| Paper: | PS-1B.40 |
| Session: | Poster Session 1B |
| Location: | Symphony/Overture |
| Session Time: | Thursday, September 6, 18:45 - 20:45 |
| Presentation Time: | Thursday, September 6, 18:45 - 20:45 |
| Presentation: |
Poster
|
| Publication: |
2018 Conference on Cognitive Computational Neuroscience, 5-8 September 2018, Philadelphia, Pennsylvania |
| Paper Title: |
The impact of noise correlation on multivariate pattern classification in fMRI |
| Manuscript: |
Click here to view manuscript |
| DOI: |
https://doi.org/10.32470/CCN.2018.1186-0 |
| Authors: |
RUYUAN ZHANG, Kendrick Kay, University of Minnesota, United States |
| Abstract: |
Previous theoretical work has shown that noise correlations (NC) that have the same sign as signal correlations (SC) limit the amount of information encoded in neural population responses. A recent fMRI study replicated this finding in functional magnetic resonance imaging (fMRI) data (Bejjanki et al., 2017). Here, we aim to gain further understanding of how NCs between voxels alter the accuracy of multivariate pattern classification (MVPC), a popular analysis method in fMRI research. In a simulated fMRI orientation experiment, voxel responses were simulated using an encoding model and then classified using linear discriminant analysis. We evaluated two forms of NC: one proportional (i.e., tuning-compatible NC) to the SC between voxels and the other one independent (i.e., tuning-independent NC) of the SC between voxels. Surprisingly, our results show that both the tuning-compatible NCs and the tuning-independent NCs improve MVPC accuracy. We show that these results stem from two major factors: (1) classifiers can “greedily” select voxels that have informative covariance and (2) the SC defined using tuning for two orientations is different from the SC defined using full orientation tuning curves. Taken together, our results provide a theoretical foundation for understanding the effect of NC on MVPC in future experimental studies. |
