FOR 5187:
Towards precision psychotherapy for non-respondent patients: From signatures to predictions to clinical utility
Subject Area
Social and Behavioural Sciences
Medicine
Term
since 2022
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 442075332
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.
DFG Programme
Research Units
Projects
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Anterior cingulate cortex-based biomarker development for cognitive-behavioral ther-apy (CBT) response prediction in internalizing disorders
(Applicants
Hilbert, Kevin
;
Walter, Henrik
)
-
Brain-electrical and cardiovascular indicators of emotion regulation as predictors of treatment (non)-response to CBT in internalizing disorders
(Applicant
Kathmann, Norbert
)
-
Coordination Funds
(Applicant
Lüken, Ulrike
)
-
Digital Phenotyping of emotion (dys-)regulation as transdiagnostic process and proxy for clinical and neurobiological markers of treatment (non-)response
(Applicant
Knaevelsrud, Christine
)
-
Dynamic causal modelling of emotion regulation as predictors of treatment (non-) response to CBT in internalizing disorders
(Applicants
Erk, Susanne
;
Heinzel, Stephan
)
-
Methods toolbox and infrastructure for predictive analytics
(Applicants
Haynes, John-Dylan
;
Ritter, Kerstin
)
-
SP1: Single-case prediction of treatment (non-) response to cognitive-behavioral therapy (CBT) in the outpatient sector: a prospective-longitudinal observational study
(Applicants
Fehm, Lydia Birgit
;
Jacobi, Frank
;
Kathmann, Norbert
;
Lüken, Ulrike
;
Renneberg, Babette
)
-
SP3: Neuroimaging backbone and large-scale data harmonization
(Applicants
Blankenburg, Felix
;
Walter, Henrik
)
-
SP9: Generalizing predictive patterns of treatment (non-) response: from specific phobia and obsessive-compulsive disorder to the anxiety spectrum
(Applicants
Kathmann, Norbert
;
Lüken, Ulrike
;
Ritter, Kerstin
)
-
Transdiagnostic psychological factors as predictors of treatment non-response and cost-effectiveness measures related to predictive analytics in psychotherapy
(Applicants
Jacobi, Frank
;
Stenzel, Nikola Maria
)