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Domain A: Description of antecedents and trajectories of losing and regaining control and modulating factors
In this domain, we will assess the trajectories of losing and regaining control and elucidate how triggers and modulating factors interact and predict when subjects with mild to moderate alcohol use disorder (AUD) lose or regain control over drug intake. We will identify common, as well as age- and gender-specific mechanisms that contribute to losing and regaining control over consumption of alcohol by systematically assessing a large cohort of persons with severity levels of AUD, who do not (yet) require detoxification. We will use Ambulatory Assessment, including Ecological Momentary Assessment, to acquire intensive longitudinal datasets over several months in AUD patients with and without additional tobacco and cannabis use. This will allow us to observe longitudinal trajectories with respect to losing and regaining control over drug intake in real life situations.
Project A01: Longitudinal monitoring of triggers and modifying factors in human addiction
The principal aim of this prospective longitudinal cohort study is to examine individual-level predictors of trajectories of losing and regaining control over alcohol intake across lifespan in real life using ambulatory assessment. We will assess individual differences in behavior, neurobiological correlates and polygenic risk scores in adolescents and young adults (N = 300; WP1) as well as early (N = 300; WP2) and late middle-aged adults (N = 300; WP3) with alcohol use disorder (AUD) over the course of one year. This will allow us to identify individual-level differences (using latent growth curve models) and profiles (using latent growth mixture models) that predict regaining or losing control, which will provide critical input for novel prevention and intervention strategies.
Prof. Dr. Dr. Tobias Banaschewski, Medical Faculty Mannheim
Prof. Dr. Dr. Andreas Heinz, Charité - Universitätsmedizin Berlin
Prof. Dr. Dr. Michael Rapp, University of Potsdam
Friederike Deeken, University of Potsdam
Dr. Patricia Pelz, Charité - Universitätsmedizin Berlin
Friederike Wedemeyer, Charité - Universitätsmedizin Berlin
Julia Wenzel, Charité - Universitätsmedizin Berlin
Project A02: Longitudinal monitoring of cognitive control as a modifying factor of drinking behavior
The implicit assumption that intra-individual changes in cognitive control and decision-making are reflected by changes in real-life drinking behavior remains largely untested. We aim to identify differential cognitive trajectories related to losing and regaining control over everyday drinking behavior. We will investigate this by using a smartphone application for the longitudinal ambulatory assessment of cognitive control and decision-making.
Prof. Dr. Lorenz Deserno, Universitätsklinikum Würzburg
Prof. Dr. Michael Smolka, Technische Universität Dresden
Ying Lee, Technische Universität Dresden
Dr. Hilmar Zech, Technische Universität Dresden
Project A03: Stress-related predictor profiles for craving and relapse in human addiction
This project will investigate stress- and alcohol cue-related physiological markers in a lab experiment to assess interactions between acute psychological vs. physical stress exposure and alcohol cue-exposure regarding their effects on (1) alcohol craving and related markers, (2) their predictive capacity for future alcohol intake in a real-life setting, and (3) the identification of their neural correlates in brain circuits of motivational, cognitive, and affective processing. Our long-term aim is the definition of a setup of mobile sensors and their integration in a mobile infrastructure that allows the prediction of stress related alcohol intake in an ambulatory setting.
