Skip to Main content Skip to Navigation
Journal articles

Population shrinkage of covariance (PoSCE) for better individual brain functional-connectivity estimation

Abstract : Estimating covariances from functional Magnetic Resonance Imaging at rest (r-fMRI) can quantify interactions between brain regions. Also known as brain functional connectivity, it reflects inter-subject variations in behavior and cognition, and characterizes neuropathologies. Yet, with noisy and short time-series, as in r-fMRI, covariance estimation is challenging and calls for penalization, as with shrinkage approaches. We introduce population shrinkage of covariance estimator (PoSCE) : a covariance estimator that integrates prior knowledge of covariance distribution over a large population, leading to a non-isotropic shrinkage. The shrinkage is tailored to the Riemannian geometry of symmetric positive definite matrices. It is coupled with a probabilistic modeling of the individual and population covariance distributions. Experiments on two large r-fMRI datasets (HCP n=815, Cam-CAN n=626) show that PoSCE has a better bias-variance trade-off than existing covariance estimates: this estimator relates better functional-connectivity measures to cognition while capturing well intra-subject functional connectivity.
Complete list of metadata

Cited literature [54 references]  Display  Hide  Download
Contributor : Mehdi Rahim Connect in order to contact the contributor
Submitted on : Thursday, March 14, 2019 - 8:17:13 PM
Last modification on : Monday, December 13, 2021 - 9:16:09 AM
Long-term archiving on: : Saturday, June 15, 2019 - 7:21:21 PM


Files produced by the author(s)



Mehdi Rahim, Bertrand Thirion, Gaël Varoquaux. Population shrinkage of covariance (PoSCE) for better individual brain functional-connectivity estimation. Medical Image Analysis, Elsevier, 2019, ⟨10.1016/⟩. ⟨hal-02068389⟩



Les métriques sont temporairement indisponibles