Multivariate pattern dependence
Name
journal.pcbi.1005799.pdf
Size
8.18 MB
Format
Adobe PDF
Checksum (MD5)
18e934403b8731d9a4c62f4a5a15b410
Author(s)
Caramazza, Alfonso
Anzellotti, Stefano
Saxe, Rebecca R
Date Issued
November 2017
Journal
PLOS Computational Biology
Publisher
Public Library of Science
Citation
Anzellotti, Stefano, Alfonso Caramazza, and Rebecca Saxe. “Multivariate Pattern Dependence.” Edited by Saad Jbabdi. PLOS Computational Biology 13, no. 11 (November 20, 2017): e1005799.
Abstract
When we perform a cognitive task, multiple brain regions are engaged. Understanding how these regions interact is a fundamental step to uncover the neural bases of behavior. Most research on the interactions between brain regions has focused on the univariate responses in the regions. However, fine grained patterns of response encode important information, as shown by multivariate pattern analysis. In the present article, we introduce and apply multivariate pattern dependence (MVPD): a technique to study the statistical dependence between brain regions in humans in terms of the multivariate relations between their patterns of responses. MVPD characterizes the responses in each brain region as trajectories in region-specific multidimensional spaces, and models the multivariate relationship between these trajectories. We applied MVPD to the posterior superior temporal sulcus (pSTS) and to the fusiform face area (FFA), using a searchlight approach to reveal interactions between these seed regions and the rest of the brain. Across two different experiments, MVPD identified significant statistical dependence not detected by standard functional connectivity. Additionally, MVPD outperformed univariate connectivity in its ability to explain independent variance in the responses of individual voxels. In the end, MVPD uncovered different connectivity profiles associated with different representational subspaces of FFA: the first principal component of FFA shows differential connectivity with occipital and parietal regions implicated in the processing of low-level properties of faces, while the second and third components show differential connectivity with anterior temporal regions implicated in the processing of invariant representations of face identity.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution 4.0 International License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1371/journal.pcbi.1005799
