Neurocognitive signatures predicting risk of recurrent depression

预测抑郁症复发风险的神经认知特征

• 批准号: MR/T017538/1
• 负责人: Roland Zahn
• 金额: $ 137.67万
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FT017538%2F1
• 关键词: Neurocognitive; signatures; predicting; risk; recurrent

Depression is a leading cause of disability, because many people who have recovered from its symptoms ("symptomatic phase") will experience recurring episodes. Most research has focused on the symptomatic phase of depression and often assumed that when people have no symptoms, they are cured. A disorder can, however, be ongoing even when patients do not experience symptoms. In this "asymptomatic" state people can be at high risk of developing symptoms in the future. Many patients take antidepressant medication over years to reduce the risk of recurrence, because our current way of predicting their risk based on number of previous episodes is very inaccurate. It is usually assumed that those treatments working to reduce symptoms of depression will also be beneficial in their future prevention, but this is largely unproven. The development of new treatments that prevent recurrence has been hampered by a lack of knowledge about psychological and brain changes that are risk factors. In an MRC-funded study, we have identified such risk factors in recovered depression patients that predict, on an individual basis, which patient will have another episode in the next year. By adding functional MRI scans, and a novel test of being inclined to self-blame to standard measures, we achieved 83% accuracy, exceeding the recommended target for useful so-called "prognostic markers". In contrast, standard measures alone were no better than chance guessing who would develop another episode. Patients were scanned whilst they experienced self-blame, which is thought to play an important role in depression by decreasing self-worth and hope. We have demonstrated that asymptomatic patients who go on to develop depression show altered connections in the self-blame-related brain network which differed from those who remained well. Despite these encouraging results in 50 patients, it is unknown: 1) whether we can confirm the result that functional MRI and psychological tests of self-blame predict subsequent recurrence of depression in a larger independent group2) whether MRI is needed for predicting who will develop depression at an individual level or could be replaced by adding further psychological and hormonal measures 3) whether the brain networks found to be disrupted when blaming oneself in depression are linked to abnormal stress hormones, the only established chemical risk factor for recurrence 4) whether the disruption in brain networks when blaming oneself makes people more vulnerable to develop depression after a stressful life event measured weekly via a mobile appTo answer these questions, we propose to enrol 150 patients recovered from depression who have stopped their antidepressant medication in accordance with guidelines (as in our previous study). An initial MRI scan, cognitive tests, and stress hormones will be used to predict recurrence after one year. This will deliver much needed evidence for reproducible psychological and brain-based risk factors to 1) inform novel psychological and brain training, as well as brain stimulation treatment approaches and 2) develop a so-called "prognostic marker" to predict recurrence risk for affected individuals. This marker could be used in future clinical trials to select patients that are at high risk of recurrence. This would greatly reduce the number of patients needed for a trial and thereby reduce its cost. Further, if we can replace expensive MRI scans with cheaper measures, the prognostic marker could be studied in future trials to determine whether it helps people with depression to make decisions about continuing their antidepressant medication. After completing this project, our future goal is to facilitate the development of novel treatments more likely to remedy depression in the long-term by preventing recurrence rather than to only treat its symptoms. Our collaborator, Janssen, are actively developing markers for depression recurrence and are highly committed.

