Responders to rTMS for depression show increased fronto-midline theta and theta connectivity compared to non-responders

磁刺激 脑电图 阿尔法(金融) 萧条(经济学) 心理学 心情 难治性抑郁症 听力学 生物标志物 神经科学 刺激 医学 重性抑郁障碍 精神科 临床心理学 心理测量学 经济 化学 宏观经济学 生物化学 结构效度
作者
Neil W. Bailey,Kate Elizabeth Hoy,Nigel C. Rogasch,Richard Hilton Siddall Thomson,Susan McQueen,David Elliot,Caley Sullivan,Ben D. Fulcher,Zafiris J. Daskalakis,Paul B. Fitzgerald
出处
期刊:Brain Stimulation [Elsevier]
卷期号:11 (1): 190-203 被引量:116
标识
DOI:10.1016/j.brs.2017.10.015
摘要

Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for depression, but only some individuals respond. Predicting response could reduce patient and clinical burden. Neural activity related to working memory (WM) has been related to mood improvements, so may represent a biomarker for response prediction.We expected higher theta and alpha activity in responders compared to non-responders to rTMS.Fifty patients with treatment resistant depression and twenty controls performed a WM task while electroencephalography (EEG) was recorded. Patients underwent 5-8 weeks of rTMS treatment, repeating the EEG at week 1 (W1). Of the 39 participants with valid WM-related EEG data from baseline and W1, 10 were responders. Comparisons between responders and non-responders were made at baseline and W1 for measures of theta (4-8 Hz), upper alpha (10-12.5 Hz), and gamma (30-45 Hz) power, connectivity, and theta-gamma coupling. The control group's measures were compared to the depression group's baseline measures separately.Responders showed higher levels of WM-related fronto-midline theta power and theta connectivity compared to non-responders at baseline and W1. Responder's fronto-midline theta power and connectivity was similar to controls. Responders also showed an increase in gamma connectivity from baseline to W1, with a concurrent improvement in mood and WM reaction times. An unbiased combination of all measures provided mean sensitivity of 0.90 at predicting responders and specificity of 0.92 in a predictive machine learning algorithm.Baseline and W1 fronto-midline theta power and theta connectivity show good potential for predicting response to rTMS treatment for depression.
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