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 neurovascula r coupling requires an elaborate and sensitive response model. Methods base d on physiologic assumptions as well as direct, descriptive models have bee n proposed. The focus of this study is placed on such a direct approach tha t allows for a robust pixelwise determination of hemodynamic characteristic s, such as time to peak or the poststimulus undershoot. A Bayesian procedur e is presented that can easily be adapted to different hemodynamic properti es in question and can be estimated without numerical problems known from n onlinear optimization algorithms. The usefulness of the model is demonstrat ed by thorough analyzes of the poststimulus undershoot in visual and acoust ic stimulation paradigms. Further, we show the capability of this approach to improve analysis of fMRI data in altered hemodynamic conditions. (C) 200 1 Academic Press.