Neurobehavioural predictors of depression relapse

抑郁症复发的神经行为预测因子

• 批准号: 255342426
• 负责人: Professor Dr. Henrik Walter
• 金额: --
• 依托单位: Klinik für Psychiatrie und Psychotherapie (CCM)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2014
• 资助国家: 德国
• 起止时间: 2013-12-31 至 2018-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/255342426?language=en
• 关键词: Neurobehavioural; predictors; depression; relapse

We aim to a) identify neuroimaging predictors of a high risk of Major Depressive Disorder (MDD) relapse after antidepressant medication (ADM) discontinuation; and b) examine the effect of medication withdrawal on the remitted depressed state. This is part of an endeavour to use behavioural and neurobiological measures to stratify existing clinical psychopathological entities with respect to treatment outcomes. Current pharmacological depression treatment options lead to eventual remission in up to 70\% of patients. Because the risk of relapse after discontinuation is high (30-60\% in 6 months), guidelines recommend treatment continuation for various periods. However, physicians then face a similar problem again: (i) patients discontinue psychotropic medication independently at very high rates,particularly after achieving remission; and (ii) these recommendations do not take individual variability into account. Markers for safe ADM discontinuation would help identify at-risk patients in whom continuation or further therapy could be recommended on stronger, individually valid, grounds. By providing an objective end-point to treatment this may enhance concordance with treatment. Furthermore, although the neurobiology of affective function after remission has been examined previously, the contribution of ADM remains poorly understood and characterised. We propose a 6-month follow-up study of 76 patients who have been in remission for a minimum of 6 months and intend to discontinue their ADMs independently of this study. We will test the ability of three neuroimaging biomarkers in predicting early relapse, including both novel predictors derived from computational neuroscience, and established ones. Several versions of the former have established validity in predicting response to treatment. The latter has been suggested to be one important characteristic of depression vulnerability. Second, subjects will undergo an established planning task that measures the impact of aversive outcomes on planning. This will be slightly modified to additionally quantify helplessness. All subjects will undergo scanning twice, and will be divided into two groups of equal size. In group 1W2, scan 1 will occur just prior to medication withdrawal, and scan 2 between 5-20 ADM half-lives after withdrawal. In group 12W, both scans will occur before withdrawal: scan 1 approx 5-20 ADM half-lives before, and scan 2 just prior to withdrawal. We will use scan 1 as the main predictor for relapse. We will use the interaction between groups and scans to examine the effect of medication withdrawal. In a subsidiary analysis, we will also use changes between scan 1 and 2 in group 1W2 to predict relapse. In all cases, we will test for incremental predictive power above and beyond clinically available measurements.

我们的目的是：a）确定停用抗抑郁药物（ADM）后重性抑郁障碍（MDD）复发高风险的神经影像学预测因子;和B）检查停药对缓解的抑郁状态的影响。这是努力使用行为和神经生物学的措施，分层现有的临床精神病理实体方面的治疗结果的一部分。目前的药物抑郁症治疗选择导致高达70%的患者最终缓解。由于停药后复发的风险很高（6个月内为30- 60%），指南建议在不同时期继续治疗。然而，医生们又面临着类似的问题：（i）患者以非常高的比率独立地停止精神药物治疗，特别是在达到缓解后;（ii）这些建议没有考虑到个体差异。安全停用阿霉素的标志物将有助于识别高危患者，可以根据更有力的、个体有效的理由建议他们继续或进一步治疗。通过提供客观的治疗终点，这可能会提高与治疗的一致性。此外，虽然缓解后的情感功能的神经生物学已被检查之前，ADM的贡献仍然知之甚少和特点。 我们建议对76例缓解至少6个月的患者进行6个月的随访研究，这些患者打算独立于本研究停止使用ADM。我们将测试三种神经影像学生物标志物预测早期复发的能力，包括来自计算神经科学的新预测因子和已建立的预测因子。前者的几个版本已经确定了预测治疗反应的有效性。后者被认为是抑郁易感性的一个重要特征。第二，受试者将经历一个既定的计划任务，衡量令人厌恶的结果对计划的影响。这将略有修改，以额外量化无助。所有受试者将接受两次扫描，并将被分为两组，每组人数相同。在组1 W2中，第1次扫描将在停药前进行，第2次扫描将在停药后5-20个ADM半衰期之间进行。在第12 W组中，两次扫描都将在停药前进行：在大约5-20个ADM半衰期前进行扫描1，在停药前进行扫描2。我们将使用扫描1作为复发的主要预测因素。我们将使用组和扫描之间的相互作用来检查药物戒断的影响。在辅助分析中，我们还将使用组1 W2中扫描1和2之间的变化来预测复发。 在所有情况下，我们将测试超出临床可用测量值的增量预测能力。