Removing the Rose-Tinted Glasses: The effects of antidepressant drug withdrawal on mood and neurocognitive function

抑郁症是英国最常见、最昂贵的精神健康状况之一，仅在2018年，就有超过7000万份抗抑郁药处方，如SSRIs。事实上，处方数量在过去十年中翻了一番。许多人在一段时间内持续服用抗抑郁药，考虑到它们的副作用，如恶心、失眠和性功能障碍，这并不理想。NHS的成本也相当可观。据估计，大约50%的患者可以安全地停药，但目前很难预测哪些患者可以停止服用SSRIs，哪些不能（复发风险）。因此，寻找区分短暂戒断效应和抑郁复发指标的方法对于改善患者预后和潜在地减轻NHS负担至关重要。在抑郁症中一直观察到情绪处理和情绪失调的偏见。研究已经证明了消极偏见情绪处理在抑郁症状发展中的作用，并表明抗抑郁药通过针对这些消极偏见起作用。这种效果在开始治疗后的几小时或几天内就会出现，可能会导致数周内积极情绪的增加。然而，虽然这些变化有充分的记录，但人们对抗抑郁药戒断对情绪处理和情绪的影响知之甚少。在戒断期间，情绪处理中是否会重新建立低水平的负性偏差？情绪偏差或失调的重新出现是否预示着后来的情绪障碍或抑郁复发？这些信息可能是关键，因为在SSRI停药后，情绪处理的早期变化可能是复发的警告信号。该项目的目的有两个，首先是为了更好地了解抗抑郁药戒断的影响及其时间过程，描述与戒断相关的情绪和情感处理变化。其次，能够识别症状和情绪变化的早期标记，这可能有助于预测抑郁症复发。为了实现这些目标，我们将研究初级保健患者。该项目将涉及经验采样、网络分析和神经认知测试（专注于情绪处理）的新颖组合。我们将结合研究方法，提供新的见解和深入了解抗抑郁药的戒断效应。该项目将与研究人员、临床医生、服用SSRIs的患者、NHS专员和政策制定者相关。从科学的角度来看，抗抑郁药停药后发生的情绪处理变化将被表征，这可能会告知并潜在地修改抗抑郁药作用的理论。然而，从临床角度来看，情绪变化和其他戒断症状的特征以及相关的网络分析将为抗抑郁药戒断管理提供信息。该项目跨越了ESRC和MRC的优先级，并有可能导致与社会科学家广泛受众相关的方法创新。我们希望这个项目能够识别出抑郁症复发的早期标志，从而有针对性地预防复发（同时也帮助那些可以安全退出的人这样做）。迫切需要这项研究来帮助患者安全有效地退出抗抑郁药，并降低持续使用抗抑郁药的个人、社会和经济成本。可以在阿伯丁和阿姆斯特丹寻求专业培训。

Depression is among the most common and costly mental health conditions in the UK with over 70 million prescriptions for antidepressants, such as SSRIs, in 2018 alone. Indeed, the number of prescriptions has doubled in the past decade. Many individuals are on antidepressants for a sustained period of time which is not ideal given their negative side effects, such as nausea, insomnia and sexual dysfunction. The cost to the NHS is also substantial. It is estimated that >50% of patients can safely withdraw from medication but it is currently difficult to predict which patients can stop taking SSRIs and which cannot (risk of relapse). Thus, finding ways of distinguishing between transient withdrawal effects and indicators of depressive relapse is vital in improving patient outcomes and potentially reducing the burden on the NHS.Biases in emotion processing and emotion dysregulation have consistently been observed in depression. Research has demonstrated a role of negatively-biased emotion processing in the development of depressive symptoms and has suggested that antidepressants work by targeting these negative biases. Such effects occur within hours or days after starting treatment, potentially leading to increases in positive mood over the course of weeks. However, whilst these changes are well-documented, little is known about the effects of antidepressant withdrawal on emotion processing and mood. Is there a re-establishment of low-level negative biases in emotion processing during withdrawal and does the re-emergence of emotional biases or dysregulation predict later mood disturbances or depressive relapse? This information might be key as early changes in emotion processing, after SSRI withdrawal, may serve as warning signs of relapse.The project's aims are twofold, firstly to characterise mood and emotion processing changes associated with withdrawal over time in order to better understand the effects of antidepressant withdrawal and their time course. Secondly, to enable the identification of early markers of symptom and mood changes that might aid in predicting depressive relapse. To address these aims, we will study primary care patients. The project will involve a novel combination of experience sampling, network analyses and neurocognitive testing (focusing on emotion processing).We will combine research methods to provide new insights and an in-depth understanding of antidepressant withdrawal effects. The project will be relevant to researchers, clinicians, patients taking SSRIs, NHS commissioners and policy makers. From a scientific perspective, emotion processing changes occurring after antidepressant withdrawal will be characterised, which could inform and potentially modify theories of antidepressant action. Whereas from a clinical perspective, characterising mood changes and other withdrawal symptoms and the associated network analysis will inform antidepressant withdrawal management. The project spans ESRC and MRC priorities and has the potential to lead to methodological innovations that will be relevant to a wide audience of social scientists. We hope the project will identify early markers of depressive relapse that can be targeted specifically to prevent relapse (while also helping those who can withdraw safely to do so). This research is urgently needed to help patients withdraw from antidepressants safely and effectively, and to reduce the personal, societal and economic costs of sustained antidepressant use.Specialist training in Aberdeen and Amsterdam may be sought.