ArticleNeuroImage2023
A joint subspace mapping between structural and functional brain connectomes.
Article in NeuroImage, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Eigenvalue Signatures Reveal Residual Motion Effects Across Resting-State fMRI Denoising Strategies.bioRxiv : the preprint server for biology · 2026Article
- Subregional hypothalamic volumes associate with childhood conduct problems in healthy adults.Brain imaging and behavior · 2026Article
- The role of structural connectivity on brain function through a Markov model of signal transmission.bioRxiv : the preprint server for biology · 2025Article
- Krakencoder: a unified brain connectome translation and fusion tool.Nature methods · 2025Article
- The role of structural connectivity on brain function through a Markov model of signal transmission.PloS one · 2025Article
- Mode-based morphometry: A multiscale approach to mapping human neuroanatomy.Human brain mapping · 2024Article
- Bayesian inference of frequency-specific functional connectivity in MEG imaging using a spectral graph model.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Eigenmode-based approach reveals a decline in brain structure-function liberality across the human lifespan.Communications biology · 2023Article
- Enhanced brain structure-function tethering in transmodal cortex revealed by high-frequency eigenmodes.Nature communications · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Understanding the connection between the brain's structural connectivity and its functional connectivity is of immense interest in computational neuroscience. Although some studies have suggested that whole brain functional connectivity is shaped by the underlying structure, the rule by which anatomy constraints brain dynamics remains an open question. In this work, we introduce a computational framework that identifies a joint subspace of eigenmodes for both functional and structural connectomes. We found that a small number of those eigenmodes are sufficient to reconstruct functional connectivity from the structural connectome, thus serving as low-dimensional basis function set. We then develop an algorithm that can estimate the functional eigen spectrum in this joint space from the structural eigen spectrum. By concurrently estimating the joint eigenmodes and the functional eigen spectrum, we can reconstruct a given subject's functional connectivity from their structural connectome. We perform elaborate experiments and demonstrate that the proposed algorithm for estimating functional connectivity from the structural connectome using joint space eigenmodes gives competitive performance as compared to the existing benchmark methods with better interpretability.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.