ArticleThe annals of applied statistics2024
AN INTEGRATIVE NETWORK-BASED MEDIATION MODEL (NMM) TO ESTIMATE MULTIPLE GENETIC EFFECTS ON OUTCOMES MEDIATED BY FUNCTIONAL CONNECTIVITY.
Article in The annals of applied statistics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Intervention on Modifiable Lifestyle and Physiological Factors via Variational Autoencoder Reveals Changes in Functional Connectivity-Mediated Risk for Alzheimer's Disease.medRxiv : the preprint server for health sciences · 2025Article
- S-GMAS: Genome-Wide Mediation Analysis With Brain Subcortical Shape Mediators.Human brain mapping · 2025Article
- AN INTEGRATIVE NETWORK-BASED MEDIATION MODEL (NMM) TO ESTIMATE MULTIPLE GENETIC EFFECTS ON OUTCOMES MEDIATED BY FUNCTIONAL CONNECTIVITY.The annals of applied statistics · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Functional connectivity of the brain, characterized by interconnected neural circuits across functional networks, is a cutting-edge feature in neuroimaging. It has the potential to mediate the effect of genetic variants on behavioral outcomes or diseases. Existing mediation analysis methods can evaluate the impact of genetics and brain structurefunction on cognitive behavior or disorders, but they tend to be limited to single genetic variants or univariate mediators, without considering cumulative genetic effects and the complex matrix and group and network structures of functional connectivity. To address this gap, the paper presents an integrative network-based mediation model (NMM) that estimates the effect of multiple genetic variants on behavioral outcomes or diseases mediated by functional connectivity. The model incorporates group information of inter-regions at broad network level and imposes low-rank and sparse assumptions to reflect the complex structures of functional connectivity and selecting network mediators simultaneously. We adopt block coordinate descent algorithm to implement a fast and efficient solution to our model. Simulation results indicate the efficacy of the model in selecting active mediators and reducing bias in effect estimation. With application to the Human Connectome Project Youth Adult (HCP-YA) study of 493 young adults, two genetic variants (rs769448 and rs769449) on the
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Registered trials
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