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Abstract

In multivariate pattern analysis (MVPA) for functional magnetic resonance imaging (fMRI) signals, trial-wise response amplitudes are sometimes estimated using a general linear model (GLM) with one onset regressor for each trial. When using rapid event-related designs with trials closely spaced in time, those estimates can be highly correlated due to the temporally smoothed shape of the hemodynamic response function. In previous work (Soch, J., Allefeld, C., & Haynes, J.-D. (2020). Inverse transformed encoding models – a solution to the problem of correlated trialby-trial parameter estimates in fMRI decoding. NeuroImage, 209, 116449, 1-19. https://doi.org/10.1016/j.neuroimage.2019.116449), we have proposed inverse transformed encoding modelling (ITEM), a principled approach for trial-wise decoding from fMRI signals in the presence of trial-by-trial correlations. Here, we (i) perform simulation studies addressing its performance for multivariate signals and (ii) present searchlightbased ITEM analysis – which allows to predict a variable of interest from the vicinity of each voxel in the brain. We empirically validate the approach by confirming a priori plausible hypotheses about the well-understood visual system.