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Robust Fréchet Mean and PGA on Riemannian Manifolds with Applications to Neuroimaging Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Hierarchical Region-Network Sparsity for High-Dimensional Inference in Brain Imaging

Authors : Danilo Bzdok, Michael Eickenberg, Gaël Varoquaux, Bertrand Thirion

Published in: Information Processing in Medical Imaging

Publisher: Springer International Publishing

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Abstract

Structured sparsity penalization has recently improved statistical models applied to high-dimensional data in various domains. As an extension to medical imaging, the present work incorporates priors on network hierarchies of brain regions into logistic-regression to distinguish neural activity effects. These priors bridge two separately studied levels of brain architecture: functional segregation into regions and functional integration by networks. Hierarchical region-network priors are shown to better classify and recover 18 psychological tasks than other sparse estimators. Varying the relative importance of region and network structure within the hierarchical tree penalty captured complementary aspects of the neural activity patterns. Local and global priors of neurobiological knowledge are thus demonstrated to offer advantages in generalization performance, sample complexity, and domain interpretability.

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previous chapter Multivariate Manifold Modelling of Functional Connectivity in Developing Language Networks
next chapter A Restaurant Process Mixture Model for Connectivity Based Parcellation of the Cortex
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Metadata
Title
Hierarchical Region-Network Sparsity for High-Dimensional Inference in Brain Imaging
Authors
Danilo Bzdok
Michael Eickenberg
Gaël Varoquaux
Bertrand Thirion
Copyright Year
2017
Book
Information Processing in Medical Imaging

Print ISBN: 978-3-319-59049-3

Electronic ISBN: 978-3-319-59050-9

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-59050-9_26

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