RT info:eu-repo/semantics/article
T1 Novel Measure of the Weigh Distribution Balance on the Brain Network: Graph Complexity Applied to Schizophrenia
A1 Gómez Pilar, Javier
A1 Bachiller Matarranz, Alejandro
A1 Núñez Novo, Pablo
A1 Poza Crespo, Jesús
A1 Gómez Peña, Carlos
A1 Lubeiro Juárez, Alba
A1 Molina Rodríguez, Vicente
A1 Hornero Sánchez, Roberto
K1 Schizophrenia
AB 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.
PB IEEE Conference Publications
SN 1557-170X
YR 2016
FD 2016
LK http://uvadoc.uva.es/handle/10324/21725
UL http://uvadoc.uva.es/handle/10324/21725
LA eng
NO Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2016, p. 700 - 703
NO Producción Científica
DS UVaDOC
RD 19-abr-2024