TY - GEN
T1 - Resting state functional connectivity explains individual scores of multiple clinical measures for major depression
AU - Yoshida, Kosuke
AU - Shimizu, Yu
AU - Yoshimoto, Junichiro
AU - Toki, Shigeru
AU - Okada, Go
AU - Takamura, Masahiro
AU - Okamoto, Yasumasa
AU - Yamawaki, Shigeto
AU - Doya, Kenji
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/16
Y1 - 2015/12/16
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84962339937&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84962339937&partnerID=8YFLogxK
U2 - 10.1109/BIBM.2015.7359831
DO - 10.1109/BIBM.2015.7359831
M3 - Conference contribution
AN - SCOPUS:84962339937
T3 - Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
SP - 1078
EP - 1083
BT - Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
A2 - Schapranow, lng. Matthieu
A2 - Zhou, Jiayu
A2 - Hu, Xiaohua Tony
A2 - Ma, Bin
A2 - Rajasekaran, Sanguthevar
A2 - Miyano, Satoru
A2 - Yoo, Illhoi
A2 - Pierce, Brian
A2 - Shehu, Amarda
A2 - Gombar, Vijay K.
A2 - Chen, Brian
A2 - Pai, Vinay
A2 - Huan, Jun
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
Y2 - 9 November 2015 through 12 November 2015
ER -