ArticleFrontiers in neuroscience2022
Predicting Functional Connectivity From Observed and Latent Structural Connectivity
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 9 papers.
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Who cites it
9 citing papers in PubMed.
- Multi-Scale Structural MRI Features Reveal Task-Based Functional Connectivity and Its Alterations in Psychiatric Disorders: A Collaborative Graph Attention Network Approach.Brain topography · 2026Article
- Network Diffusion Modeling in Computational Neuroscience.Advances in experimental medicine and biology · 2026Article
- Mode-based morphometry: A multiscale approach to mapping human neuroanatomy.Human brain mapping · 2024Article
- Predicting an individual's functional connectivity from their structural connectome: Evaluation of evidence, recommendations, and future prospects.Network neuroscience (Cambridge, Mass.) · 2024Article
- Spectral graph model for fMRI: A biophysical, connectivity-based generative model for the analysis of frequency-resolved resting-state fMRI.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Enhanced brain structure-function tethering in transmodal cortex revealed by high-frequency eigenmodes.Nature communications · 2023Article
- Article
- Mode-based morphometry: A multiscale approach to mapping human neuroanatomy.bioRxiv : the preprint server for biology · 2023Article
- Structure-function models of temporal, spatial, and spectral characteristics of non-invasive whole brain functional imaging.Frontiers in neuroscience · 2022Review
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Authors and funding
4 authors.
Funding
Abstract
Understanding how complex dynamic activity propagates over a static structural network is an overarching question in the field of neuroscience. Previous work has demonstrated that linear graph-theoretic models perform as well as non-linear neural simulations in predicting functional connectivity with the added benefits of low dimensionality and a closed-form solution which make them far less computationally expensive. Here we show a simple model relating the eigenvalues of the structural connectivity and functional networks using the Gamma function, producing a reliable prediction of functional connectivity with a single model parameter. We also investigate the impact of local activity diffusion and long-range interhemispheric connectivity on the structure-function model and show an improvement in functional connectivity prediction when accounting for such latent variables which are often excluded from traditional diffusion tensor imaging (DTI) methods.
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