Local and large-scale resting-state oscillatory dysfunctions for early antidepressant response prediction in major depressive disorder

脑磁图 重性抑郁障碍 抗抑郁药 静息状态功能磁共振成像 哈姆德 心理学 接收机工作特性 汉密尔顿抑郁量表 萧条(经济学) 评定量表 电生理学 皮质电图 神经科学 内科学 精神科 医学 心情 脑电图 发展心理学 海马体 经济 宏观经济学
作者
Shui Tian,Qiang Wang,Siqi Zhang,Zhilu Chen,Zhongpeng Dai,Wei Zhang,Zhijian Yao,Qing Lü
出处
期刊:Journal of Affective Disorders [Elsevier BV]
卷期号:340: 751-757 被引量:2
标识
DOI:10.1016/j.jad.2023.08.096
摘要

Magnetoencephalography (MEG) could explore and resolve brain signals with realistic temporal resolution to investigate the underlying electrophysiology of major depressive disorder (MDD) and the treatment efficacy. Here, we explore whether neuro-electrophysiological features of MDD at baseline can be used as a neural marker to predict their early antidepressant response.Sixty-six medication-free patients with MDD and 48 healthy controls were enrolled and underwent resting-state MEG scans. Hamilton depression rating scale (HAMD-17) was assessed at both baseline and after two-week pharmacotherapy. We measured local and large-scale resting-state oscillatory dysfunctions with a data-driven model, the Fitting Oscillations & One-Over F algorithm. Then, we quantified band-limited regional power and functional connectivity between brain regions.After two-week follow-up, 52 patients completed the re-interviews. Thirty-one patients showed early response (ER) to pharmacotherapy and 21 patients did not. Treatment response was defined as at least 50 % reduction of severity reflected by HAMD-17. We observed decreased regional periodic power in patients with MDD comparing to controls. However, patients with ER exhibited that functional couplings across brain regions in both alpha and beta band were increased and significantly correlated with severity of depressive symptoms after treatment. Receiver operating characteristic curves (ROC) further confirmed the predictive ability of baseline large-scale functional connectivity for early antidepressant efficacy (AUC = 0.9969).Relatively small sample size and not a double-blind design.The current study demonstrated the electrophysiological dysfunctions of local neural oscillatory related with depression and highlighted the identification ability of large-scale couplings biomarkers in early antidepressant response prediction.
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