Physiologically informed dynamic causal modeling of fMRI data

• Published in: NeuroImage (Orlando, Fla.) Fla.), 2015-11, Vol.122, p.355-372
• Main Authors: Havlicek, Martin, Roebroeck, Alard, Friston, Karl, Gardumi, Anna, Ivanov, Dimo, Uludag, Kamil
• Format: Article
• Language: English
• Subjects: Bayes Theorem; BOLD signal; Brain - physiology; Brain Mapping - methods; Computer Simulation; Dynamic causal modeling (DCM); Excitation–inhibition; Feedforward neurovascular coupling; Hemodynamic model; Hemodynamics; Humans; Image Processing, Computer-Assisted; Magnetic Resonance Imaging - methods; Models, Neurological; Neurons - physiology; Neurovascular Coupling; NMR; Nuclear magnetic resonance; Physiology; Post-stimulus undershoot; Signal Processing, Computer-Assisted
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2015.07.078

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. [Display omitted] •New physiological model for DCM of fMRI data supported by experimental observations•Excitatory–inhibitory neuronal model for a wide repertoire of neuronal responses•Feedforward neurovascular coupling that links neuronal activity to blood flow•Neuronal and vascular mechanisms to explain post-stimulus BOLD undershoot•Adjusted BOLD signal equation for different magnetic