Understanding multivariate brain activity: Evaluating the effect of voxelwise noise correlations on population codes in functional magnetic resonance imaging

Ru-Yuan Zhang, Xue-Xin Wei and Kendrick Kay

PLOS Computational Biology, 2020, vol. 16, issue 8, 1-29

Abstract: Previous studies in neurophysiology have shown that neurons exhibit trial-by-trial correlated activity and that such noise correlations (NCs) greatly impact the accuracy of population codes. Meanwhile, multivariate pattern analysis (MVPA) has become a mainstream approach in functional magnetic resonance imaging (fMRI), but it remains unclear how NCs between voxels influence MVPA performance. Here, we tackle this issue by combining voxel-encoding modeling and MVPA. We focus on a well-established form of NC, tuning-compatible noise correlation (TCNC), whose sign and magnitude are systematically related to the tuning similarity between two units. We show that this form of voxelwise NCs can improve MVPA performance if NCs are sufficiently strong. We also confirm these results using standard information-theoretic analyses in computational neuroscience. In the same theoretical framework, we further demonstrate that the effects of noise correlations at both the neuronal level and the voxel level may manifest differently in typical fMRI data, and their effects are modulated by tuning heterogeneity. Our results provide a theoretical foundation to understand the effect of correlated activity on population codes in macroscopic fMRI data. Our results also suggest that future fMRI research could benefit from a closer examination of the correlational structure of multivariate responses, which is not directly revealed by conventional MVPA approaches.Author summary: Noise correlation (NC) is the key component of multivariate response distributions and thus characterizing its effects on population codes is the cornerstone for understanding probabilistic computation in the brain. Despite extensive studies of NCs in neurophysiology, little is known with respect to their role in functional magnetic resonance imaging (fMRI). We characterize the effect of voxelwise NC by building voxel-encoding models and directly quantifying the amount of information in simulated multivariate fMRI data. In contrast to the detrimental effects of NC implied in neurophysiological studies, we find that voxelwise NCs can enhance information codes if NC is sufficiently strong. Our work highlights the important role of noise correlations in decipher population codes using fMRI.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1008153

DOI: 10.1371/journal.pcbi.1008153

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