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Conference Papers Year : 2014

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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Dates and versions

hal-01015173 , version 1 (25-06-2014)

Identifiers

  • HAL Id : hal-01015173 , version 1

Cite

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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