FOR 5187/1: Towards precision psychotherapy for non-respondent patients: from signatures to predictions to clinical utility (coordination fonds)

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 program of this Research Unit (RU) will foster the development of precision psychotherapy by i) investigating 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, bringing 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-trau-matic 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 al-gorithms such as convolutional neural networks, multiple kernel and transfer learning and an in-frastructural 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 assess-ment battery to search for the best predictors, combinations thereof, and cost-efficient proxies, 2) investigating 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 action of experts in the fields of clinical psychology, psychotherapy, e-mental health, psychophys-iology, cognitive neuroscience, and neuroinformatics. We will maximize synergies with large-scale consortia (UK Biobank, ENIGMA, CRC-TRR 58, BMBF psychotherapy initiative, PING, KO-DAP). This RU will make substantial progress in answering the question how we can better understand the phenomenon of NR, identify and address this vulnerable and cost-inten-sive group of NR patients.