Novel Measure of the Weigh Distribution Balance on the Brain Network: Graph Complexity Applied to Schizophrenia

Abstract

The aim of this study was to assess brain complexity dynamics in schizophrenia (SCH) patients during an auditory oddball task. For that task, we applied a novel graph measure based on the balance of the node weighs distribution. Previous studies applied complexity parameters that were strongly dependent on network topology. This fact could bias the results besides being necessary correction techniques as surrogating process. In the present study, we applied a novel graph complexity measure from the information theory: Shannon Graph Complexity (SGC). Complexity patterns form electroencephalographic recordings of 20 healthy controls and 20 SCH patients during an auditory oddball task were analyzed. Results showed a significantly more pronounced decrease of SGC for controls than for SCH patients during the cognitive task. These findings suggest an important change in the brain configuration towards more balanced networks, mainly in the connections related to long-range interactions. Since these changes are significantly more pronounced in controls, it implies a deficit in the neural network reorganization in SCH patients. In addition, SGC showed a suitable discrimination ability using a leave-one-out cross-validation: 0.725 accuracy and 0.752 area under receiver operating characteristics curve. The novel complexity measure proposed in this study demonstrated to be independent of network topology and, therefore, it complements traditional graph measures to characterize brain networks.