학술논문

Resilience of functional networks: A potential indicator for classifying bipolar disorder and schizophrenia
Document Type
Conference
Source
2017 International Automatic Control Conference (CACS) Automatic Control Conference (CACS), 2017 International. :1-5 Nov, 2017
Subject
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
Robotics and Control Systems
Transportation
Diseases
Resilience
Magnetic resonance imaging
Testing
Support vector machines
Data models
Hospitals
Language
Abstract
Bipolar disorder and schizophrenia are two prevailing psychiatric disorders with significant overlaps in symptoms, abnormalities, and disease progression. Therefore, it is difficult to differentiate these two diseases without repeated clinical visits. Previous studies demonstrated high accuracy of classification for bipolar disorder and schizophrenia at the individual level by functional connectivity, but few studies focused on classifying between these two diseases directly. In order to assist diagnosis, we investigated further the feasibility of classifying bipolar disorder and schizophrenia by the structure of functional networks. The results revealed 90.0% accuracy of the classification with the sensitivity 1.0 and the specificity 0.80 for the patients with bipolar disorder. The present study indicated that the differences between the characteristics of brain network structures in bipolar disorder and schizophrenia could be the reliable features for the classification and may be the diagnostic indicators in the future.