Neurobehavioural predictors of depression relapse

我们的目标是a)确定抗抑郁药物（ADM）停药后重度抑郁症（MDD）复发高风险的神经影像学预测因素；b)检查药物停药对缓解抑郁状态的影响。这是使用行为和神经生物学方法对现有临床精神病理实体进行治疗结果分层的努力的一部分。目前的抑郁症药物治疗方案导致高达70%的患者最终缓解。由于停药后复发的风险很高（6个月内30- 60%），指南建议在不同时期继续治疗。然而，医生再次面临类似的问题：(1)患者以非常高的比例独立停止精神药物治疗，特别是在达到缓解后；（ii）这些建议没有考虑到个体的可变性。安全停用ADM的标记物将有助于识别有风险的患者，这些患者可以根据更强有力的、个人有效的理由推荐继续或进一步治疗。通过提供一个客观的治疗终点，可以提高治疗的一致性。此外，尽管缓解后情感功能的神经生物学已经被研究过，但ADM的作用仍然知之甚少。我们建议对76名缓解至少6个月且打算独立于本研究停止使用adm的患者进行为期6个月的随访研究。我们将测试三种神经成像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指标和已建立的预测指标。前者的几个版本已经在预测治疗反应方面建立了有效性。后者被认为是抑郁症易感性的一个重要特征。第二，受试者将完成一个既定的计划任务，测量厌恶结果对计划的影响。这将被稍微修改，以额外量化无助感。所有受试者将接受两次扫描，并被分成两组，每组人数相等。在1W2组中，扫描1发生在停药之前，扫描2发生在停药后5-20个ADM半衰期之间。在12W组，两次扫描都将在停药前进行：扫描1大约在停药前5-20个ADM半衰期，扫描2正好在停药前。我们将使用扫描1作为复发的主要预测指标。我们将使用组间的相互作用和扫描来检查药物戒断的效果。在辅助分析中，我们还将使用1W2组扫描1和扫描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的增量预测能力。

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.