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.

A dynamical model of the laminar BOLD response

“…The fMRI using the blood oxygenation level-dependent (BOLD) 1 signal provides an indirect, vascular reflection of neuronal activity and, thus, comprises both neuronal and vascular sources of variability (Havlicek et al (2015), and references therein). Specifically, neuronal activation causes a series of physiological events altering blood oxygenation, including changes in cerebral blood flow (CBF), cerebral blood volume (CBV) and cerebral metabolic rate of oxygen (CMRO2) (Kim and Ogawa, 2012).…”

Combining optogenetic stimulation and fMRI to validate a multivariate dynamical systems model for estimating causal brain interactions

“…Computer simulations of artificial neural networks with a small number of nodes have been thus far the most widely-used strategy for validating causal estimation methods (Havlicek et al, 2015; Schippers et al, 2011; Seth et al, 2013; Smith et al, 2011). However, these simulations do not adequately model neurophysiological and vascular features underlying in vivo fMRI data.…”

Temporal pattern of acoustic imaging noise asymmetrically modulates activation in the auditory cortex

“…Though these methods have been extremely effective in modeling the nonlinearities, they are purely theoretical and do not have a clear physiologic basis. Physiologically-based modeling is best exemplified by the Balloon model proposed by Buxton et al (1998) and further developed by Uludag and colleagues (Uludag et al, 2004; Sadaghiani et al, 2009, Havlicek et al, 2015). However, while also effective, this method requires estimation of various difficult-to-obtain parameters that characterize the hemodynamics in the cortex (venous volume, deoxyhemoglobin content, oxygen extraction fraction, etc.).…”

“…Therefore, it seemed that a simpler model (i.e., having fewer parameters to estimate) could account for the time-limited observed nonlinearity, especially during the post-stimulus undershoot phase of the response. Though the combined model was also not physiologically inspired, a physiologically relevant derivative of the balloon model, presented by Havlicek et al (2015), provides some basis for the physiological validity of such a combined approach. Havlicek et al (2015) considered the conditions of stimulus and lack-of-stimulus as two modulating inputs in the model.…”

“…Though the combined model was also not physiologically inspired, a physiologically relevant derivative of the balloon model, presented by Havlicek et al (2015), provides some basis for the physiological validity of such a combined approach. Havlicek et al (2015) considered the conditions of stimulus and lack-of-stimulus as two modulating inputs in the model. Therefore, the post-stimulus undershoot could be modulated by the response to cessation of the stimulus.…”