Detecting stable distributed patterns of brain activation using Gini contrast
The relationship between spatially distributed fMRI patterns and experimental stimuli or tasks offers insights into cognitive processes beyond those traceable from individual local activations. The multivariate properties of the fMRI signals allow us to infer interactions among individual regions an...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | en_US |
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Elsevier
2015
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| Online Access: | http://hdl.handle.net/1721.1/98859 https://orcid.org/0000-0003-2516-731X |
| _version_ | 1826206622285824000 |
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| author | Langs, Georg Menze, Bjoern H. Lashkari, Danial 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 Langs, Georg Menze, Bjoern H. Lashkari, Danial Golland, Polina |
| author_sort | Langs, Georg |
| collection | MIT |
| description | The relationship between spatially distributed fMRI patterns and experimental stimuli or tasks offers insights into cognitive processes beyond those traceable from individual local activations. The multivariate properties of the fMRI signals allow us to infer interactions among individual regions and to detect distributed activations of multiple areas. Detection of task-specific multivariate activity in fMRI data is an important open problem that has drawn much interest recently. In this paper, we study and demonstrate the benefits of random forest classifiers and the associated Gini importance measure for selecting voxel subsets that form a multivariate neural response. The Gini importance measure quantifies the predictive power of a particular feature when considered as part of the entire pattern. The measure is based on a random sampling of fMRI time points and voxels. As a consequence the resulting voxel score, or Gini contrast, is highly reproducible and reliably includes all informative features. The method does not rely on a priori assumptions about the signal distribution, a specific statistical or functional model or regularization. Instead, it uses the predictive power of features to characterize their relevance for encoding task information. The Gini contrast offers an additional advantage of directly quantifying the task-relevant information in a multiclass setting, rather than reducing the problem to several binary classification subproblems. In a multicategory visual fMRI study, the proposed method identified informative regions not detected by the univariate criteria, such as the t-test or the F-test. Including these additional regions in the feature set improves the accuracy of multicategory classification. Moreover, we demonstrate higher classification accuracy and stability of the detected spatial patterns across runs than the traditional methods such as the recursive feature elimination used in conjunction with support vector machines. |
| first_indexed | 2024-09-23T13:35:49Z |
| format | Article |
| id | mit-1721.1/98859 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:35:49Z |
| publishDate | 2015 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | mit-1721.1/988592022-10-01T15:54:14Z Detecting stable distributed patterns of brain activation using Gini contrast Langs, Georg Menze, Bjoern H. Lashkari, Danial Golland, Polina Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Langs, Georg Menze, Bjoern H. Lashkari, Danial Golland, Polina The relationship between spatially distributed fMRI patterns and experimental stimuli or tasks offers insights into cognitive processes beyond those traceable from individual local activations. The multivariate properties of the fMRI signals allow us to infer interactions among individual regions and to detect distributed activations of multiple areas. Detection of task-specific multivariate activity in fMRI data is an important open problem that has drawn much interest recently. In this paper, we study and demonstrate the benefits of random forest classifiers and the associated Gini importance measure for selecting voxel subsets that form a multivariate neural response. The Gini importance measure quantifies the predictive power of a particular feature when considered as part of the entire pattern. The measure is based on a random sampling of fMRI time points and voxels. As a consequence the resulting voxel score, or Gini contrast, is highly reproducible and reliably includes all informative features. The method does not rely on a priori assumptions about the signal distribution, a specific statistical or functional model or regularization. Instead, it uses the predictive power of features to characterize their relevance for encoding task information. The Gini contrast offers an additional advantage of directly quantifying the task-relevant information in a multiclass setting, rather than reducing the problem to several binary classification subproblems. In a multicategory visual fMRI study, the proposed method identified informative regions not detected by the univariate criteria, such as the t-test or the F-test. Including these additional regions in the feature set improves the accuracy of multicategory classification. Moreover, we demonstrate higher classification accuracy and stability of the detected spatial patterns across runs than the traditional methods such as the recursive feature elimination used in conjunction with support vector machines. National Science Foundation (U.S.) (Grant IIS/CRCNS 0904625) National Science Foundation (U.S.) (CAREER Grant 0642971) National Institutes of Health (U.S.) (NCRR NAC P41-RR13218) National Institutes of Health (U.S.) (Grant NIBIB NAMIC U54-EB005149) German Academy of Sciences Leopoldina (Fellowship Programme LPDS 2009-10) 2015-09-22T15:11:59Z 2015-09-22T15:11:59Z 2010-08 2010-07 Article http://purl.org/eprint/type/JournalArticle 10538119 1095-9572 http://hdl.handle.net/1721.1/98859 Langs, Georg, Bjoern H. Menze, Danial Lashkari, and Polina Golland. “Detecting Stable Distributed Patterns of Brain Activation Using Gini Contrast.” NeuroImage 56, no. 2 (May 2011): 497–507. https://orcid.org/0000-0003-2516-731X en_US http://dx.doi.org/10.1016/j.neuroimage.2010.07.074 NeuroImage Creative Commons Attribution-Noncommercial-NoDerivatives http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier MIT Web Domain |
| spellingShingle | Langs, Georg Menze, Bjoern H. Lashkari, Danial Golland, Polina Detecting stable distributed patterns of brain activation using Gini contrast |
| title | Detecting stable distributed patterns of brain activation using Gini contrast |
| title_full | Detecting stable distributed patterns of brain activation using Gini contrast |
| title_fullStr | Detecting stable distributed patterns of brain activation using Gini contrast |
| title_full_unstemmed | Detecting stable distributed patterns of brain activation using Gini contrast |
| title_short | Detecting stable distributed patterns of brain activation using Gini contrast |
| title_sort | detecting stable distributed patterns of brain activation using gini contrast |
| url | http://hdl.handle.net/1721.1/98859 https://orcid.org/0000-0003-2516-731X |
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