ArticleMachine learning in medical imaging. MLMI (Workshop)2020
Structural Connectivity Enriched Functional Brain Network using Simplex Regression with GraphNet.
Article in Machine learning in medical imaging. MLMI (Workshop), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Preference matrix guided sparse canonical correlation analysis for mining brain imaging genetic associations in Alzheimer's disease.Methods (San Diego, Calif.) · 2023Article
- Integrative analysis of multi-omics and imaging data with incorporation of biological information via structural Bayesian factor analysis.Briefings in bioinformatics · 2023Article
- [Abnormal functional connectivity and structure-function coupling of the nucleus accumbens in patients with major depressive disorder].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciencesArticle
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Authors and funding
6 authors.
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Abstract
The connectivity analysis is a powerful technique for investigating a hard-wired brain architecture as well as flexible, functional dynamics tied to human cognition. Recent multi-modal connectivity studies had the challenge of combining functional and structural connectivity information into one integrated network. In this paper, we proposed a simplex regression model with graph-constrained Elastic Net (GraphNet) to estimate functional networks enriched by structural connectivity in a biologically meaningful way with a low model complexity. Our model constructed the functional networks using sparse simplex regression framework and enriched structural connectivity information based on GraphNet constraint. We applied our model on the real neuroimaging datasets to show its ability for predicting a clinical score. Our results demonstrated that integrating multi-modal features could detect more sensitive and subtle brain biomarkers than using a single modality.
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