ArticleNeuroImage2020
Comparing dynamic causal models of neurovascular coupling with fMRI and EEG/MEG.
Article in NeuroImage, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 44 citations in OpenAlex.
- Multimodal EEG-MRI Neuroimaging in Schizophrenia-A Systematic and Mechanistic Review.Journal of clinical medicine · 2026Review
- Altered resting-state functional connectivity in women survivors of intimate partner violence: an ICA study.BMC psychology · 2026Article
- A systematic review of the cerebrovascular adaptations following exposure to spaceflight or ground-based analogs: lessons from human and animal studies.NPJ microgravity · 2025Article
- Neurophysiological Progression in Alzheimer's Disease: Insights From Dynamic Causal Modelling of Longitudinal Magnetoencephalography.Human brain mapping · 2025Article
- Neuromodulation of Cerebral Blood Flow: A Physiological Mechanism and Methodological Review of Neurovascular Coupling.Bioengineering (Basel, Switzerland) · 2025Review
- Whole brain functional connectivity: Insights from next generation neural mass modelling incorporating electrical synapses.PLoS computational biology · 2024Article
- Reliability of dynamic causal modelling of resting-state magnetoencephalography.Human brain mapping · 2024Article
- Neuronal connected burst cascades bridge macroscale adaptive signatures across arousal states.Nature communications · 2023Article
- Multi-modal and multi-model interrogation of large-scale functional brain networks.NeuroImage · 2023Article
- Dynamic causal modelling shows a prominent role of local inhibition in alpha power modulation in higher visual cortex.PLoS computational biology · 2022Article
- Auditory cortex modelled as a dynamical network of oscillators: understanding event-related fields and their adaptation.Biological cybernetics · 2022Article
- Toward an integrative neurovascular framework for studying brain networks.Neurophotonics · 2022Article
- [Research progress and application of transfer entropy algorithm].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
- Grey-box modeling and hypothesis testing of functional near-infrared spectroscopy-based cerebrovascular reactivity to anodal high-definition tDCS in healthy humans.PLoS computational biology · 2021Article
- Adiabatic dynamic causal modelling.NeuroImage · 2021Article
- Ageing and the Ipsilateral M1 BOLD Response: A Connectivity Study.Brain sciences · 2021Article
- The effects of age on resting-state BOLD signal variability is explained by cardiovascular and cerebrovascular factors.Psychophysiology · 2021Article
- Markov blankets in the brain.Neuroscience and biobehavioral reviews · 2021Review
- MRIES: A Matlab Toolbox for Mapping the Responses to Intracranial Electrical Stimulation.Frontiers in neuroscience · 2021Article
- Dynamic Causal Modeling for fMRI With Wilson-Cowan-Based Neuronal Equations.Frontiers in neuroscience · 2020Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
This technical note presents a dynamic causal modelling (DCM) procedure for evaluating different models of neurovascular coupling in the human brain - using combined electromagnetic (M/EEG) and functional magnetic resonance imaging (fMRI) data. This procedure compares the evidence for biologically informed models of neurovascular coupling using Bayesian model comparison. First, fMRI data are used to localise regionally specific neuronal responses. The coordinates of these responses are then used as the location priors in a DCM of electrophysiological responses elicited by the same paradigm. The ensuing estimates of model parameters are then used to generate neuronal drive functions, which model pre- or post-synaptic activity for each experimental condition. These functions form the input to a model of neurovascular coupling, whose parameters are estimated from the fMRI data. Crucially, this enables one to evaluate different models of neurovascular coupling, using Bayesian model comparison - asking, for example, whether instantaneous or delayed, pre- or post-synaptic signals mediate haemodynamic responses. We provide an illustrative application of the procedure using a single-subject auditory fMRI and MEG dataset. The code and exemplar data accompanying this technical note are available through the statistical parametric mapping (SPM) software.
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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.