Decoding fMRI with temporal integration: Learning the hemodynamical response function

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. [1] Bilinear logistic regression (BLR) has previously been applied for simultaneous learning of topographies and temporal envelopes in event-related EEG. [2] 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).

Accepted 3 November 2007
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