ArticleNetwork neuroscience (Cambridge, Mass.)2020
Model-based whole-brain effective connectivity to study distributed cognition in health and disease.
Article in Network neuroscience (Cambridge, Mass.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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32 citing papers in PubMed.
- Thermodynamics of consciousness: Non-equilibrium brain dynamics track conscious states.Cell reports · 2026Article
- Sub-second fluctuations between top-down and bottom-up modes distinguish diverse human brain states.Current biology : CB · 2026Article
- Multimodal Branched Transport Infers Anatomically Aligned Brain Reaction Maps.Neuroinformatics · 2026Article
- Competitive interactions shape mammalian brain network dynamics and computation.Nature neuroscience · 2026Article
- Optimal stochastic tracking control for brain network dynamics.Communications biology · 2025Article
- Longitudinal investigation of structural and resting-state effective connectivity alterations in a non-human primate model of Huntington's disease.Neurobiology of disease · 2025Article
- Structurally informed resting-state effective connectivity recapitulates cortical hierarchy.bioRxiv : the preprint server for biology · 2025Article
- Comparison of derivative-based and correlation-based methods to estimate effective connectivity in neural networks.Scientific reports · 2025Article
- Structurally informed models of directed brain connectivity.Nature reviews. Neuroscience · 2025Review
- Interdependence patterns of multifrequency oscillations predict visuomotor behavior.Network neuroscience (Cambridge, Mass.) · 2025Article
- Analyzing asymmetry in brain hierarchies with a linear state-space model of resting-state fMRI data.Network neuroscience (Cambridge, Mass.) · 2024Article
- Reduced Prefrontal-Thalamic Theta Flow During Working Memory Retrieval in APP/PS1 Mice.Journal of Alzheimer's disease : JAD · 2024Article
- Bridging functional and anatomical neural connectivity through cluster synchronization.Scientific reports · 2023Article
- Whole-brain analyses indicate the impairment of posterior integration and thalamo-frontotemporal broadcasting in disorders of consciousness.Human brain mapping · 2023Article
- Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness.NeuroImage · 2023Article
- From neurotransmitters to networks: Transcending organisational hierarchies with molecular-informed functional imaging.Neuroscience and biobehavioral reviews · 2023Review
- Characterization of regional differences in resting-state fMRI with a data-driven network model of brain dynamics.Science advances · 2023Article
- A topological classifier to characterize brain states: When shape matters more than variance.PloS one · 2023Article
- Dynamic sensitivity analysis: Defining personalised strategies to drive brain state transitions via whole brain modelling.Computational and structural biotechnology journal · 2023Review
- Driving brain state transitions in major depressive disorder through external stimulation.Human brain mapping · 2022Article
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10 authors.
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Abstract
Neuroimaging techniques are now widely used to study human cognition. The functional associations between brain areas have become a standard proxy to describe how cognitive processes are distributed across the brain network. Among the many analysis tools available, dynamic models of brain activity have been developed to overcome the limitations of original connectivity measures such as functional connectivity. This goes in line with the many efforts devoted to the assessment of directional interactions between brain areas from the observed neuroimaging activity. This opinion article provides an overview of our model-based whole-brain effective connectivity to analyze fMRI data, while discussing the pros and cons of our approach with respect to other established approaches. Our framework relies on the multivariate Ornstein-Uhlenbeck (MOU) process and is thus referred to as MOU-EC. Once tuned, the model provides a directed connectivity estimate that reflects the dynamical state of BOLD activity, which can be used to explore cognition. We illustrate this approach using two applications on task-evoked fMRI data. First, as a connectivity measure, MOU-EC can be used to extract biomarkers for task-specific brain coordination, understood as the patterns of areas exchanging information. The multivariate nature of connectivity measures raises several challenges for whole-brain analysis, for which machine-learning tools present some advantages over statistical testing. Second, we show how to interpret changes in MOU-EC connections in a collective and model-based manner, bridging with network analysis. Our framework provides a comprehensive set of tools that open exciting perspectives to study distributed cognition, as well as neuropathologies.
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