Novel measure of the weigh distribution balance on the brain network: Graph complexity applied to schizophrenia

• Published in: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2016; pp. 700 - 703
• Main Authors: Gomez-Pilar, J., Poza, J., Bachiller, A., Nunez, P., Gomez, C., Lubeiro, A., Molina, V., Hornero, R.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.08.2016
• Subjects: Adult; Brain - physiology; Brain - physiopathology; Case-Control Studies; Complexity theory; Electrodes; Electroencephalography; Electroencephalography - methods; Entropy; Female; Humans; Male; Nerve Net - physiopathology; Network topology; Schizophrenia - physiopathology; Wavelet transforms; Weight measurement
• ISSN: 1557-170X
• DOI: 10.1109/EMBC.2016.7590798

The aim of this study was to assess brain complexity dynamics in schizophrenia (SCH) patients during an auditory oddball task. For this task, we applied a novel graph measure based on the balance of the node weights distribution. Previous studies applied complexity parameters that were strongly dependent on network topology. This could bias the results, as well as making correction techniques, such as surrogating process, necessary. In the present study, we applied a novel graph complexity measure derived from the information theory: Shannon Graph Complexity (SGC). Complexity patterns from 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 a more balanced network, mainly in the connections related to long-range interactions. Since these changes are significantly more pronounced in controls, a deficit in the neural network reorganization can be associated with SCH. In addition, an accuracy of 72.5% was obtained using a receiver operating characteristic curve with a leave-one-out cross-validation procedure. The independence of network topology has been demonstrated by the novel complexity measure proposed in this study, therefore, it complements traditional graph measures as a means to characterize brain networks.