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Abstract GLMdenoise is a denoising technique for task-based fMRI. In GLMdenoise, estimates of spatially correlated noise (which may be physiological, instrumental, motion-related, or neural in origin) are derived from the data and incorporated as nuisance regressors in a general linear model (GLM) analysis. We previously showed that GLMdenoise outperforms a variety of other denoising techniques in terms of cross-validation accuracy of GLM estimates (Kay et al., 2013a). However, the practical impact of denoising for experimental studies remains unclear. Here we examine whether and to what extent GLMdenoise improves sensitivity in the context of multivariate pattern analysis of fMRI data. On a large number of participants (31 participants across 4 experiments; 3 T, gradient-echo, spatial resolution 2–3.75 mm, temporal resolution 1.3–2 s, number of conditions 32–75), we perform representational similarity analysis (Kriegeskorte et al., 2008a) as well as pattern classification (Haxby et al., 2001). We find that GLMdenoise substantially improves replicability of representational dissimilarity matrices (RDMs) across independent splits of each participant's dataset (average RDM replicability increases from r = 0.46 to r = 0.61). Additionally, we find that GLMdenoise substantially improves pairwise classification accuracy (average classification accuracy increases from 79% correct to 84% correct). We show that GLMdenoise often improves and never degrades performance for individual participants and that GLMdenoise also improves across-participant consistency. We conclude that GLMdenoise is a useful tool that can be routinely used to maximize the amount of information extracted from fMRI activity patterns. Highlights • GLMdenoise improves pattern classification of fMRI data. • GLMdenoise improves representational similarity analyses (RSA) of fMRI data. • GLMdenoise improves consistency of RSA results within and across subjects. • GLMdenoise improves brain-behavior correlation. • GLMdenoise outperforms classical approaches in denoising fMRI data for MVPA. [ABSTRACT FROM AUTHOR]

Abstract External information can modify the subjective value of a tasted stimulus, but little is known about neural mechanisms underlying these behavioral modifications. This study used flavored drinks to produce variable degrees of discrepancy between expected and received flavor. During a learning session, 43 healthy young men learned 4 symbol-flavor associations. In a separate session, associations were presented again during an fMRI scan, but half of the trials introduced discrepancy with previously learned associations. Liking ratings of drinks were collected and were analyzed using a linear model to define the degree to which discrepant symbols affected liking ratings of the subjects during the fMRI session. Based on these results, a GLM analysis of fMRI data was conducted to determine neural correlates of observed behavior. Groups of subjects were composed based on their behavior in response to discrepant symbols, and comparison of brain activity between groups showed that activation in the PCC and the caudate nucleus was more potent in those subjects in which liking was not affected by discrepant symbols. These activations were not found in subjects who assimilated unexpected flavors to flavors preceeded by discrepant symbols. Instead, these subjects showed differences in the activity in the parietal operculum. The activity of reward network appears to be related to assimilation of received flavor to expected flavor in response to symbol-flavor discrepancy. Highlights • Impact of visual cues on liking for unexpected drinks varied between subjects. • Valuation network was only activated in subjects whose liking was not affected by discrepancy. • Insula and sensory cortices also could be involved in the assimilation. [ABSTRACT FROM AUTHOR]

Abstract We explore a method for reconstructing visual stimuli from brain activity. Using large databases of natural images we trained a deep convolutional generative adversarial network capable of generating gray scale photos, similar to stimuli presented during two functional magnetic resonance imaging experiments. Using a linear model we learned to predict the generative model's latent space from measured brain activity. The objective was to create an image similar to the presented stimulus image through the previously trained generator. Using this approach we were able to reconstruct structural and some semantic features of a proportion of the natural images sets. A behavioural test showed that subjects were capable of identifying a reconstruction of the original stimulus in 67.2% and 66.4% of the cases in a pairwise comparison for the two natural image datasets respectively. Our approach does not require end-to-end training of a large generative model on limited neuroimaging data. Rapid advances in generative modeling promise further improvements in reconstruction performance. Highlights • A generative adversarial network (DCGAN) is used for reconstructing visual percepts. • Minimizing image loss, a linear model learns to predict the latent space from BOLD. • With a GAN limited to 6 handwritten characters, detailed features can be retrieved. • Reconstructions of arbitrary natural images are identifiable by human raters. • The specific GAN is a component and replaceable by advanced deterministic generators. [ABSTRACT FROM AUTHOR]