Introduction: The early and therapy-specific prediction of treatment success in major depressive disorder is of paramount importance due to high lifetime prevalence, and heterogeneity of response to standard medication and symptom expression. Hence, this study assessed the predictability of long-term antidepressant effects of escitalopram based on the short-term influence of citalopram on functional connectivity. Methods: Twenty nine subjects suffering from major depression were scanned twice with resting-state functional magnetic resonance imaging under the influence of intravenous citalopram and placebo in a randomized, double-blinded cross-over fashion. Symptom factors were identified for the Hamilton depression rating scale (HAM-D) and Beck's depression inventory (BDI) taken before and after a median of seven weeks of escitalopram therapy. Predictors were calculated from whole-brain functional connectivity, fed into robust regression models, and cross-validated. Results: Significant predictive power could be demonstrated for one HAM-D factor describing insomnia and the total score (r = 0.45–0.55). Remission and response could furthermore be predicted with an area under the receiver operating characteristic curve of 0.73 and 0.68, respectively. Functional regions with high influence on the predictor were located especially in the ventral attention, fronto-parietal, and default mode networks. Conclusion: It was shown that medication-specific antidepressant symptom improvements can be predicted using functional connectivity measured during acute pharmacological challenge as an easily assessable imaging marker. The regions with high influence have previously been related to major depression as well as the response to selective serotonin reuptake inhibitors, corroborating the advantages of the current approach of focusing on treatment-specific symptom improvements.

中文翻译：

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通过急性静脉激发后脑功能连接的变化预测抗抑郁药西酞普兰的治疗反应

简介：由于终生患病率高以及对标准药物和症状表现的反应的异质性，对重度抑郁症治疗成功的早期和治疗特异性预测至关重要。因此，本研究基于西酞普兰对功能连接的短期影响，评估了依他普仑长期抗抑郁作用的可预测性。方法：在静脉注射西酞普兰和安慰剂的影响下，以随机、双盲交叉方式对 29 名患有重性抑郁症的受试者进行了两次静息态功能磁共振成像扫描。症状因素被确定为汉密尔顿抑郁量表 (HAM-D) 和贝克抑郁量表 (BDI)，在艾司西酞普兰治疗中位数为 7 周之前和之后进行。根据全脑功能连接计算预测变量，输入稳健的回归模型并进行交叉验证。结果：对于一个描述失眠的 HAM-D 因素和总分（r = 0.45–0.55），可以证明具有显着的预测能力。此外，还可以使用分别为 0.73 和 0.68 的受试者工作特征曲线下面积来预测缓解和反应。对预测因子影响较大的功能区域尤其位于腹侧注意力、额顶叶和默认模式网络中。结论：结果表明，使用在急性药理学挑战期间测量的功能连通性作为一种易于评估的成像标志物，可以预测药物特异性抗抑郁症状的改善。