English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Multi-modal populations of networks arise in many scenarios including inlarge-scale multi-modal neuroimaging studies that capture both functional andstructural neuroimaging data for thousands of subjects. A major researchquestion in such studies is how functional and structural brain connectivityare related and how they vary across the population. we develop a novelPCA-type framework for integrating multi-modal undirected networks measured onmany subjects. Specifically, we arrange these networks as semi-symmetrictensors, where each tensor slice is a symmetric matrix representing a networkfrom an individual subject. We then propose a novel Joint, IntegrativeSemi-Symmetric Tensor PCA (JisstPCA) model, associated with an efficientiterative algorithm, for jointly finding low-rank representations of two ormore networks across the same population of subjects. We establish one-stepstatistical convergence of our separate low-rank network factors as well as theshared population factors to the true factors, with finite sample statisticalerror bounds. Through simulation studies and a real data example forintegrating multi-subject functional and structural brain connectivity, weillustrate the advantages of our method for finding joint low-rank structuresin multi-modal populations of networks.

Joint Semi-Symmetric Tensor PCA for Integrating Multi-modal Populations of Networks