Multi‐scale network regression for brain‐phenotype associations
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 26, 2020
- Source ID
- 10.1002/hbm.24982
Entities
People
- Bertrand Thirion
- Cedric Huchuan Xia
- Daniela Witten
- Danielle Bassett
- Danilo Bzdok
- Russell T Shinohara
- Theodore D Satterthwaite
- Zaixu Cui
- Zongming Ma
Organizations
- Alfred P. Sloan Foundation
- Amazon
- Assisi Foundation of Memphis
- Blavatnik Family Foundation
- German Research Foundation
- John D. and Catherine T. MacArthur Foundation
- McGill University
- National Institutes of Health
- National Science Foundation
- Office of Naval Research
- Paris-Saclay University
- RWTH Aachen University
- United States Army Research Laboratory
- University of Pennsylvania
- University of Washington