Dynamic Causal Modelling and physiological confounds: a functional MRI study of vagus nerve stimulation.

Dynamic Causal Modelling (DCM) has been proposed to estimate neuronal connectivity from functional magnetic resonance imaging (fMRI) using a biophysical model that links synaptic activity to hemodynamic processes. However, it is well known that fMRI is sensitive not only to neuronal activity, but also to many other psychophysiological responses which may be task-related, such as changes in cardio-respiratory activity. They are not explicitly taken into account in the generative models of DCM and their effects on estimated neuronal connectivity are not known. The main goal of this study was to report the face validity of DCM in the presence of strong physiological confounds that presumably cannot be corrected for, using an fMRI experiment of vagus nerve stimulation (VNS) performed in rats. First, a simple simulation was used to evaluate the principled ability of DCM to recover directed connectivity in the presence of a confounding factor. Second, we tested the experimental validity using measures of the BOLD correlates of left 5Hz VNS. Because VNS mostly activates the central autonomic regulation system, fMRI signals were likely to represent both direct and indirect vascular responses to such activation. In addition to the inference of standard statistical parametric maps, DCM was thus used to estimate directed neural connectivity in a small brain network including the nucleus tractus solitarius (NTS) known to receive vagal afferents. Though blood pressure changes may constitute a major physiological confound in this dataset, model comparison of DCMs still allowed the identification of the NTS as the input station of the VNS pathway to the brain. Our study indicates that current developments of DCM are robust to psychophysiological responses to some extent, but does not exclude the need to develop specific models of brain - body interactions within the DCM framework to better estimate neuronal connectivity from fMRI time series.

Identifying neural drivers with functional MRI: an electrophysiological validation.

Whether functional magnetic resonance imaging (fMRI) allows the identification of neural drivers remains an open question of particular importance to refine physiological and neuropsychological models of the brain, and/or to understand neurophysiopathology. Here, in a rat model of absence epilepsy showing spontaneous spike-and-wave discharges originating from the first somatosensory cortex (S1BF), we performed simultaneous electroencephalographic (EEG) and fMRI measurements, and subsequent intracerebral EEG (iEEG) recordings in regions strongly activated in fMRI (S1BF, thalamus, and striatum). fMRI connectivity was determined from fMRI time series directly and from hidden state variables using a measure of Granger causality and Dynamic Causal Modelling that relates synaptic activity to fMRI. fMRI connectivity was compared to directed functional coupling estimated from iEEG using asymmetry in generalised synchronisation metrics. The neural driver of spike-and-wave discharges was estimated in S1BF from iEEG, and from fMRI only when hemodynamic effects were explicitly removed. Functional connectivity analysis applied directly on fMRI signals failed because hemodynamics varied between regions, rendering temporal precedence irrelevant. This paper provides the first experimental substantiation of the theoretical possibility to improve interregional coupling estimation from hidden neural states of fMRI. As such, it has important implications for future studies on brain connectivity using functional neuroimaging.