Multivariate pattern dependence.
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2017-11-01
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| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC5714382?pdf=render |
| _version_ | 1818209994293641216 |
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| author | Stefano Anzellotti Alfonso Caramazza Rebecca Saxe |
| author_facet | Stefano Anzellotti Alfonso Caramazza Rebecca Saxe |
| author_sort | Stefano Anzellotti |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T05:09:33Z |
| format | Article |
| id | doaj.art-1b31bf7969e440ee880141b4bac4be94 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-12T05:09:33Z |
| publishDate | 2017-11-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-1b31bf7969e440ee880141b4bac4be942022-12-22T00:36:59ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582017-11-011311e100579910.1371/journal.pcbi.1005799Multivariate pattern dependence.Stefano AnzellottiAlfonso CaramazzaRebecca SaxeWhen 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.http://europepmc.org/articles/PMC5714382?pdf=render |
| spellingShingle | Stefano Anzellotti Alfonso Caramazza Rebecca Saxe Multivariate pattern dependence. PLoS Computational Biology |
| title | Multivariate pattern dependence. |
| title_full | Multivariate pattern dependence. |
| title_fullStr | Multivariate pattern dependence. |
| title_full_unstemmed | Multivariate pattern dependence. |
| title_short | Multivariate pattern dependence. |
| title_sort | multivariate pattern dependence |
| url | http://europepmc.org/articles/PMC5714382?pdf=render |
| work_keys_str_mv | AT stefanoanzellotti multivariatepatterndependence AT alfonsocaramazza multivariatepatterndependence AT rebeccasaxe multivariatepatterndependence |