A neural mass model for critical assessment of brain connectivity (Ursino et al 2020)

We use a neural mass model of interconnected regions of interest to simulate reliable neuroelectrical signals in the cortex. In particular, signals simulating mean field potentials were generated assuming two, three or four ROIs, connected via excitatory or by-synaptic inhibitory links. Then we investigated whether bivariate Transfer Entropy (TE) can be used to detect a statistically significant connection from data (as in binary 0/1 networks), and even if connection strength can be quantified (i.e., the occurrence of a linear relationship between TE and connection strength). Results suggest that TE can reliably estimate the strength of connectivity if neural populations work in their linear regions. However, nonlinear phenomena dramatically affect the assessment of connectivity, since they may significantly reduce TE estimation. Software included here allows the simulation of neural mass models with a variable number of ROIs and connections, the estimation of TE using the free package Trentool, and the realization of figures to compare true connectivity with estimated values.

Model Type: Neural mass; Connectionist Network; Synapse

Region(s) or Organism(s): Neocortex

Cell Type(s): Neocortex L5/6 pyramidal GLU cell; Neocortex layer 5 interneuron

Transmitters: Glutamate; Gaba

Model Concept(s): Brain Rhythms; Connectivity matrix; Delay

Simulation Environment: MATLAB (web link to model); MATLAB; Trentool

Implementer(s): Ursino, Mauro [mauro.ursino at unibo.it]; Ricci, Giulia [Giulia.Ricci at unibo.it]; Magosso, Elisa [elisa.magosso at unibo.it]


Ursino M, Ricci G, Magosso E. (2020). Transfer Entropy as a Measure of Brain Connectivity: A Critical Analysis With the Help of Neural Mass Models Frontiers in computational neuroscience. 14 [PubMed]

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