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Graph-theoretical Derivation of Brain Structural Connectivity

Authors: Giuseppe Giacopelli 1,2, Michele Migliore 1, Domenico Tegolo 1,2

Author information: 1 Institute of Biophysics, National Research Council, Palermo, Italy, 2 Department of Mathematics and Informatics,University of Palermo, Palermo, Italy.

Corresponding author: Domenico Tegolo ( )

Journal: Journal of Applied Mathemathics and Computation

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Citation: Giacopelli, G., Migliore, M., Tegolo, D. (2020). Graph-theoretical Derivation of Brain Structural Connectivity. Appl. Mat. Comput. 2020 377, 125150


Licence: the Creative Commons Attribution (CC BY) license  applies for all files. Under this Open Access license anyone may copy, distribute, or reuse the files as long as the authors and the original source are properly cited.

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.

Models and Web App: all the models used in the paper and the Web Application are available at the links reported below: