Deconvolution of event-related fMRI responses in fast-rate experimental designs: Tracking amplitude variations

Recent developments towards event-related functional magnetic resonance ima ging has greatly err-ended 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 contribu tions 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 o f a hemodynamic response function (HDR) with constant yet not predefined sh ape but with possibly variable amplitudes. This approach reduces the number of variables to be estimated but still keeps the solutions flexible with r espect to the shape. Consequently, statistical efficiency is improved. Temp oral variations of the HDR strength are directly indicated by the amplitude s derived by the algorithm. Both the estimation efficiency and statistical inference are further supported by an improved estimation of the noise cova riance. Using synthesized data sets, both differently shaped HDRs and varyi ng amplitude factors were correctly identified. The gain in statistical sen sitivity led to improved ratios of false- and true-positive detection rates for synthetic activations in these data. In an event-related fMRI experime nt with a human subject, different HDR amplitudes could be derived correspo nding to stimulation at different visual stimulus contrasts. Finally, in a visual spatial attention experiment we obtained different fMRI response amp litudes depending on the sequences of attention conditions.