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PubMed · 15193593

Detection and detrending in fMRI data analysis.

Ola Friman·Magnus Borga·Peter Lundberg·Hans Knutsson
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Abstract

This article addresses the impact that colored noise, temporal filtering, and temporal detrending have on the fMRI analysis situation. Specifically, it is shown why the detection of event-related designs benefit more from pre-whitening than blocked designs in a colored noise structure. Both theoretical and empirical results are provided. Furthermore, a novel exploratory method for producing drift models that efficiently capture trends and drifts in the fMRI data is introduced. A comparison to currently employed detrending approaches is presented. It is shown that the novel exploratory model is able to remove a major part of the slowly varying drifts that are abundant in fMRI data. The value of such a model lies in its ability to remove drift components that otherwise would have contributed to a colored noise structure in the voxel time series.

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BrainBrain MappingEvoked PotentialsHumansLeast-Squares AnalysisMagnetic Resonance ImagingModels, NeurologicalModels, StatisticalProbabilityReproducibility of ResultsJournal ArticleResearch Support, Non-U.S. Gov't

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Ola Friman, Magnus Borga, Peter Lundberg, Hans Knutsson. 2004. Detection and detrending in fMRI data analysis.. https://doi.org/10.1016/j.neuroimage.2004.01.033

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