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

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Accession:261881
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.
Reference:
1 . Giacopelli G, Migliore M, Tegolo D (2020) Graph-theoretical derivation of brain structural connectivity Applied Mathematics and Computation 377:125150
Model Information (Click on a link to find other models with that property)
Model Type: Connectionist Network; Realistic Network;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB;
Model Concept(s): Connectivity matrix; Methods;
Implementer(s): Giacopelli, Giuseppe [giuseppe.giacopelli at unipa.it]; Tegolo, Domenico [domenico.tegolo at unipa.it]; 
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