title: Physiologically informed dynamic causal modeling of fMRI data.
abstract: The functional MRI (fMRI) signal is an indirect measure of neuronal activity. In order to deconvolve the neuronal activity from the experimental fMRI data, biophysical generative models have been proposed describing the link between neuronal activity and the cerebral blood flow (the neurovascular coupling), and further the hemodynamic response and the BOLD signal equation. These generative models have been employed both for single brain area deconvolution and to infer effective connectivity in networks of multiple brain areas. In the current paper, we introduce a new fMRI model inspired by experimental observations about the physiological underpinnings of the BOLD signal and compare it with the generative models currently used in dynamic causal modeling (DCM), a widely used framework to study effective connectivity in the brain. We consider three fundamental aspects of such generative models for fMRI: (i) an adaptive two-state neuronal model that accounts for a wide repertoire of neuronal responses during and after stimulation; (ii) feedforward neurovascular coupling that links neuronal activity to blood flow; and (iii) a balloon model that can account for vascular uncoupling between the blood flow and the blood volume. Finally, we adjust the parameterization of the BOLD signal equation for different magnetic field strengths. This paper focuses on the form, motivation and phenomenology of DCMs for fMRI and the characteristics of the various models are demonstrated using simulations. These simulations emphasize a more accurate modeling of the transient BOLD responses - such as adaptive decreases to sustained inputs during stimulation and the post-stimulus undershoot. In addition, we demonstrate using experimental data that it is necessary to take into account both neuronal and vascular transients to accurately model the signal dynamics of fMRI data. By refining the models of the transient responses, we provide a more informed perspective on the underlying neuronal process and offer new ways of inferring changes in local neuronal activity and effective connectivity from fMRI.
authors.affiliation: Dept. of Cognitive Neuroscience, Maastricht University, 6200MD Maastricht, The Netherlands. Electronic address: m.havlicek@maastrichtuniversity.nl.
authors.forename: Martin
authors.initials: M
authors.lastname: Havlicek
authors.affiliation: Dept. of Cognitive Neuroscience, Maastricht University, 6200MD Maastricht, The Netherlands.
authors.forename: Alard
authors.initials: A
authors.lastname: Roebroeck
authors.affiliation: Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London WC1N 3BG, United Kingdom.
authors.forename: Karl
authors.initials: K
authors.lastname: Friston
authors.affiliation: Dept. of Cognitive Neuroscience, Maastricht University, 6200MD Maastricht, The Netherlands.
authors.forename: Anna
authors.initials: A
authors.lastname: Gardumi
authors.affiliation: Dept. of Cognitive Neuroscience, Maastricht University, 6200MD Maastricht, The Netherlands.
authors.forename: Dimo
authors.initials: D
authors.lastname: Ivanov
authors.affiliation: Dept. of Cognitive Neuroscience, Maastricht University, 6200MD Maastricht, The Netherlands. Electronic address: kamil.uludag@maastrichtuniversity.nl.
authors.forename: Kamil
authors.initials: K
authors.lastname: Uludag
mesh_terms: D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D006439:Hemodynamics; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D000069336:Neurovascular Coupling; D012815:Signal Processing, Computer-Assisted
keywords: BOLD signal; Dynamic causal modeling (DCM); Excitation–inhibition; Feedforward neurovascular coupling; Hemodynamic model; Post-stimulus undershoot