抑郁症是导致残疾的主要原因，因为许多从症状中恢复过来的人（“症状期”）会反复发作。大多数研究都集中在抑郁症的症状阶段，通常认为当人们没有症状时，他们就被治愈了。然而，即使患者没有出现症状，疾病也可能持续存在。在这种“无症状”状态下，人们未来出现症状的风险很高。许多患者多年来服用抗抑郁药物来降低复发的风险，因为我们目前基于以前发作次数来预测风险的方法是非常不准确的。人们通常认为，那些减轻抑郁症症状的治疗方法也有利于未来的预防，但这在很大程度上是未经证实的。由于缺乏对风险因素心理和大脑变化的了解，预防复发的新疗法的发展一直受到阻碍。在mrc资助的一项研究中，我们已经在康复的抑郁症患者中确定了这些风险因素，这些因素可以预测，在个体的基础上，哪些患者明年会再次发作。通过在标准测量中加入功能性核磁共振扫描和一项新的自责倾向测试，我们达到了83%的准确率，超过了有用的所谓“预后标记”的推荐目标。相比之下，单独的标准措施并不比随机猜测谁会再次发作更好。患者在经历自责时接受扫描，自责被认为在抑郁症中通过降低自我价值和希望发挥重要作用。我们已经证明，无症状的抑郁症患者在自责相关的大脑网络中表现出与那些保持健康的人不同的连接。尽管在50例患者中取得了令人鼓舞的结果，但尚不清楚：1)我们是否可以证实功能性磁共振成像和自责心理测试预测抑郁症在更大的独立群体中随后复发的结果2)是否需要磁共振成像来预测谁将在个体水平上患上抑郁症，或者可以通过增加进一步的心理和激素测量来取代3)当抑郁症中自责时被发现的大脑网络中断是否与异常的压力激素有关。4)自责时大脑网络的破坏是否会使人们在经历压力生活事件后更容易患上抑郁症。为了回答这些问题，我们建议招募150名抑郁症康复患者，他们已经按照指导原则停止服用抗抑郁药物（正如我们之前的研究一样）。最初的核磁共振扫描、认知测试和应激激素将用于预测一年后的复发。这将为可重复的心理和基于大脑的风险因素提供急需的证据，1)为新的心理和大脑训练以及脑刺激治疗方法提供信息，2)开发所谓的“预后标记”来预测受影响个体的复发风险。该标志物可用于未来的临床试验，以选择复发风险高的患者。这将大大减少试验所需的患者数量，从而降低其成本。此外，如果我们可以用更便宜的方法取代昂贵的核磁共振扫描，那么在未来的试验中就可以研究这种预后标记，以确定它是否有助于抑郁症患者做出继续服用抗抑郁药物的决定。在完成这个项目后，我们未来的目标是促进新的治疗方法的发展，更有可能通过预防复发来长期治疗抑郁症，而不仅仅是治疗其症状。我们的合作者，Janssen，正在积极开发抑郁症复发的标志物，并高度致力于此。

项目成果

AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale.

• DOI: 10.1186/s12888-022-04509-7
• 发表时间: 2023-01-23
• 期刊: BMC psychiatry
• 影响因子: 4.4

Charting brain growth and aging at high spatial precision.

• DOI: 10.7554/elife.72904
• 发表时间: 2022-02-01
• 期刊: eLife
• 影响因子: 7.7
• 作者: Rutherford S;Fraza C;Dinga R;Kia SM;Wolfers T;Zabihi M;Berthet P;Worker A;Verdi S;Andrews D;Han LK;Bayer JM;Dazzan P;McGuire P;Mocking RT;Schene A;Sripada C;Tso IF;Duval ER;Chang SE;Penninx BW;Heitzeg MM;Burt SA;Hyde LW;Amaral D;Wu Nordahl C;Andreasssen OA;Westlye LT;Zahn R;Ruhe HG;Beckmann C;Marquand AF
• 通讯作者: Marquand AF

Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo

• DOI: 10.1038/s44220-023-00187-w
• 发表时间: 2024-01
• 期刊: Nature. Mental Health
• 作者: Cynthia H. Y. Fu;Mathilde Antoniades;G. Erus;Jose A. Garcia;Yong Fan;Danilo Arnone;S. Arnott;Taolin Chen;K. S. Choi;Cherise R. Chin Fatt;B. N. Frey;V. Frokjaer;M. Ganz;Beata R. Godlewska;S. Hassel;K. Ho;Andrew M. McIntosh;Kun Qin;S. Rotzinger;M. Sacchet;J. Savitz;H. Shou;Ashish Singh;A. Stolicyn;Irina Strigo;S. Strother;D. Tosun;Teresa A. Victor;D. Wei;T. Wise;Roland Zahn;Ian M. Anderson;W. E. Craighead;J. Deakin;B. Dunlop;Rebecca Elliott;Qiyong Gong;I. Gotlib;C. Harmer;Sidney H. Kennedy;G. Knudsen;H. Mayberg;Martin P. Paulus;Jiang Qiu;Madhukar H. Trivedi;H. Whalley;Chao-Gan Yan;Allan H. Young;Christos Davatzikos