2011
DOI: 10.1016/j.neuroimage.2010.07.073
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Encoding and decoding in fMRI

Abstract: Over the past decade fMRI researchers have developed increasingly sensitive techniques for analyzing the information represented in BOLD activity. The most popular of these techniques is linear classification, a simple technique for decoding information about experimental stimuli or tasks from patterns of activity across an array of voxels. A more recent development is the voxel-based encoding model, which describes the information about the stimulus or task that is represented in the activity of single voxels… Show more

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Cited by 746 publications
(787 citation statements)
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References 94 publications
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“…In a previous investigation of English monolingual speakers, both directions yielded similar accuracies in the mapping between the activation patterns and the NPSFs of sentences (Wang, Cherkassky, & Just, submitted). Neural activation prediction models, such as the one used in the current study, have several scientific advantages, at least in the initial stage of model development (Naselaris, Kay, Nishimoto, & Gallant, 2011). The most salient advantage is that it can yield a functional characterization of specific brain regions that can be compared to regional characterizations in other studies.…”
Section: Prediction Directionmentioning
confidence: 99%
“…In a previous investigation of English monolingual speakers, both directions yielded similar accuracies in the mapping between the activation patterns and the NPSFs of sentences (Wang, Cherkassky, & Just, submitted). Neural activation prediction models, such as the one used in the current study, have several scientific advantages, at least in the initial stage of model development (Naselaris, Kay, Nishimoto, & Gallant, 2011). The most salient advantage is that it can yield a functional characterization of specific brain regions that can be compared to regional characterizations in other studies.…”
Section: Prediction Directionmentioning
confidence: 99%
“…This improved multivariate performance allows estimation of quantities such as conditional mutual information (CMI) [Ince et al, 2012], directed information (DI; also called transfer entropy [TE]) [Ince et al, 2015; Massey, 1990; Schreiber, 2000] as well as measures quantifying pairwise interactions between variables [Chicharro, 2014; Panzeri et al, 2008]. We believe these higher order information theoretic quantities have the potential to provide transformative new interpretations of neuroimaging data, by providing a unified framework for analyses based on the information content of neural signals [Kriegeskorte and Bandettini, 2007; Naselaris et al, 2011; Schyns et al, 2009]. The methods we present enable study of the representation, processing, and communication in the brain of multiple features of the external world [Ince et al, 2015].…”
Section: Introductionmentioning
confidence: 99%
“…It has become a central tool in neuroimage [1]. In particular, linear estimators can highlight the brain maps that lead to the identification of cognitive labels [2] [3]. Yet, to date, decoding is still orders of magnitude slower than standard analysis.…”
Section: Introductionmentioning
confidence: 99%