Modelling the brain with a multilevel Random Geometric Graph

Abstract

The brain is a complex network. Recently multiple studies have tried to modelcomplex networks using graph theory. In this thesis we have developed a multi-level Random Geometric Graph to model the human brain. We have includedthe extra property that the probability of a connection declines proportionallyto the distance. Based on our analysis we conclude that this graph has a similarlevel of segregation, but a higher level of integration compared to the real brain. This graph also predicts the properties of deeper levels in the brain, where dataof these levels is not yet available.