title: 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.
authors.forename: Bertrand
authors.initials: B
authors.lastname: Thirion
authors.forename: Gaël
authors.initials: G
authors.lastname: Varoquaux
authors.forename: Olivier
authors.initials: O
authors.lastname: Grisel
authors.forename: Cyril
authors.initials: C
authors.lastname: Poupon
authors.forename: Philippe
authors.initials: P
authors.lastname: Pinel
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003627:Data Interpretation, Statistical; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods; D009415:Nerve Net / Q000502:physiology*; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity