ArticleFrontiers in neuroscience2022
Incorporation of spatial- and connectivity-based cortical brain region information in regularized regression: Application to Human Connectome Project data.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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Who cites it
2 citing papers in PubMed.
- PROLONG: penalized regression for outcome guided longitudinal omics analysis with network and group constraints.Bioinformatics (Oxford, England) · 2025Article
- Mapping the structure-function relationship along macroscale gradients in the human brain.Nature communications · 2024Article
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Authors and funding
7 authors.
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Abstract
Studying the association of the brain's structure and function with neurocognitive outcomes requires a comprehensive analysis that combines different sources of information from a number of brain-imaging modalities. Recently developed regularization methods provide a novel approach using information about brain structure to improve the estimation of coefficients in the linear regression models. Our proposed method, which is a special case of the Tikhonov regularization, incorporates structural connectivity derived with Diffusion Weighted Imaging and cortical distance information in the penalty term. Corresponding to previously developed methods that inform the estimation of the regression coefficients, we incorporate additional information
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