Dynamic models in fMRI.

C Gössl; D P Auer; L Fahrmeir

doi:10.1002/(sici)1522-2594(200001)43:1<72::aid-mrm9>3.0.co;2-y

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PubMed · 10642733

Dynamic models in fMRI.

C Gössl·D P Auer·L Fahrmeir

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Abstract

Most statistical methods for assessing activated voxels in fMRI experiments are based on correlation or regression analysis. In this context, the main assumptions are that the baseline can be described by a few known basis functions or variables and that the effect of the stimulus, i.e., the activation, stays constant over time. As these assumptions are in many cases neither necessary nor correct, a new dynamic approach that does not depend on those assumptions will be presented. This allows for simultaneous nonparametric estimation of the baseline and, as an important feature, of time-varying effects of stimulation. This method of estimating the stimulus related areas of the brain furthermore provides the possibility to analyze the temporal and spatial evolution of the activation within an fMRI experiment.

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Algorithms Brain Humans Magnetic Resonance Imaging Models, Neurological Models, Statistical ROC Curve Reproducibility of Results Sensitivity and Specificity Statistics, Nonparametric Journal Article Research Support, Non-U.S. Gov't

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BibTeX RIS

C Gössl, D P Auer, L Fahrmeir. 2000. Dynamic models in fMRI.. https://doi.org/10.1002/(sici)1522-2594(200001)43%3A1%3C72%3A%3Aaid-mrm9%3E3.0.co%3B2-y

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