Recent developments towards event-related functional magnetic resonance imaging has greatly extended the range of experimental designs. If the events occur in rapid succession, the corresponding time-locked responses overlap significantly and need to be deconvolved in order to separate the contributions of different events. Here we present a deconvolution approach, which is especially aimed at the analysis of fMRI data where sequence- or context-related responses are expected. For this purpose, we make the assumption of a hemodynamic response function (HDR) with constant yet not predefined shape but with possibly variable amplitudes. This approach reduces the number of variables to be estimated but still keeps the solutions flexible with respect to the shape. Consequently, statistical efficiency is improved. Temporal variations of the HDR strength are directly indicated by the amplitudes derived by the algorithm. Both the estimation efficiency and statistical inference are further supported by an improved estimation of the noise covariance. Using synthesized data sets, both differently shaped HDRs and varying amplitude factors were correctly identified. The gain in statistical sensitivity led to improved ratios of false- and true-positive detection rates for synthetic activations in these data. In an event-related fMRI experiment with a human subject, different HDR amplitudes could be derived corresponding to stimulation at different visual stimulus contrasts. Finally, in a visual spatial attention experiment we obtained different fMRI response amplitudes depending on the sequences of attention conditions.