EB002013, P20 EB 002013] Funding Source: Medline
- NIMH NIH HHS [MH61619-03] Funding Source: Medline
- NINDS NIH HHS [P01NS41328-01A1, P01 NS041328] Funding Source: Medline
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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.
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