dc.contributor.author
Langhammer, Till
dc.contributor.author
Unterfeld, Chantal
dc.contributor.author
Blankenburg, Felix
dc.contributor.author
Erk, Susanne
dc.contributor.author
Fehm, Lydia
dc.contributor.author
Haynes, John-Dylan
dc.contributor.author
Heinzel, Stephan
dc.contributor.author
Hilbert, Kevin
dc.contributor.author
Jacobi, Frank
dc.contributor.author
Kathmann, Norbert
dc.contributor.author
Knaevelsrud, Christine
dc.contributor.author
Renneberg, Babette
dc.contributor.author
Ritter, Kerstin
dc.contributor.author
Stenzel, Nikola
dc.contributor.author
Walter, Henrik
dc.contributor.author
Lueken, Ulrike
dc.date.accessioned
2025-05-12T05:07:05Z
dc.date.available
2025-05-12T05:07:05Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/47601
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-47319
dc.description.abstract
Introduction
Cognitive–behavioural therapy (CBT) works—but not equally well for all patients. Less than 50% of patients with internalising disorders achieve clinically meaningful improvement, with negative consequences for patients and healthcare systems. The research unit (RU) 5187 seeks to improve this situation by an in-depth investigation of the phenomenon of treatment non-response (TNR) to CBT. We aim to identify bio-behavioural signatures associated with TNR, develop predictive models applicable to individual patients and enhance the utility of predictive analytics by collecting a naturalistic cohort with high ecological validity for the outpatient sector.
Methods and analysis
The RU is composed of nine subprojects (SPs), spanning from clinical, machine learning and neuroimaging science and service projects to particular research questions on psychological, electrophysiological/autonomic, digital and neural signatures of TNR. The clinical study SP 1 comprises a four-centre, prospective-longitudinal observational trial where we recruit a cohort of 585 patients with a wide range of internalising disorders (specific phobia, social anxiety disorder, panic disorder, agoraphobia, generalised anxiety disorder, obsessive–compulsive disorder, post-traumatic stress disorder, and unipolar depressive disorders) using minimal exclusion criteria. Our experimental focus lies on emotion (dys)-regulation as a putative key mechanism of CBT and TNR. We use state-of-the-art machine learning methods to achieve single-patient predictions, incorporating pretrained convolutional neural networks for high-dimensional neuroimaging data and multiple kernel learning to integrate information from various modalities. The RU aims to advance precision psychotherapy by identifying emotion regulation-based biobehavioural markers of TNR, setting up a multilevel assessment for optimal predictors and using an ecologically valid sample to apply findings in diverse clinical settings, thereby addressing the needs of vulnerable patients.
Ethics and dissemination
The study has received ethical approval from the Institutional Ethics Committee of the Department of Psychology at Humboldt-Universität zu Berlin (approval no. 2021-01) and the Ethics Committee of Charité-Universitätsmedizin Berlin (approval no. EA1/186/22). Results will be disseminated through peer-reviewed journals and presentations at national and international conferences. Deidentified data and analysis scripts will be made available to researchers within the RU via a secure server, in line with ethical guidelines and participant consent. In compliance with European and German data protection regulations, patient data will not be publicly available through open science frameworks but may be shared with external researchers on reasonable request and under appropriate data protection agreements.
Trial registration number
DRKS00030915.
en
dc.format.extent
17 Seiten
dc.rights
This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
dc.rights.uri
https://creativecommons.org/licenses/by-nc/4.0/
dc.subject
Machine Learning
en
dc.subject
MENTAL HEALTH
en
dc.subject
PUBLIC HEALTH
en
dc.subject.ddc
100 Philosophie und Psychologie::150 Psychologie::150 Psychologie
dc.title
Design and methods of the research unit 5187 PREACT (towards precision psychotherapy for non-respondent patients: from signatures to predictions to clinical utility) – a study protocol for a multicentre observational study in outpatient clinics
dc.type
Wissenschaftlicher Artikel
dc.date.updated
2025-05-06T09:19:31Z
dcterms.bibliographicCitation.articlenumber
40010810
dcterms.bibliographicCitation.doi
10.1136/bmjopen-2024-094110
dcterms.bibliographicCitation.journaltitle
BMJ Open
dcterms.bibliographicCitation.number
2
dcterms.bibliographicCitation.volume
15
dcterms.bibliographicCitation.url
https://doi.org/10.1136/bmjopen-2024-094110
refubium.affiliation
Erziehungswissenschaft und Psychologie
refubium.affiliation.other
Arbeitsbereich Neurocomputation and Neuroimaging Unit

refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.isPartOf.eissn
2044-6055
refubium.resourceType.provider
DeepGreen