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Abstract Gradients provide low-dimensional representations of macroscale brain organization, yet how structural-functional gradient coupling develops and relates to behavioral and molecular features remains unclear. Here, we studied structural-functional gradient coupling across multiple metrics and spatial scales using high-resolution structural and functional connectivity from 5343 children in the Adolescent Brain Cognitive Development study and 875 adults from the Human Connectome Project.
Advanced search Journal of Computational and Graphical Statistics Latest Articles Submit an article Journal homepage Full Article Figures & data References Supplemental Citations Metrics Reprints & Permissions Read this article /doi/full/10.1080/10618600.2025.2612629?needAccess=true Accepted author version Abstract We propose a novel deep neural network methodology for density estimation on product Riemannian manifold domains.
Conflicts of Interest The authors declare no conflicts of interest. Supporting Information Filename Description jtsa70002-sup-0001-supinfo.pdfPDF document, 311.4 KB Data S1. Supporting Information. References , and . 2008. “Extended Bayesian Information Criteria for Model Selection With Large Model Spaces.” Biometrika 95: 759–771. , , and . 2017. “Structural Identification and Variable Selection in High-Dimensional Varying-Coefficient Models.” Journal of Nonparametric Statistics 29: 258–279. , , , et al.
This is an early access version, the complete PDF, HTML, and XML versions will be available soon. Open AccessArticle by Kai Zhu 1,*, Zhiling Gu 1 and Jingang Li 2 1 School of Design and Architecture, Zhejiang University of Technology, Hangzhou 310023, China 2 School of Architecture, Southeast University, Nanjing 210096, China * Author to whom correspondence should be addressed.
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