@inproceedings{190ceed5ad134373b42330999dfc76c9,
title = "Resilience of functional networks: A potential indicator for classifying bipolar disorder and schizophrenia",
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.",
author = "Chen, {Yen Ling} and Kao, {Zih Kai} and Wang, {Po Shan} and Huang, {Chao Wen} and Chen, {Yi Chieh} and Wu, {Yu Te}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 International Automatic Control Conference, CACS 2017 ; Conference date: 12-11-2017 Through 15-11-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/CACS.2017.8284247",
language = "English",
series = "2017 International Automatic Control Conference, CACS 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--5",
booktitle = "2017 International Automatic Control Conference, CACS 2017",
address = "美國",
}