Coordination Funds

协调基金

• 批准号: 468355968
• 负责人: Professorin Dr. Ulrike Lüken
• 金额: --
• 依托单位: Institut für Psychologie
• 依托单位国家: 德国
• 项目类别: Research Units
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/468355968?language=en
• 关键词: Coordination; Funds

Although cognitive-behavioral therapy (CBT) is a first-line treatment for internalizing disorders, a substantial proportion of patients fails to benefit - with severe consequences for patients and costs for societies. Precision mental health can help to identify patients at risk for non-response (NR) already prior to treatment initialization. The paucity of standard clinical features that allow for single-case predictions serves as an impetus to search for additional layers of NR. The work pro-gram of this Research Unit (RU) will foster the development of precision psychotherapy by i) in-vestigating clinical and bio-behavioral signatures of NR to improve our understanding of this phenomenon, ii) applying state-of-the-art machine learning technology for single-case predic-tions, and iii) validating these for clinical utility in an ecologically valid treatment setting, bring-ing together four major academic outpatient clinics in Berlin. Our effort will thus pave the way for a priori patient stratification to intensified or augmented treatments in a putative second funding period. To achieve this, we will set up a prospective-longitudinal multicenter observational study on n = 500 patients with internalizing disorders (specific phobia, social anxiety disorder, panic disorder, agoraphobia, generalized anxiety disorder, obsessive-compulsive disorder, post-traumatic stress disorder, unipolar depressive disorders) who will be deeply phenotyped prior to CBT using hypotheses-based clinical, e-mental health, psychophysiological and neuroimaging measures. Assessment batteries and treatment documentation will be harmonized across cen-ters. Predictive analytics will be provided by our methods platform, including computer vision algo-rithms such as convolutional neural networks, multiple kernel and transfer learning and an infra-structural basis (hard- and software, data management plans, high-performance computing). The RU aims to significantly improve the field by 1) setting up a multilevel and -method assessment battery to search for the best predictors, combinations thereof, and cost-efficient proxies, 2) in-vestigating bio-behavioral signatures of emotion regulation as a putative key mechanism of CBT, 3) applying a transdiagnostic focus on NR signatures, 4) within one comprehensive sample that exerts a high degree of ecological validity, thus fostering translation to clinical practice with diverse patient characteristics. These goals can only be achieved by concerted ac-tion of experts in the fields of clinical psychology, psychotherapy, e-mental health, psychophysiol-ogy, cognitive neuroscience, and neuroinformatics. We will maximize synergies with large-scale consortia (UK Biobank, ENIGMA, CRC-TRR 58, BMBF psychotherapy initiative, PING, KODAP). This RU will make substantial progress in answering the question how we can better under-stand the phenomenon of NR, identify and address this vulnerable and cost-intensive group of NR patients.

尽管认知行为疗法（CBT）是治疗内化障碍的一线疗法，但仍有相当一部分患者未能从中受益——这给患者带来了严重后果，也给社会带来了成本。精确的心理健康可以帮助在治疗开始之前就确定有无反应风险的患者。标准临床特征的缺乏使得人们能够进行单一病例的预测，这促使人们寻找更多的NR层。该研究单位（RU）的工作计划将通过以下方式促进精准心理治疗的发展：1)调查NR的临床和生物行为特征，以提高我们对这一现象的理解；2)应用最先进的机器学习技术进行单一病例的预测；iii)在生态有效的治疗环境中验证这些临床效用，将柏林的四个主要学术门诊诊所聚集在一起。因此，我们的努力将为先验的患者分层铺平道路，以便在假定的第二个资助期内加强或扩大治疗。为了实现这一目标，我们将建立一项前瞻性纵向多中心观察研究，对n = 500名患有内化障碍（特定恐惧症、社交焦虑障碍、惊恐障碍、场所恐怖症、广泛性焦虑障碍、强迫症、创伤后应激障碍、单极抑郁症）的患者进行研究，这些患者将在CBT前使用基于假设的临床、电子心理健康、心理生理学和神经影像学措施进行深度表型分析。评估电池和处理文件将在各中心协调一致。预测分析将由我们的方法平台提供，包括计算机视觉算法，如卷积神经网络，多核和迁移学习以及基础设施基础（硬件和软件，数据管理计划，高性能计算）。RU的目标是通过以下方式显著改善该领域：1)建立一个多层次和多方法的评估系统，以寻找最佳预测因子、其组合和成本效益代理；2)调查情绪调节的生物行为特征，将其作为CBT的假定关键机制；3)应用对NR特征的跨诊断关注；4)在一个具有高度生态有效性的综合样本中。从而促进翻译到临床实践与不同的病人特点。这些目标只能通过临床心理学、心理治疗、电子心理健康、心理生理学、认知神经科学和神经信息学等领域专家的协同行动来实现。我们将最大化与大型财团（UK Biobank， ENIGMA, CRC-TRR 58， BMBF心理治疗计划，PING， KODAP）的协同效应。该RU将在回答我们如何更好地理解NR现象，识别和解决这一脆弱且成本密集的NR患者群体的问题方面取得实质性进展。