ArticleFrontiers in computational neuroscience2012
An electrophysiological validation of stochastic DCM for fMRI.
Article in Frontiers in computational neuroscience, 2012. 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.
- Impact of PSA- versus STN-DBS on effective connectivity in Parkinson's disease - a 3.0T resting-state fMRI study.NPJ Parkinson's disease · 2026Article
- Article
- Convergent evidence for hierarchical prediction networks from human electrocorticography and magnetoencephalography.Cortex; a journal devoted to the study of the nervous system and behavior · 2016Article
- Inhibitory behavioral control: a stochastic dynamic causal modeling study using network discovery analysis.Brain connectivity · 2015Article
- Inhibitory behavioral control: A stochastic dynamic causal modeling study comparing cocaine dependent subjects and controls.NeuroImage. Clinical · 2015Article
- Editorial: State-dependent brain computation.Frontiers in computational neuroscience · 2015Article
- Temporal prediction errors modulate task-switching performance.Frontiers in psychology · 2015Article
- Resting state functional MRI in Parkinson's disease: the impact of deep brain stimulation on 'effective' connectivity.Brain : a journal of neurology · 2014Article
- Dynamical predictions of insular hubs for social cognition and their application to stroke.Frontiers in behavioral neuroscience · 2014Article
- Network Connectivity in Epilepsy: Resting State fMRI and EEG-fMRI Contributions.Frontiers in neurology · 2014Review
- Causality within the Epileptic Network: An EEG-fMRI Study Validated by Intracranial EEG.Frontiers in neurology · 2013Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
In this note, we assess the predictive validity of stochastic dynamic causal modeling (sDCM) of functional magnetic resonance imaging (fMRI) data, in terms of its ability to explain changes in the frequency spectrum of concurrently acquired electroencephalography (EEG) signal. We first revisit the heuristic model proposed in Kilner et al. (2005), which suggests that fMRI activation is associated with a frequency modulation of the EEG signal (rather than an amplitude modulation within frequency bands). We propose a quantitative derivation of the underlying idea, based upon a neural field formulation of cortical activity. In brief, dense lateral connections induce a separation of time scales, whereby fast (and high spatial frequency) modes are enslaved by slow (low spatial frequency) modes. This slaving effect is such that the frequency spectrum of fast modes (which dominate EEG signals) is controlled by the amplitude of slow modes (which dominate fMRI signals). We then use conjoint empirical EEG-fMRI data-acquired in epilepsy patients-to demonstrate the electrophysiological underpinning of neural fluctuations inferred from sDCM for fMRI.
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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.