6533b827fe1ef96bd1286fe9
RESEARCH PRODUCT
Graph-theoretical derivation of brain structural connectivity
GiacopelliG.MiglioreM.TegoloD.subject
0209 industrial biotechnologyTheoretical computer scienceComputer scienceNeuronal network02 engineering and technologyMECHANISMSCENTRALITY020901 industrial engineering & automationSettore MAT/05 - Analisi MatematicaNeuronal networksConnectome0202 electrical engineering electronic engineering information engineeringINDEXComputer Science::DatabasesRandom graphsSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniSettore INF/01 - InformaticaQuantitative Biology::Neurons and CognitionApplied MathematicsProbabilistic logicExperimental data020206 networking & telecommunicationsComputational MathematicsSYNCHRONIZATIONSIMULATIONGraph (abstract data type)description
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. (C) 2020 The Author(s). Published by Elsevier Inc.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-07-01 | Applied Mathematics and Computation |