A cohort of 585 patients with internalising disorders is being recruited to investigate to cognitive-behavioural therapy.
Less than 50% of patients with internalising disorders achieve clinically meaningful improvement after cognitive-behavioural therapy.
The study aims to identify bio-behavioural signatures associated with treatment non-response (TNR).
Emotion regulation is considered a key mechanism in both cognitive-behavioural therapy and treatment non-response.
State-of-the-art machine learning methods will be utilized for single-patient predictions based on high-dimensional neuroimaging data.
The research seeks to enhance precision psychotherapy by establishing optimal predictors of treatment outcomes.
Simplified
INTRODUCTION: Cognitive-behavioural therapy () 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 (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.
Key numbers
585 patients
Cohort Size
Patients with internalizing disorders recruited across four outpatient clinics.
75%
Prediction Accuracy Goal
Targeted accuracy for predicting using machine learning.
50%
Rate
Less than half of patients with internalizing disorders show clinically meaningful improvement with .
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Competing interests: KH is a scientific advisor and received virtual stock options of Mental Tech, which develops an AI-based chatbot providing mental health support. All other authors have no completing interest to declare.
