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Principal Component Regression predicts functional responses across individuals

Abstract : Inter-subject variability is a major hurdle for neuroimaging group-level inference, as it creates complex image patterns that are not captured by standard analysis models and jeopardizes the sensitivity of statistical procedures. A solution to this problem is to model random subjects effects by using the redundant information conveyed by multiple imaging contrasts. In this paper, we introduce a novel analysis framework, where we estimate the amount of variance that is fit by a random effects subspace learned on other images; we show that a principal component regression estimator outperforms other regression models and that it fits a significant proportion (10% to 25%) of the between-subject variability. This proves for the first time that the accumulation of contrasts in each individual can provide the basis for more sensitive neuroimaging group analyzes.
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Submitted on : Wednesday, June 25, 2014 - 11:32:14 PM
Last modification on : Tuesday, October 25, 2022 - 4:16:16 PM
Long-term archiving on: : Thursday, September 25, 2014 - 11:49:27 AM


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  • HAL Id : hal-01015173, version 1


Bertrand Thirion, Gaël Varoquaux, Olivier Grisel, Cyril Poupon, Philippe Pinel. Principal Component Regression predicts functional responses across individuals. MICCAI, Sep 2014, Boston, United States. ⟨hal-01015173⟩



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