Daisuke Koshiyama1, Kenji Kirihara1, Kaori Usui1, Mariko Tada2, Mao Fujioka1, Susumu Morita1, Shintaro Kawakami1, Mika Yamagishi1, Hanako Sakurada1, Eisuke Sakakibara1, Yoshihiro Satomura1, Naohiro Okada2, Shinsuke Kondo1, Tsuyoshi Araki1, Seichiro Jinde1, Kiyoto Kasai3. 1. Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. 2. Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; The International Research Center for Neurointelligence (WPI-IRCN) at The University of Tokyo Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan. 3. Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; The International Research Center for Neurointelligence (WPI-IRCN) at The University of Tokyo Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan. Electronic address: kasaik-tky@umin.net.
Abstract
BACKGROUND: Quality of life is severely impaired in patients with depressive disorders. Previous studies have focused on biomarkers predicting depressive symptomatology; however, studies investigating biomarkers predicting quality of life outcomes are limited. Improving quality of life is important because it is related not only to mental health but also to physical health. We need to develop a biomarker related to quality of life as a therapeutic target for patients with depressive disorders. Resting-state electroencephalography (EEG) is easy to record in clinical settings. The index of bandwidth spectral power predicts treatment response in depressive disorders and thus may be a candidate biomarker. However, no longitudinal studies have investigated whether EEG-recorded power could predict quality of life outcomes in patients with depressive disorders. METHODS: The resting-state EEG-recorded bandwidth spectral power at baseline and the World Health Organization Quality of Life (QOL)-26 scores at 3-year follow-up were measured in 44 patients with depressive disorders. RESULTS: The high beta band power (20-30 Hz) at baseline significantly predicted QOL at the 3-year follow-up after considering depressive symptoms and medication effects in a longitudinal investigation in patients with depressive disorders (β = 0.38, p = 0.01). LIMITATIONS: We did not have healthy subjects as a comparison group in this study. CONCLUSIONS: Our findings suggest that resting-state beta activity has the potential to be a useful biomarker for predicting future quality of life outcomes in patients with depressive disorders.
BACKGROUND: Quality of life is severely impaired in patients with depressive disorders. Previous studies have focused on biomarkers predicting depressive symptomatology; however, studies investigating biomarkers predicting quality of life outcomes are limited. Improving quality of life is important because it is related not only to mental health but also to physical health. We need to develop a biomarker related to quality of life as a therapeutic target for patients with depressive disorders. Resting-state electroencephalography (EEG) is easy to record in clinical settings. The index of bandwidth spectral power predicts treatment response in depressive disorders and thus may be a candidate biomarker. However, no longitudinal studies have investigated whether EEG-recorded power could predict quality of life outcomes in patients with depressive disorders. METHODS: The resting-state EEG-recorded bandwidth spectral power at baseline and the World Health Organization Quality of Life (QOL)-26 scores at 3-year follow-up were measured in 44 patients with depressive disorders. RESULTS: The high beta band power (20-30 Hz) at baseline significantly predicted QOL at the 3-year follow-up after considering depressive symptoms and medication effects in a longitudinal investigation in patients with depressive disorders (β = 0.38, p = 0.01). LIMITATIONS: We did not have healthy subjects as a comparison group in this study. CONCLUSIONS: Our findings suggest that resting-state beta activity has the potential to be a useful biomarker for predicting future quality of life outcomes in patients with depressive disorders.
Authors: Kumiko Tanaka-Koshiyama; Daisuke Koshiyama; Makoto Miyakoshi; Yash B Joshi; Juan L Molina; Joyce Sprock; David L Braff; Gregory A Light Journal: Front Psychiatry Date: 2020-08-31 Impact factor: 4.157