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...
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| Format: | Article |
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Public Library of Science
2018
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| Online Access: | http://hdl.handle.net/1721.1/113232 https://orcid.org/0000-0002-8964-6988 https://orcid.org/0000-0003-2377-1791 |
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| author | Caramazza, Alfonso Anzellotti, Stefano Saxe, Rebecca R |
| author2 | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences |
| author_facet | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Caramazza, Alfonso Anzellotti, Stefano Saxe, Rebecca R |
| author_sort | Caramazza, Alfonso |
| collection | MIT |
| 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 | 2024-09-23T09:59:49Z |
| format | Article |
| id | mit-1721.1/113232 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T09:59:49Z |
| publishDate | 2018 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | mit-1721.1/1132322022-09-26T15:03:11Z Multivariate pattern dependence Caramazza, Alfonso Anzellotti, Stefano Saxe, Rebecca R Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Anzellotti, Stefano Saxe, Rebecca R 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. 2018-01-19T15:15:45Z 2018-01-19T15:15:45Z 2017-11 2017-06 2018-01-19T15:06:37Z Article http://purl.org/eprint/type/JournalArticle 1553-7358 1553-734X http://hdl.handle.net/1721.1/113232 Anzellotti, Stefano, Alfonso Caramazza, and Rebecca Saxe. “Multivariate Pattern Dependence.” Edited by Saad Jbabdi. PLOS Computational Biology 13, no. 11 (November 20, 2017): e1005799. https://orcid.org/0000-0002-8964-6988 https://orcid.org/0000-0003-2377-1791 http://dx.doi.org/10.1371/journal.pcbi.1005799 PLOS Computational Biology Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0 application/pdf Public Library of Science PLoS |
| spellingShingle | Caramazza, Alfonso Anzellotti, Stefano Saxe, Rebecca R Multivariate pattern dependence |
| 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://hdl.handle.net/1721.1/113232 https://orcid.org/0000-0002-8964-6988 https://orcid.org/0000-0003-2377-1791 |
| work_keys_str_mv | AT caramazzaalfonso multivariatepatterndependence AT anzellottistefano multivariatepatterndependence AT saxerebeccar multivariatepatterndependence |