Dynamic Causal Modeling for fMRI With Wilson-Cowan-Based Neuronal Equations - Sadeghi, Sadjad; Mier, Daniela; Gerchen, Martin F.; Schmidt, Stephanie N. L.; Hass, Joachim (Frontiers in Neuroscience, 2020)
Dynamic causal modelling (DCM) is an analysis technique that has been successfully used to infer about directed connectivity between brain regions based on imaging data such as functional magnetic resonance imaging (fMRI). Most variants of DCM rely on a simple bilinear differential equation for neural activation, making it difficult to interpret the results in terms of local neural dynamics. In this work, we introduce a modification to DCM for fMRI by replacing the bilinear equation with a nonlinear Wilson-Cowan based equation and use Bayesian Model Comparison (BMC) to show that this modification improves fits to data. Improved fitting performance of the nonlinear model is shown for our empirical data (imitation of facial expressions) and validated by synthetic data as well as an established empirical data set (attention to visual motion). For our empirical data, we conduct the analysis for a group of 42 healthy participants who performed an imitation task, activating regions putatively containing the human mirror neuron system (MNS). In this regard, we build 540 models as one family for comparing the standard bilinear with the modified Wilson-Cowan models on the family-level. Using this modification, we can interpret the sigmoid transfer function as an averaged f-I curve of many neurons in a single region which has a sigmoidal format, and in this way, we can make a direct inference from the macroscopic model to detailed microscopic models. The new DCM variant shows superior fitting performance on all tested data sets.