There is a growing interest in employing multivariate methods for analyzing fMRI data, specifically as a way to exploit spatially distributed correlations linked to events/conditions of interest. Such approaches typically focus on learning spatial decompositions which optimize either a supervised or unsupervised objective function. However, fMRI is inherently a spatio-temporal signal and a principled approach should simultaneously find the spatial and temporal filters which optimize the objective of interest.  Bilinear logistic regression (BLR) has previously been applied for simultaneous learning of topographies and temporal envelopes in event-related EEG.  Here we present a version of BLR suitable for fMRI. The goal is to extract a spatial map of discriminating voxels and an associated hemodynamical integral for optimal inference about the experimental events (i.e. decoding).