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- W2116799974 abstract "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." @default.
- W2116799974 created "2016-06-24" @default.
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- W2116799974 date "2003-10-01" @default.
- W2116799974 modified "2023-10-09" @default.
- W2116799974 title "Independent component analysis of functional MRI: what is signal and what is noise?" @default.
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- W2116799974 doi "https://doi.org/10.1016/j.conb.2003.09.012" @default.
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