Graph-theoretical Derivation of Brain Structural Connectivity (Giacopelli et al 2020)

Brain connectivity at the single neuron level can provide fundamental insights into how information is integrated and propagated within and between brain regions. However, it is almost impossible to adequately study this problem experimentally and, despite intense efforts in the field, no mathematical description has been obtained so far. Here, we present a mathematical framework based on a graph-theoretical approach that, starting from experimental data obtained from a few small subsets of neurons, can quantitatively explain and predict the corresponding full network properties. This model also changes the paradigm with which large-scale model networks can be built, from using probabilistic/empiric connections or limited data, to a process that can algorithmically generate neuronal networks connected as in the real system.

Model Type: Connectionist Network; Realistic Network

Model Concept(s): Connectivity matrix; Methods

Simulation Environment: MATLAB

Implementer(s): Giacopelli, Giuseppe [giuseppe.giacopelli at]; Tegolo, Domenico [domenico.tegolo at]


Giacopelli G, Migliore M, Tegolo D. (2020). Graph-theoretical derivation of brain structural connectivity Applied Mathematics and Computation. 377

This website requires cookies and limited processing of your personal data in order to function. By continuing to browse or otherwise use this site, you are agreeing to this use. See our Privacy policy and how to cite and terms of use.