Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas (Chin Fatt, Jha, Cooper, South, Grannemann, Carmody, Greer, Kurian, Trivedi); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Jha); Department of Psychiatry and Behavioral Sciences and Stanford Neurosciences Institute, Stanford University, Stanford, Calif. (Fonzo, Etkin); Mental Illness Research, Education, and Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, Calif. (Fonzo, Etkin); Department of Psychiatry, Massachusetts General Hospital, Boston (Fava); New York State Psychiatric Institute and Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York (McGrath, Adams, Weissman); Department of Psychiatry, University of Michigan School of Medicine, Ann Arbor (McInnis); Department of Psychiatry and Behavioral Science and Department of Radiology, Stony Brook University, Stony Brook, N.Y. (Parsey); and Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh (Phillips).
Am J Psychiatry. 2020 Feb 1;177(2):143-154. doi: 10.1176/appi.ajp.2019.18070870. Epub 2019 Sep 20.
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.
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.
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.
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.
重度抑郁症与支持情绪处理、执行功能和奖励处理的多个大脑网络的静息状态功能连接异常有关。本研究的目的是确定大脑区域之间的静息状态连接模式是否可以预测抗抑郁药(舍曲林)与安慰剂相比的不同治疗效果。
参与建立临床护理中抗抑郁药反应的调节因子和生物标志物(EMBARC)研究的参与者在基线时接受了结构和静息状态功能磁共振成像。然后,参与者被随机分配接受舍曲林或安慰剂治疗 8 周(N=279)。利用基于感兴趣区域的方法计算大脑区域之间的功能连接。线性混合模型意向治疗分析用于识别调节(即,差异预测)舍曲林和安慰剂臂之间结果的大脑区域。
对舍曲林反应的预测涉及几种内部和网络间连接模式。一般来说,默认模式网络内的更高连接性特异性地预测了舍曲林的更好结果,而默认模式和执行控制网络之间的网络间连接性更强也是如此。相比之下,默认模式网络和执行控制网络之间的海马连接既预测了安慰剂的结果,也预测了舍曲林的结果(方向相反)。
这项研究确定了治疗结果的特定功能网络调节因子,涉及到已知受重度抑郁症影响的大脑网络。具体来说,大脑区域之间和内部网络的功能连接模式似乎在识别重度抑郁症药物治疗的有利反应方面发挥着重要作用。