ArticleFrontiers in neuroscience2020
Dynamic Causal Modeling for fMRI With Wilson-Cowan-Based Neuronal Equations.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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13 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- The research progress on effective connectivity in adolescent depression based on resting-state fMRI.Frontiers in neurology · 2025Pooled it
- A review of decomposition methods for brain states estimation.Biomedical engineering online · 2026Review
- Shaping dynamical neural computations using spatiotemporal constraints.Biochemical and biophysical research communications · 2024Review
- Article
- 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
- A multi-layer mean-field model of the cerebellum embedding microstructure and population-specific dynamics.PLoS computational biology · 2023Article
- Article
- Functional and effective connectivity analysis of drug-resistant epilepsy: a resting-state fMRI analysis.Frontiers in neuroscience · 2023Article
- Effective connectivity of the human mirror neuron system during social cognition.Social cognitive and affective neuroscience · 2022Article
- It's about time: Linking dynamical systems with human neuroimaging to understand the brain.Network neuroscience (Cambridge, Mass.) · 2022Article
- Generative Models of Brain Dynamics.Frontiers in artificial intelligence · 2022Review
- Alteration in the Functional Organization of the Default Mode Network Following Closed Non-severe Traumatic Brain Injury.Frontiers in neuroscience · 2022Article
- From descriptive connectome to mechanistic connectome: Generative modeling in functional magnetic resonance imaging analysis.Frontiers in human neuroscience · 2022Review
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Authors and funding
5 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dynamic causal modeling (DCM) is an analysis technique that has been successfully used to infer about directed connectivity between brain regions based on imaging data such as functional magnetic resonance imaging (fMRI). Most variants of DCM for fMRI rely on a simple bilinear differential equation for neural activation, making it difficult to interpret the results in terms of local neural dynamics. In this work, we introduce a modification to DCM for fMRI by replacing the bilinear equation with a non-linear Wilson-Cowan based equation and use Bayesian Model Comparison (BMC) to show that this modification improves the model evidences. Improved model evidence of the non-linear model is shown for our empirical data (imitation of facial expressions) and validated by synthetic data as well as an empirical test dataset (attention to visual motion) used in previous foundational papers. For our empirical data, we conduct the analysis for a group of 42 healthy participants who performed an imitation task, activating regions putatively containing the human mirror neuron system (MNS). In this regard, we build 540 models as one family for comparing the standard bilinear with the modified Wilson-Cowan models on the family-level. Using this modification, we can interpret the sigmoid transfer function as an averaged f-I curve of many neurons in a single region with a sigmoidal format. In this way, we can make a direct inference from the macroscopic model to detailed microscopic models. The new DCM variant shows superior model evidence on all tested data sets.
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