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, Kendrick Kay

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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, tuningcompatible 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.

Original languageEnglish (US)
Article numbere1008153
JournalPLoS computational biology
Volume16
Issue number8
DOIs
StatePublished - Aug 2020

Bibliographical note

Funding Information:
The work was supported by NIH Grants P41 EB015894, P30 NS076408, S10 RR026783, S10 OD017974-01; NSF NeuroNex Award DBI- 1707398; the Gatsby Charitable Foundation; and the W.M. Keck Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Publisher Copyright:
© 2020 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Keywords

  • Brain/diagnostic imaging
  • Computer Simulation
  • Humans
  • Magnetic Resonance Imaging/methods
  • Multivariate Analysis
  • Neurons/physiology
  • Signal Processing, Computer-Assisted

PubMed: MeSH publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Journal Article
  • Research Support, N.I.H., Extramural

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