Resilience of functional networks: A potential indicator for classifying bipolar disorder and schizophrenia

Yen Ling Chen, Zih Kai Kao, Po Shan Wang, Chao Wen Huang, Yi Chieh Chen, Yu Te Wu*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

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.

Original languageEnglish
Title of host publication2017 International Automatic Control Conference, CACS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9781538639009
DOIs
StatePublished - 7 Feb 2018
Event2017 International Automatic Control Conference, CACS 2017 - Pingtung, Taiwan
Duration: 12 Nov 201715 Nov 2017

Publication series

Name2017 International Automatic Control Conference, CACS 2017
Volume2017-November

Conference

Conference2017 International Automatic Control Conference, CACS 2017
Country/TerritoryTaiwan
CityPingtung
Period12/11/1715/11/17

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