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Inverse Transformed Encoding Models – a solution to the problem of correlated trial-by-trial parameter estimates in fMRI decoding

Soch, J., Allefeld, C. ORCID: 0000-0002-1037-2735 & Haynes, J-D. (2019). Inverse Transformed Encoding Models – a solution to the problem of correlated trial-by-trial parameter estimates in fMRI decoding. NeuroImage, 209, article number 116449. doi: 10.1101/610626


Techniques of multivariate pattern analysis (MVPA) can be used to decode the discrete experimental condition or a continuous modulator variable from measured brain activity during a particular trial. In functional magnetic resonance imaging (fMRI), trial-wise response amplitudes are sometimes estimated from the measured signal 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 are highly variable and serially correlated due to the temporally extended shape of the hemodynamic response function (HRF). Here, we describe inverse transformed encoding modelling (ITEM), a principled approach of accounting for those serial correlations and decoding from the resulting estimates, at low computational cost and with no loss in statistical power. We use simulated data to show that ITEM outperforms the current standard approach in terms of decoding accuracy and analyze empirical data to demonstrate that ITEM is capable of visual reconstruction from fMRI signals.

Publication Type: Article
Additional Information: ©2019 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license ( This article has been published in NeuroImage, DOI:
Publisher Keywords: fMRI decoding, multivariate pattern analysis, trial-wise parameter estimates, general linear model, multivariate GLM, model inversion, classification, reconstruction
Subjects: R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Departments: School of Health & Psychological Sciences > Psychology
SWORD Depositor:
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