Bayesian Modeling of the Hemodynamic Response Function in BOLD fMRI

In functional magnetic resonance imaging (fMRI), modeling the complex link between neuronal activity and its hemodynamic response via the neurovascular coupling requires an elaborate and sensitive response model. Methods based on physiologic assumptions as well as direct, descriptive models have been proposed. The focus of this study is placed on such a direct approach that allows for a robust pixelwise determination of hemodynamic characteristics, such as time to peak or the poststimulus undershoot. A Bayesian procedure is presented that can easily be adapted to different hemodynamic properties in question and can be estimated without numerical problems known from nonlinear optimization algorithms. The usefulness of the model is demonstrated by thorough analyzes of the poststimulus undershoot in visual and acoustic stimulation paradigms. Further, we show the capability of this approach to improve analysis of fMRI data in altered hemodynamic conditions.

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