Brain regulation of emotional conflict predicts antidepressant treatment response for depression
Brain regulation of emotional conflict predicts antidepressant treatment response for depression
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DOI：
10.1038/s41562-019-0732-1
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发表时间：
2019-12-01
影响因子：
29.9
通讯作者：
Trivedi, Madhukar H.
中科院分区：
文献类型：
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作者：
Fonzo, Gregory A.;Etkin, Amit;Trivedi, Madhukar H.
抗抑郁药治疗抑郁症的疗效存在争议，因为与安慰剂相比仅具有适度的优越性。然而，抑郁症的神经生物学异质性可能会限制整体抗抑郁药的疗效。我们试图通过测试治疗前对情绪冲突的反应和调节过程中的大脑激活来确定对抗抑郁治疗反应的神经生物学表型，作为抗抑郁药舍曲林与安慰剂相比临床益处的调节因素。利用一项大型随机对照试验的神经影像数据，我们发现在调节情绪冲突期间大脑活动对临床益处的广泛调节，其中冲突反应区域的更大下调预示着更好的舍曲林结果。使用大脑指标进行治疗预测的机器学习优于根据临床和人口统计变量训练的模型。我们的研究结果表明，抗抑郁反应是通过关键的自我调节情绪能力背后的大脑活动来预测的。在精神病学中利用基于大脑的测量方法将开辟一条通往更好的个性化治疗、完善的机制见解和改善结果的道路。
The efficacy of antidepressant treatment for depression is controversial due to the only modest superiority demonstrated over placebo. However, neurobiological heterogeneity within depression may limit overall antidepressant efficacy. We sought to identify a neurobiological phenotype responsive to antidepressant treatment by testing pretreatment brain activation during response to, and regulation of, emotional conflict as a moderator of the clinical benefit of the antidepressant sertraline versus placebo. Using neuroimaging data from a large randomized controlled trial, we found widespread moderation of clinical benefits by brain activity during regulation of emotional conflict, in which greater downregulation of conflict-responsive regions predicted better sertraline outcomes. Treatment-predictive machine learning using brain metrics outperformed a model trained on clinical and demographic variables. Our findings demonstrate that antidepressant response is predicted by brain activity underlying a key self-regulatory emotional capacity. Leveraging brain-based measures in psychiatry will forge a path toward better treatment personalization, refined mechanistic insights and improved outcomes.