Resting state functional connectivity explains individual scores of multiple clinical measures for major depression

Kosuke Yoshida, Yu Shimizu, Junichiro Yoshimoto, Shigeru Toki, Go Okada, Masahiro Takamura, Yasumasa Okamoto, Shigeto Yamawaki, Kenji Doya

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Recent studies have revealed that resting state functional connectivity is associated with major depressive disorder (MDD). However, the relationship between functional connectivity and clinical measures for the detailed assessment of depression remains unclear. The objective of our study is thus to associate functional connectivity of depressed patients and healthy controls with their individual clinical measures, using a statistical method called partial least squares analysis (PLS). We demonstrated that this method could predict certain clinical measures based on a limited number of functional connections and provided benefits to the prediction performance through incorporation of the subject's age and the estimation of multiple measures simultaneously. Generalizability of the prediction model was assured through leave one out cross validation. The results showed that for BDI-II and SHAPS the most contributing connections concerned cuneus, precuneus and middle frontal cortex and areas of the cerebellum. While the relationship was similar for PANAS(n), it showed its strongest relation with functional connection between calcarine and insula.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
Editorslng. Matthieu Schapranow, Jiayu Zhou, Xiaohua Tony Hu, Bin Ma, Sanguthevar Rajasekaran, Satoru Miyano, Illhoi Yoo, Brian Pierce, Amarda Shehu, Vijay K. Gombar, Brian Chen, Vinay Pai, Jun Huan
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1078-1083
Number of pages6
ISBN (Electronic)9781467367981
DOIs
Publication statusPublished - 16-12-2015
Externally publishedYes
EventIEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015 - Washington, United States
Duration: 09-11-201512-11-2015

Publication series

NameProceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015

Conference

ConferenceIEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
Country/TerritoryUnited States
CityWashington
Period09-11-1512-11-15

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence
  • Health Informatics
  • Biomedical Engineering

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