An electrophysiological validation of stochastic DCM for fMRI.
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.
Motivation, Brain and Behaviour Group, Brain and Spine Institute Paris, France ; Wellcome Trust Centre for Neuroimaging, University College London London, UK.
J
J
Daunizeau
L
L
Lemieux
A E
AE
Vaudano
K J
KJ
Friston
K E
KE
Stephan
EEG; dynamic causal modeling; effective connectivity; fMRI; neural field; neural noise; separation of time scales