Salience Network Functional Connectivity Predicts Placebo Effects in Major Depression.
Salience Network Functional Connectivity Predicts Placebo Effects in Major Depression.
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DOI：
10.1016/j.bpsc.2015.10.002
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发表时间：
2016-01
期刊：
影响因子：
--
通讯作者：
Peciña M
中科院分区：
文献类型：
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作者：
Sikora M;Heffernan J;Avery ET;Mickey BJ;Zubieta JK;Peciña M
最近的神经影像学研究表明，重度抑郁症（MDD）的内在脑网络中存在静息状态功能连接（rsFC）异常；然而，它们作为治疗反应预测因子的作用尚未被探讨。在这里，我们研究基于网络的rsFC是否能预测重度抑郁症的抗抑郁药和安慰剂效应。我们进行了一项为期两周的随机对照试验，相同的安慰剂（被描述为具有“有效的”速效抗抑郁作用或“无效的”），随后进行了为期十周的开放标签抗抑郁药物治疗。29名参与者在每一种安慰剂条件下完成了rsFC功能磁共振成像扫描。使用独立分量分析从静息状态血氧水平依赖的信号波动中分离出网络。基线和安慰剂诱导的rsFC在默认模式、显著性和执行网络中的变化与安慰剂和抗抑郁药物治疗反应的关系进行了检查。显著性神经网络中吻侧前扣带（rACC）的rsFC基线增加，该区域通常与安慰剂镇痛的形成和MDD治疗反应的预测有关，与1周有效安慰剂治疗和10周抗抑郁治疗的更大反应相关。机器学习进一步证明，显著网络rsFC的增加，主要是在rACC内，显著预测个体对安慰剂的反应。这些数据表明，显著性网络中的基线rsFC与临床安慰剂反应有关。这些信息可用于确定哪些患者将从低剂量抗抑郁药物或非药物治疗中获益，或在临床试验中开发安慰剂效应的生物标志物。
Recent neuroimaging studies have demonstrated resting-state functional connectivity (rsFC) abnormalities among intrinsic brain networks in Major Depressive Disorder (MDD); however, their role as predictors of treatment response has not yet been explored. Here, we investigate whether network-based rsFC predicts antidepressant and placebo effects in MDD. We performed a randomized controlled trial of two weeklong, identical placebos (described as having either “active” fast-acting, antidepressant effects or as “inactive”) followed by a ten-week open-label antidepressant medication treatment. Twenty-nine participants underwent a rsFC fMRI scan at the completion of each placebo condition. Networks were isolated from resting-state blood-oxygen-level-dependent signal fluctuations using independent component analysis. Baseline and placebo-induced changes in rsFC within the default-mode, salience, and executive networks were examined for associations with placebo and antidepressant treatment response. Increased baseline rsFC in the rostral anterior cingulate (rACC) within the salience network, a region classically implicated in the formation of placebo analgesia and the prediction of treatment response in MDD, was associated with greater response to one week of active placebo and ten weeks of antidepressant treatment. Machine learning further demonstrated that increased salience network rsFC, mainly within the rACC, significantly predicts individual responses to placebo administration. These data demonstrate that baseline rsFC within the salience network is linked to clinical placebo responses. This information could be employed to identify patients who would benefit from lower doses of antidepressant medication or non-pharmacological approaches, or to develop biomarkers of placebo effects in clinical trials.