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艾司西酞普兰治疗中神经振荡的调制:抑郁症研究中的加拿大生物标志物整合网络。

Modulation of neural oscillations in escitalopram treatment: a Canadian biomarker integration network in depression study.

机构信息

eBrain Lab, School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, British Columbia, Canada.

University of Toronto, Toronto, Ontario, Canada.

出版信息

Transl Psychiatry. 2024 Oct 12;14(1):432. doi: 10.1038/s41398-024-03110-8.

DOI:10.1038/s41398-024-03110-8
PMID:39396045
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11470922/
Abstract

Current pharmacological agents for depression have limited efficacy in achieving remission. Developing and validating new medications is challenging due to limited biological targets. This study aimed to link electrophysiological data and symptom improvement to better understand mechanisms underlying treatment response. Longitudinal changes in neural oscillations were assessed using resting-state electroencephalography (EEG) data from two Canadian Biomarker Integration Network in Depression studies, involving pharmacological and cognitive behavioral therapy (CBT) trials. Patients in the pharmacological trial received eight weeks of escitalopram, with treatment response defined as ≥ 50% decrease in Montgomery-Åsberg Depression Rating Scale (MADRS). Early (baseline to week 2) and late (baseline to week 8) changes in neural oscillation were investigated using relative power spectral measures. An association was found between an initial increase in theta and symptom improvement after 2 weeks. Additionally, late increases in delta and theta, along with a decrease in alpha, were linked to a reduction in MADRS after 8 weeks. These late changes were specifically observed in responders. To assess specificity, we extended our analysis to the independent CBT cohort. Responders exhibited an increase in delta and a decrease in alpha after 2 weeks. Furthermore, a late (baseline to week 16) decrease in alpha was associated with symptom improvement following CBT. Results suggest a common late decrease in alpha across both treatments, while modulatory effects in theta may be specific to escitalopram treatment. This study offers insights into electrophysiological markers indicating a favorable response to antidepressants, enhancing our comprehension of treatment response mechanisms in depression.

摘要

目前用于治疗抑郁症的药物在实现缓解方面效果有限。由于生物靶点有限,开发和验证新药物具有挑战性。本研究旨在将电生理数据与症状改善联系起来,以更好地了解治疗反应的机制。使用来自加拿大抑郁症生物标志物整合网络的两项研究中的静息态脑电图 (EEG) 数据评估神经振荡的纵向变化,这些研究涉及药理学和认知行为疗法 (CBT) 试验。药理学试验中的患者接受了 8 周的依地普仑治疗,以蒙哥马利-阿斯伯格抑郁评定量表 (MADRS) 至少下降 50%来定义治疗反应。使用相对功率谱测量方法研究了神经振荡的早期(基线至第 2 周)和晚期(基线至第 8 周)变化。发现初始 theta 增加与 2 周后症状改善之间存在关联。此外,晚期 delta 和 theta 增加以及 alpha 减少与 8 周后 MADRS 降低有关。这些晚期变化仅在应答者中观察到。为了评估特异性,我们将分析扩展到独立的 CBT 队列。在第 2 周,应答者表现出 delta 增加和 alpha 减少。此外,CBT 后 alpha 的晚期(基线至第 16 周)减少与症状改善相关。结果表明,两种治疗方法都存在 alpha 的共同晚期减少,而 theta 的调制作用可能是依地普仑治疗特有的。这项研究提供了对表明对抗抑郁药有良好反应的电生理标志物的见解,增强了我们对抑郁症治疗反应机制的理解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1816/11470922/34e78caf3246/41398_2024_3110_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1816/11470922/20f18a409cad/41398_2024_3110_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1816/11470922/ed2a562634ef/41398_2024_3110_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1816/11470922/6a4e5bbe52dc/41398_2024_3110_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1816/11470922/34e78caf3246/41398_2024_3110_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1816/11470922/20f18a409cad/41398_2024_3110_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1816/11470922/ed2a562634ef/41398_2024_3110_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1816/11470922/6a4e5bbe52dc/41398_2024_3110_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1816/11470922/34e78caf3246/41398_2024_3110_Fig4_HTML.jpg

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本文引用的文献

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Developing an Electroencephalography-Based Model for Predicting Response to Antidepressant Medication.开发一种基于脑电图的模型以预测对抗抑郁药物的反应。
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Resting-state EEG delta and alpha power predict response to cognitive behavioral therapy in depression: a Canadian biomarker integration network for depression study.静息态 EEG 德尔塔和阿尔法功率可预测抑郁症对认知行为疗法的反应:加拿大抑郁症生物标志物整合网络研究。
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Use of Machine Learning for Predicting Escitalopram Treatment Outcome From Electroencephalography Recordings in Adult Patients With Depression.使用机器学习从抑郁症成年患者的脑电图记录中预测艾司西酞普兰治疗结果。
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