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• W17789669 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." @default.
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• W17789669 date "2014-01-01" @default.
• W17789669 modified "2023-09-25" @default.
• W17789669 title "Principal Component Regression Predicts Functional Responses across Individuals" @default.
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• W17789669 doi "https://doi.org/10.1007/978-3-319-10470-6_92" @default.
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