Discovering Structure in the Space of fMRI Selectivity Profiles
We present a method for discovering patterns of selectivity in fMRI data for experiments with multiple stimuli/tasks. We introduce a representation of the data as profiles of selectivity using linear regression estimates, and employ mixture model density estimation to identify functional systems wit...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Elsevier
2012
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| Online Access: | http://hdl.handle.net/1721.1/69952 https://orcid.org/0000-0003-3853-7885 https://orcid.org/0000-0003-2516-731X |
| _version_ | 1826214216813510656 |
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| author | Lashkari, Danial Vul, Edward Kanwisher, Nancy Golland, Polina |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Lashkari, Danial Vul, Edward Kanwisher, Nancy Golland, Polina |
| author_sort | Lashkari, Danial |
| collection | MIT |
| description | We present a method for discovering patterns of selectivity in fMRI data for experiments with multiple stimuli/tasks. We introduce a representation of the data as profiles of selectivity using linear regression estimates, and employ mixture model density estimation to identify functional systems with distinct types of selectivity. The method characterizes these systems by their selectivity patterns and spatial maps, both estimated simultaneously via the EM algorithm. We demonstrate a corresponding method for group analysis that avoids the need for spatial correspondence among subjects. Consistency of the selectivity profiles across subjects provides a way to assess the validity of the discovered systems. We validate this model in the context of category selectivity in visual cortex, demonstrating good agreement with the findings based on prior hypothesis-driven methods. |
| first_indexed | 2024-09-23T16:01:57Z |
| format | Article |
| id | mit-1721.1/69952 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:01:57Z |
| publishDate | 2012 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | mit-1721.1/699522022-09-29T17:45:03Z Discovering Structure in the Space of fMRI Selectivity Profiles Lashkari, Danial Vul, Edward Kanwisher, Nancy Golland, Polina Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Kanwisher, Nancy Lashkari, Danial Vul, Edward Kanwisher, Nancy Golland, Polina We present a method for discovering patterns of selectivity in fMRI data for experiments with multiple stimuli/tasks. We introduce a representation of the data as profiles of selectivity using linear regression estimates, and employ mixture model density estimation to identify functional systems with distinct types of selectivity. The method characterizes these systems by their selectivity patterns and spatial maps, both estimated simultaneously via the EM algorithm. We demonstrate a corresponding method for group analysis that avoids the need for spatial correspondence among subjects. Consistency of the selectivity profiles across subjects provides a way to assess the validity of the discovered systems. We validate this model in the context of category selectivity in visual cortex, demonstrating good agreement with the findings based on prior hypothesis-driven methods. McGovern Institute Neurotechnology (MINT) Program National Institutes of Health (U.S.) (Grant NIBIB NAMIC U54-EB005149) National Institutes of Health (U.S.) (Grant NCRR NAC P41-RR13218) National Eye Institute (grant 13455) National Science Foundation (U.S.) (grant CAREER 0642971) Collaborative Research in Computational Neuroscience (IIS/CRCNS 0904625) Deshpande Center for Technological Innovation (MIT HST Catalyst grant) American Society for Engineering Education. National Defense Science and Engineering Graduate Fellowship 2012-04-05T16:52:03Z 2012-04-05T16:52:03Z 2010-01 2009-12 Article http://purl.org/eprint/type/JournalArticle 1053-8119 1095-9572 http://hdl.handle.net/1721.1/69952 Lashkari, Danial et al. “Discovering Structure in the Space of fMRI Selectivity Profiles.” NeuroImage 50.3 (2010): 1085–1098. Web. 5 Apr. 2012. https://orcid.org/0000-0003-3853-7885 https://orcid.org/0000-0003-2516-731X en_US http://dx.doi.org/10.1016/j.neuroimage.2009.12.106 NeuroImage Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf Elsevier PubMed Central |
| spellingShingle | Lashkari, Danial Vul, Edward Kanwisher, Nancy Golland, Polina Discovering Structure in the Space of fMRI Selectivity Profiles |
| title | Discovering Structure in the Space of fMRI Selectivity Profiles |
| title_full | Discovering Structure in the Space of fMRI Selectivity Profiles |
| title_fullStr | Discovering Structure in the Space of fMRI Selectivity Profiles |
| title_full_unstemmed | Discovering Structure in the Space of fMRI Selectivity Profiles |
| title_short | Discovering Structure in the Space of fMRI Selectivity Profiles |
| title_sort | discovering structure in the space of fmri selectivity profiles |
| url | http://hdl.handle.net/1721.1/69952 https://orcid.org/0000-0003-3853-7885 https://orcid.org/0000-0003-2516-731X |
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