The Neural Mass Model (NMM) generates biologically reliable mean field potentials of four interconnected regions of interest (ROIs) of the cortex, each simulating a different brain rhythm (in theta, alpha, beta and gamma ranges). These neuroelectrical signals originate from the assumption that ROIs influence each other via of excitatory or by-synaptic inhibitory connections. Besides receiving long-range synapses from other ROIs, each one receives an external input and superimposed Gaussian white noise. We used the NMM to simulate different connectivity networks of four ROIs, by varying both the synaptic strengths and the inputs. The purpose of this study is to investigate how the transmission of brain rhythms behaves under linear and nonlinear conditions. To this aim, we investigated the performance of eight Functional Connectivity (FC) estimators (Correlation, Delayed Correlation, Coherence, Lagged Coherence, Temporal Granger Causality, Spectral Granger Causality, Phase Synchronization and Transfer Entropy) in detecting the connectivity network changes. Results suggest that when a ROI works in the linear region, its capacity to transmit its rhythm increases, while when it saturates, the oscillatory activity becomes strongly affected by other ROIs. Software included here allows the simulation of mean field potentials of four interconnected ROIs, their visualization, both in time and frequency domains, and the estimation of the related FC with eight different methods (for Transfer Entropy the Trentool package is needed).
Region(s) or Organism(s): Neocortex
Ricci G, Magosso E, Ursino M. (2021). The relationship between oscillations in brain regions and functional connectivity: a critical analysis with the aid of neural mass models Brain sciences. 11 [PubMed]