ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2019
Structure learning in coupled dynamical systems and dynamic causal modelling.
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Mechanistic corticostriatal circuit model predicts learning-dependent fMRI dynamics and individual reward bias in humans.bioRxiv : the preprint server for biology · 2026Article
- Neurophysiological Progression in Alzheimer's Disease: Insights From Dynamic Causal Modelling of Longitudinal Magnetoencephalography.Human brain mapping · 2025Article
- Reliability of dynamic causal modelling of resting-state magnetoencephalography.Human brain mapping · 2024Article
- A new causal centrality measure reveals the prominent role of subcortical structures in the causal architecture of the extended default mode network.Brain structure & function · 2023Article
- Impact of DC-Coupled Electrophysiological Recordings for Translational Neuroscience: Case Study of Tracking Neural Dynamics in Rodent Models of Seizures.Frontiers in computational neuroscience · 2022Article
- Toward biophysical markers of depression vulnerability.Frontiers in psychiatry · 2022Article
- Adiabatic dynamic causal modelling.NeuroImage · 2021Article
- Ageing and the Ipsilateral M1 BOLD Response: A Connectivity Study.Brain sciences · 2021Article
- Article
- Coupling functions: dynamical interaction mechanisms in the physical, biological and social sciences.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2019Article
- Multiscale parcellation of dynamic causal models of the brain.Imaging neuroscience (Cambridge, Mass.)Article
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Authors and funding
4 authors.
Funding
Abstract
Identifying a coupled dynamical system out of many plausible candidates, each of which could serve as the underlying generator of some observed measurements, is a profoundly ill-posed problem that commonly arises when modelling real-world phenomena. In this review, we detail a set of statistical procedures for inferring the structure of nonlinear coupled dynamical systems (structure learning), which has proved useful in neuroscience research. A key focus here is the comparison of competing models of network architectures-and implicit coupling functions-in terms of their Bayesian model evidence. These methods are collectively referred to as dynamic causal modelling. We focus on a relatively new approach that is proving remarkably useful, namely Bayesian model reduction, which enables rapid evaluation and comparison of models that differ in their network architecture. We illustrate the usefulness of these techniques through modelling neurovascular coupling (cellular pathways linking neuronal and vascular systems), whose function is an active focus of research in neurobiology and the imaging of coupled neuronal systems. This article is part of the theme issue 'Coupling functions: dynamical interaction mechanisms in the physical, biological and social sciences'.
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