The Applied Computational Psychiatry lab focuses on developing computational tools with real potential for clinical applications.
Computational Psychiatry is a multidisciplinary field of research at the intersection of psychiatry, neuroscience, machine learning and statistics. The aim of the field is to harness advances in these fields to advance treatments for mental illnesses.
We are part of the Divison of Psychiatry and the Max Planck UCL Centre for Computational Psychiatry and Ageing Research in the Institute of Neurology at University College London.
For overviews over computational psychiatry, see Huys et al., 2016 Nature Neuroscience and Huys et al. 2020 Neuropsychopharmacology
When an organ is unable to meet the demands placed on it, illness can arise. As the main functions of the brain are to compute and learn, an understanding of mental illnesses will benefit from an understanding of the computational and learning functions the brain performs, and how these are affected in states of ill-health.
Latest Peer-Reviewed Original Publications
Susceptibility to interference between Pavlovian and instrumental control predisposes risky alcohol use developmental trajectory from ages 18 to 24
We examined if susceptibility to interference between Pavlovian and instrumental control assessed via a PIT task predicts drinking trajectories until age 24. Overall, it might be viewed as a predisposing mechanism towards hazardous alcohol use during young adulthood, and the identified high-risk group may profit from targeted interventions.
Elevated amygdala responses during de-novo Pavlovian conditioning in alcohol-use disorder are associated with Pavlovian-to-Instrumental transfer and relapse latency
Cue-reactivity research has demonstrated the power of alcohol-associated cues to activate the brain’s reward system, which has been linked to craving and subsequent relapse. However, whether de-novo Pavlovian conditioning is altered in alcohol use disorder (AUD) has been rarely investigated. We provide evidence of altered neural correlates of de-novo Pavlovian conditioning in patients with AUD, especially for appetitive stimuli. Thus, heightened processing of Pavlovian cues might constitute a behaviorally relevant mechanism in alcohol addiction.
Latest Preprints
Different components of cognitive-behavioural therapy affect specific cognitive mechanisms
Understanding the mechanisms by which psychotherapies for common mental health problems work is a promising route to improving treatment efficacy and treatment personalization.Here, we found that a goal-setting intervention, based on behavioural activation therapy, reliably and selectively reduced sensitivity to effort when deciding how to act to gain reward. By contrast, a cognitive restructuring intervention based on cognitive therapy, reliably and selectively reduced the tendency to attribute negative everyday events to self-related causes.
The selective serotonin reuptake inhibitor sertraline alters learning from aversive reinforcements in patients with depression: evidence from a randomized controlled trial
To understand the causal links between antidepressants, cognitive effects and improvements in clinical symptoms, evaluations within randomized clinical trials (RCTs) comparing antidepressants to placebo are required. This study examined whether reinforcement learning processes mediate the treatment effect of the SSRI sertraline in the PANDA trial using the affective Go/NoGo task. Specific reinforcement learning mechanisms did show a relationship to aspects of depression and anxiety and its treatment with sertraline, but this was weak and not as hypothesized a priori. Poor task performance limited interpretability and generalizability of the findings and highlighted the importance of developing acceptable and reliable tasks for clinical studies.