Prof. Dr. Falk Kiefer, Zentralinstitut für Seelische Gesundheit Mannheim
Prof. Dr. Clemens Kirschbaum, Technische Universität Dresden
Prof. Dr. Jan Stallkamp, Fraunhofer Institute for Manufacturing Engineering and Automation, Mannheim
Dr. Patrick Bach, Zentralinstitut für Seelische Gesundheit Mannheim
Dr. Jens Langejürgen, Fraunhofer Institute for Manufacturing Engineering and Automation
Philipp Radler, Fraunhofer Institute for Manufacturing Engineering and Automation
Prof. Dr. Sabine Vollstädt-Klein, Zentralinstitut für Seelische Gesundheit Mannheim
Judith Zaiser, Zentralinstitut für Seelische Gesundheit Mannheim
Sina Zimmermann, Zentralinstitut für Seelische Gesundheit Mannheim
Project A04: Intense characterization of triggers, modifying factors and mechanisms at course transition points in human addiction
The project will use ambulatory assessment and functional neuroimaging to identify triggers, modifying factors and intermediate neural mechanisms in alcohol use disorder (AUD) in course phases with a substantial increase or decrease in alcohol consumption, respectively. We will assess temporally highly resolved ambulatory assessment for six weeks to identify intraindividual differences in in the momentary effects of triggers and modifying factors on subjective craving, mood, impulsivity and alcohol consumption. We will further identify their neural correlates in brain circuits relevant for cognitive, affective and motivational processing and test the predictive value of the identified mechanisms for subsequent disease trajectories to critically inform novel prevention and intervention strategies.
Prof. Dr. Ulrich Ebner-Priemer, Karlsruhe Institute of Technology
Prof. Dr. Christine Heim, Charité – Universitätsmedizin Berlin
Prof. Dr. Dr. Heike Tost, Zentralinstitut für Seelische Gesundheit Mannheim
Dr. Gabriela Gan, Zentralinstitut für Seelische Gesundheit Mannheim
Sarah Lohr, Zentralinstitut für Seelische Gesundheit Mannheim
Ren Ma, Zentralinstitut für Seelische Gesundheit Mannheim
Mirjam Melzer, Zentralinstitut für Seelische Gesundheit Mannheim
Project A05: Intensive behavioral monitoring and dynamical state transitions in animal models of addiction
Using novel approaches established in statistical physics and dynamical systems, we will investigate whether early warning signs that are indicative of critical state transitions into addictive behavior can be detected by intensive longitudinal data (ILD) derived from a rat model for alcohol addiction and from a long-term nicotine self-administration model.
PD Dr. Dr. Hamid Noori, Max Planck Institute for Biological Cybernetics Tübingen, Zentralinstitut für Seelische Gesundheit Mannheim
Prof. Dr. Rainer Spanagel, Zentralinstitut für Seelische Gesundheit Mannheim
Project A06: AI-based predictive neuro-behavioral modeling of individual trajectories in addiction
Our project aims at inferring subject-level recurrent neural network (RNN) models of behavioral dynamics from multi-modal mobile data. We will develop methods that integrate data obtained from different modalities (such as EMA, accelerometer etc.) and follow distinct probability distributions and sampling rates. This framework will be applied to data from A01-A04 to identify and predict dynamical transitions between regaining & losing control, predict long-term trajectories, and identify subgroups based on inferred RNN parameters. Lastly, we aim at identifying crucial factors and drivers behind these transitions, as well as mechanisms governing the behavioral dynamics.
Prof. Dr. rer. nat. Daniel Durstewitz, Zentralinstitut für Seelische Gesundheit Mannheim
Dr. sc. hum. Georgia Koppe, Zentralinstitut für Seelische Gesundheit Mannheim
Project A07: Deep Learning for identifying subtypes in addiction based on structural MRI data
In this subproject, we will develop explainable deep learning approaches in order to systematically investigate structural MRI brain patterns with respect to sociodemographic variables, disease diagnosis, previous and later alcohol consumption as well as potential disease subtypes. In particular, we will build convolutional neural network (CNN) architectures for structural MRI data in addiction and test them for (1) making a differential diagnosis between AUD patients and healthy controls (2) prediction of previous and future alcohol use (3) identifying AUD subtypes and (4) characterization of different aspects of mental health. Furthermore, we will visualize individual network decisions and integrate CNN models with other one-shot data acquired in the TRR 265, including sociodemographic and psychometric assessments as well as newly derived behavioral and fMRI biomarkers.
Prof. Dr. Kerstin Ritter, Charité - Universitätsmedizin Berlin