Multi-Scale Network Regression for Brain-Phenotype Associations - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Human Brain Mapping Année : 2020

Multi-Scale Network Regression for Brain-Phenotype Associations

Résumé

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
Origine : Publication financée par une institution
Loading...

Dates et versions

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

Identifiants

Citer

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⟩
89 Consultations
130 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More