Effective connectivity of the human mirror neuron system during social cognition.

The human mirror neuron system (MNS) can be considered the neural basis of social cognition. Identifying the global network structure of this system can provide significant progress in the field. In this study, we use dynamic causal modeling (DCM) to determine the effective connectivity between central regions of the MNS for the first time during different social cognition tasks. Sixty-seven healthy participants completed fMRI scanning while performing social cognition tasks, including imitation, empathy and theory of mind. Superior temporal sulcus (STS), inferior parietal lobule (IPL) and Brodmann area 44 (BA44) formed the regions of interest for DCM. Varying connectivity patterns, 540 models were built and fitted for each participant. By applying group-level analysis, Bayesian model selection and Bayesian model averaging, the optimal family and model for all experimental tasks were found. For all social-cognitive processes, effective connectivity from STS to IPL and from STS to BA44 was found. For imitation, additional mutual connections occurred between STS and BA44, as well as BA44 and IPL. The results suggest inverse models in which the motor regions BA44 and IPL receive sensory information from the STS. In contrast, for imitation, a sensory loop with an exchange of motor-to-sensory and sensory-to-motor information seems to exist.

Dynamic Causal Modeling for fMRI With Wilson-Cowan-Based Neuronal Equations.

Dynamic causal modeling (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 for fMRI 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 non-linear Wilson-Cowan based equation and use Bayesian Model Comparison (BMC) to show that this modification improves the model evidences. Improved model evidence of the non-linear model is shown for our empirical data (imitation of facial expressions) and validated by synthetic data as well as an empirical test dataset (attention to visual motion) used in previous foundational papers. 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 with a sigmoidal format. In this way, we can make a direct inference from the macroscopic model to detailed microscopic models. The new DCM variant shows superior model evidence on all tested data sets.