Multi-Scale Network Regression for Brain-Phenotype Associations - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Human Brain Mapping Year : 2020

Multi-Scale Network Regression for Brain-Phenotype Associations


Brain networks are increasingly characterized at different scales, including summary statistics, community connectivity, and individual edges. While research relating brain networks to behavioral measurements has yielded many insights into brain‐phenotype relationships, common analytical approaches only consider network information at a single scale. Here, we designed, implemented, and deployed Multi‐Scale Network Regression (MSNR), a penalized multivariate approach for modeling brain networks that explicitly respects both edge‐ and community‐level information by assuming a low rank and sparse structure, both encouraging less complex and more interpretable modeling. Capitalizing on a large neuroimaging cohort (n = 1, 051), we demonstrate that MSNR recapitulates interpretable and statistically significant connectivity patterns associated with brain development, sex differences, and motion‐related artifacts. Compared to single‐scale methods, MSNR achieves a balance between prediction performance and model complexity, with improved interpretability. Together, by jointly exploiting both edge‐ and community‐level information, MSNR has the potential to yield novel insights into brain‐behavior relationships.
Fichier principal
Vignette du fichier
Cedric_Witten_2020_HBM_multiscale.pdf (2.03 Mo) Télécharger le fichier
Origin : Publication funded by an institution

Dates and versions

hal-02523974 , version 1 (31-03-2020)



Cedric Huchuan Xia, Zongming Ma, Zaixu Cui, Danilo Bzdok, Bertrand Thirion, et al.. Multi-Scale Network Regression for Brain-Phenotype Associations. Human Brain Mapping, In press, ⟨10.1002/hbm.24982⟩. ⟨hal-02523974⟩
88 View
118 Download



Gmail Facebook X LinkedIn More