当前位置:科学网首页 > 小柯机器人 >详情
新方法可预测重度抑郁症患者的抗抑郁反应
作者:小柯机器人 发布时间:2020/2/13 10:23:02

美国斯坦福大学Amit Etkin及其团队发现脑电图可预测重度抑郁症患者的抗抑郁反应。相关论文2020年2月10日在线发表在国际学术期刊《自然—生物技术》杂志上。

研究人员试图确定与安慰剂相比抗抑郁治疗反应引起的神经生物学特征。研究人员设计了一种适合静息态脑电图(EEG)的潜在空间机器学习算法,并将其应用于最大的影像耦合、安慰剂对照的抗抑郁药研究(n = 309)。该方法准确预测了症状的改善无论是使用抗抑郁药舍曲林(相对于安慰剂)还是在不同研究地点和脑电图设备中。舍曲林预测的脑电图特征普遍适用于两个抑郁症样本,其中反映了一般抗抑郁药物的反应性,并与重复经颅磁刺激治疗结果不同。

此外,同时进行经颅磁刺激和脑电图测量,研究人员发现舍曲林静息态的脑电图特征预示着前额叶神经反应。该研究通过针对脑电图的计算模型提高了人们对抗抑郁药治疗神经生物学的理解,并为个性化的抑郁症治疗提供了临床途径。

据了解,抗抑郁药已被广泛列为处方药,但与于安慰剂相比其疗效并不高,造成这种现象的部分原因是因为重度抑郁症在临床上表现出生物学异质性。

附:英文原文

Title: An electroencephalographic signature predicts antidepressant response in major depression

Author: Wei Wu, Yu Zhang, Jing Jiang, Molly V. Lucas, Gregory A. Fonzo, Camarin E. Rolle, Crystal Cooper, Cherise Chin-Fatt, Noralie Krepel, Carena A. Cornelssen, Rachael Wright, Russell T. Toll, Hersh M. Trivedi, Karen Monuszko, Trevor L. Caudle, Kamron Sarhadi, Manish K. Jha, Joseph M. Trombello, Thilo Deckersbach, Phil Adams, Patrick J. McGrath, Myrna M. Weissman, Maurizio Fava, Diego A. Pizzagalli, Martijn Arns, Madhukar H. Trivedi, Amit Etkin

Issue&Volume: 2020-02-10

Abstract: Antidepressants are widely prescribed, but their efficacy relative to placebo is modest, in part because the clinical diagnosis of major depression encompasses biologically heterogeneous conditions. Here, we sought to identify a neurobiological signature of response to antidepressant treatment as compared to placebo. We designed a latent-space machine-learning algorithm tailored for resting-state electroencephalography (EEG) and applied it to data from the largest imaging-coupled, placebo-controlled antidepressant study (n=309). Symptom improvement was robustly predicted in a manner both specific for the antidepressant sertraline (versus placebo) and generalizable across different study sites and EEG equipment. This sertraline-predictive EEG signature generalized to two depression samples, wherein it reflected general antidepressant medication responsivity and related differentially to a repetitive transcranial magnetic stimulation treatment outcome. Furthermore, we found that the sertraline resting-state EEG signature indexed prefrontal neural responsivity, as measured by concurrent transcranial magnetic stimulation and EEG. Our findings advance the neurobiological understanding of antidepressant treatment through an EEG-tailored computational model and provide a clinical avenue for personalized treatment of depression.

DOI: 10.1038/s41587-019-0397-3

Source: https://www.nature.com/articles/s41587-019-0397-3

期刊信息

Nature Biotechnology:《自然—生物技术》,创刊于1996年。隶属于施普林格·自然出版集团,最新IF:31.864
官方网址:https://www.nature.com/nbt/
投稿链接:https://mts-nbt.nature.com/cgi-bin/main.plex