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Detecting Stable Distributed Patterns of Brain Activation using Gini Contrast

Institution:
1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
2Asclepios Research Project, INRIA Sophia-Antipolis, France.
Publisher:
Elsevier Science
Publication Date:
May-2011
Journal:
Neuroimage
Volume Number:
56
Issue Number:
2
Pages:
497-507
Citation:
Neuroimage. 2011 May 15;56(2):497-507.
PubMed ID:
20709176
PMCID:
PMC3960973
Keywords:
Projects:GiniContrast
Appears in Collections:
NAC, NA-MIC
Sponsors:
P41 RR013218/RR/NCRR NIH HHS/United States
P41 EB015902/EB/NIBIB NIH HHS/United States
Generated Citation:
Langs G., Menze B.H., Lashkari D., Golland P. Detecting Stable Distributed Patterns of Brain Activation using Gini Contrast. Neuroimage. 2011 May 15;56(2):497-507. PMID: 20709176. PMCID: PMC3960973.
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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.

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