OBJECTIVE Major depressive disorder is associated with aberrant resting-state functional connectivity across multiple brain networks supporting emotion processing, executive function, and reward processing. The purpose of this study was to determine whether patterns of resting-state connectivity between brain regions predict differential outcome to antidepressant medication (sertraline) compared with placebo. METHODS Participants in the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study underwent structural and resting-state functional MRI at baseline. Participants were then randomly assigned to receive either sertraline or placebo treatment for 8 weeks (N=279). A region of interest-based approach was utilized to compute functional connectivity between brain regions. Linear mixed-model intent-to-treat analyses were used to identify brain regions that moderated (i.e., differentially predicted) outcomes between the sertraline and placebo arms. RESULTS Prediction of response to sertraline involved several within- and between-network connectivity patterns. In general, higher connectivity within the default mode network predicted better outcomes specifically for sertraline, as did greater between-network connectivity of the default mode and executive control networks. In contrast, both placebo and sertraline outcomes were predicted (in opposite directions) by between-network hippocampal connectivity. CONCLUSIONS This study identified specific functional network-based moderators of treatment outcome involving brain networks known to be affected by major depression. Specifically, functional connectivity patterns of brain regions between and within networks appear to play an important role in identifying a favorable response for a drug treatment for major depressive disorder.

中文翻译：

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功能性大脑连通性的内在模式在减轻抑郁症患者抗抑郁治疗反应中的作用。

目的重度抑郁症与多个情绪网络，支持情绪处理，执行功能和奖励处理的异常静息状态功能连接有关。这项研究的目的是确定与安慰剂相比，大脑区域之间的静止状态连接方式是否可预测抗抑郁药物（舍曲林）的预后不同。方法在临床护理（EMBARC）研究中建立抗抑郁反应的调节剂和生物特征的参与者在基线时进行了结构性和静止状态功能性MRI。然后将参与者随机分配接受舍曲林或安慰剂治疗8周（N = 279）。基于兴趣区域的方法用于计算大脑区域之间的功能连接性。线性混合模型意图治疗分析用于确定缓和（即差异预测）舍曲林和安慰剂组之间结果的大脑区域。结果对舍曲林反应的预测涉及几种网络内部和网络之间的连接模式。通常，默认模式网络内的较高连接性特别针对舍曲林预测了更好的结果，默认模式网络和执行控制网络之间的网络之间的连接性也更高。相比之下，安慰剂和舍曲林的结果都是通过网络之间的海马连接性来预测的（相反方向）。结论本研究确定了基于特定功能网络的治疗结果调节剂，涉及已知受严重抑郁影响的脑网络。具体来说，