Research 2025
– funded projects
Establishment of a Scalable Method for Developing Biomarkers for Alcohol Use Disorder Using Artificial Intelligence-Generated Synthetic Task-Based Brain Activations
Prof. Dr. Henrik Walter
Research Division of Mind and Brain Research Department of Psychiatry and Psychotherapy Charité – Universitätsmedizin Berlin
henrik.walter@charite.de
Abstract
Alcohol use disorder (AUD) is a significant global mental health challenge, leading to increased morbidity and mortality worldwide. While task-based functional magnetic resonance imaging (tb-fMRI) studies have shown promise in elucidating its neural mechanisms and establishing biomarkers, scaling up these studies is challenging due to cognitive demands and the need for extensive subject training to perform the cognitive task. To address this, we have developed an innovative artificial neural network-based approach, DeepTaskGen, which generates synthetic non-acquired task-based brain activations from resting-state brain activity (rs-fMRI), which is relatively simpler to acquire. We have already extensively validated our approach on multiple large-scale datasets comprising over 20,000 healthy individuals. However, further validation on clinical samples is essential and urgently required. This project aims to apply our innovative approach to AUD, enabling a large-scale study of AUD-related task-based biomarkers without labor-intensive experimental tasks. This would drastically reduce costs for brain image acquisition and revive datasets lacking task-based brain images. Specifically, we will adapt our approach to a large AUD sample (TRR265) with available task-based and resting-state images. This will allow us to compare the predictive performance of acquired and synthetic tb-fMRI biomarkers. For validation, we will generate synthetic task-based brain images in a separate sample of severely affected AUD patients (FOR1617) to assess generalizability. This project will provide significant evidence of our approach’s potential to enhance the predictive power of neural biomarkers associated with AUD and foster practical clinical applications.
Lay summary
This project seeks to advance the study of alcohol use disorder (AUD) by applying artificial intelligence to generate synthetic brain activity. This innovative approach eliminates the need for effortful cognitive tasks, facilitating the identification of AUD-related neural processes on a large scale, which is required to develop effective diagnosis and treatment.
Let’s drink on it? The overlooked neuromotivational impact of positive affect on alcohol use and addiction
Dr Niklas Ihssen
Associate Professor Department of Psychology Durham University, UK
niklas.ihssen@durham.ac.uk
Abstract
Decisions to drink alcohol are always embedded in a complex emotional-motivational context, both in healthy individuals and those with alcohol use disorders. Theories of alcohol use and misuse have long been arguing that it is predominantly negative affect, evoked by stress and aversive life events, that increases the likelihood of alcohol intake and the risk of alcohol addiction through emotion regulation or self-medication mechanisms. However, recent meta-analytical work (Dora et al., 2023) challenges the decade-old theoretical premise and point towards positive affect (PA) being an equal or even stronger factor in triggering drinking. Similar findings begin to emerge for the link between PA and other reward-seeking behaviours. However, it is currently unknown why PA increases drinking and how this could be prevented. The present pilot project sets out to develop a framework that provides a first glimpse into possible mechanisms behind this phenomenon. This will include contrasting a top-down explanation (achievement-related ‘celebratory drinking’ or self-licencing) with a bottom-up account (PA priming the dopaminergic reward system). The project will apply data modelling to granular drinking decisions in a controlled, naturalistic bar lab before piloting the neural underpinnings of PA-induced drinking with fMRI. Findings from the project will inform the development of behavioural tools to weaken the association between PA and drinking, especially in those on the pathway to addiction.
Lay Summary
Research shows people drink more alcohol after experiencing positive feelings. It is unclear why, and how this relates to problematic drinking. This project addresses this gap by measuring emotions and drinking in a bar lab and brain scanner. This will help develop tools to tackle harmful drinking after positive feelings.
A lab-based model of cue reactivity in opioid use disorder
Advanced Research Fellow, Addiction Research Group Division of Psychiatry Department of Brain Sciences Imperial College
l.paterson@imperial.ac.uk
Scientific abstract
Opioid use disorder (OUD) remains a critical public health challenge, with high rates of relapse and an urgent need for more effective treatment options. This proposal sets out to address this need by implementing a laboratory-based cue reactivity (CR) paradigm, using heroin cue-reactivity to reliably elicit robust craving responses within a controlled environment. Such paradigms can thus elicit CR signatures that are susceptible to attenuation by effective treatments, providing an ideal model to test new interventions. The ultimate aim is to develop a platform to test proof-of-concept effectiveness of novel promising treatments, including acute pharmacological challenges, such as serotonergic compounds with therapeutic potential (e.g. psychedelics under clinical investigation), and innovative non-invasive neurostimulation techniques such as temporal interference.
This research builds on fMRI data from our recently completed NCORE neuroimaging study which demonstrated robust brain activation during heroin cue-reactivity in methadone-maintained OUD. Self-reported craving increased post-CR (versus pre-CR) indicating the task is effective. The model could be strengthened further by addition of other evidence-based physiological markers of cue reactivity, notably those that are known indices of autonomic function, attention and arousal. Including multiple components of the CR signature within a single platform would enhance the sensitivity of the model, capturing more subtle changes that may occur even when self-reported craving is not overtly expressed.
This can be achieved out-of-scanner by integrating multi-channel physiological recordings during simultaneous CR stimulus presentation. Such a model would be of huge value since it would be more accessible and scalable for rapid testing of new treatments than fMRI paradigms alone, which effectively probe brain mechanisms, but are limited by cost and practicality issues.
In this pilot study, n=10 participants with OUD will be exposed to the validated heroin CR paradigm. We will explore neural and peripheral physiological metrics such as pupillometry, eye-tracking, galvanic skin responses, salivary cortisol and heart rate alongside self-report measures. We will additionally explore integration of 32-channel EEG, providing high temporal resolution to complement the fMRI-derived spatial metrics, adding considerable neuroimaging and mechanistic insights.
By integrating diverse physiological and neural measures, this project aims to gain novel mechanistic insights, that may help inform and accelerate the development of effective, scalable treatments to support recovery and relapse prevention.
Lay summary
This study aims to create a lab-based model of craving in people with opioid addiction. By presenting heroin-related cues in a controlled setting (known as cue reactivity), we can reliably trigger craving responses and study them using a range of brain and body-based measurements. This platform will be used to test novel treatments for OUD, including new potential medications and non-invasive neurostimulation techniques for relapse prevention and supporting long-term recovery.
