Andreea O. Diaconescu, PhD
Generative models combining Reward Associations and Social learning for Psychosis Prevention (GRASP)
Psychotic disorders such as schizophrenia are among the world’s leading causes of disability. Early identification is critical to improve outcomes in psychosis. There is a growing interest in computational models focused on aberrant learning to contribute to early identification, as cognitive impairments are a core feature of the illness and associated with poor functional outcome. We propose new avenues for translation provided by the integration of cognitive tasks deployed on mobile devices or as immersive Virtual Reality games and computational models of behaviour focusing on reward and social learning, in order to identify persecutory ideation and the emergence of delusions. GRASP is applied in longitudinal studies with individuals at risk of psychosis.
Related Publications:
Diaconescu, A. O. *, Wellstein, K. V. *, Kasper, L., Mathys, C. D., & Stephan, K. E. (2020). Hierarchical Bayesian models of social inference for probing persecutory delusional ideation. Journal of Abnormal Psychology Special Issue “Predictive Coding and Psychopathology”, 129(6): 556-569.
Cole, D. M. *, Diaconescu, A. O. *, Pfeiffer, U. J., Brodersen, K. A., Mathys, C. D., Julkowski, D., Ruhrmann, S., Schilbach, L., Tittgemeyer, M., Vogeley, K., & Stephan, K. E. (2020). Atypical processing of uncertainty in individuals at risk for psychosis. Neuroimage Clinical, 26:102239.
Wellstein, K. V.*, Diaconescu, A. O. *, Bischof, M., Rüesch Ranganadan, A., Paolini, G., Aponte, E., Ullrich, J., & Stephan, K. E. (2019). Inflexible social inference in individuals with subclinical persecutory delusional tendencies. Schizophrenia Research, 251, 344-351.
Diaconescu, A. O., Hauke, D. J., & Borgwardt, S. (2019). Models of persecutory delusions: a mechanistic insight into the early stages of psychosis. Molecular Psychiatry: 1.
The neurocomputational underpinnings of ketamine’s rapid antisuicidal effects
The World Health Organization (WHO) estimates that one person dies by suicide every 40 seconds summing up to a total of 800,000 deaths by suicide every year. The majority of annual suicides are estimated to occur within a depressive episode, meaning that major depressive disorder (MDD) patients are 20-fold more likely to die by suicide than the general population. Ketamine - an N-methyl-D-aspartate receptor (NMDAR) antagonist - has one of the most potent and rapid antisuicidal effects currently reported, significantly reducing suicidal thoughts and behaviours within hours of infusion. However, there are several challenges: First, it remains largely unknown why ketamine reduces suicidality. Furthermore, because individual treatment predictions are scarce, it is impossible to know in advance who will show rapid improvements and who will require multiple doses to sustain these therapeutic effects. We propose neurocomputational models applied in the context of perceptual learning tasks to address this challenge, and predict who will benefit from ketamine intervention. Since these models are mechanistic, they can provide insights into the drug’s antisuicidal effects.
Related Publications:
Weber, L. A. E.*, Diaconescu, A. O. *, Mathys, C. D., Schmidt, A., Kometer, M., Vollenweider, F., & Stephan, K. S. (2020). Ketamine impacts on hierarchical prediction errors: A computational single-trial analysis of the auditory mismatch. Journal of Neuroscience, 40(29): 5658-5668.
Schmidt, A.*, Diaconescu, A. O. *, Kometer, M., Friston, K. J., Stephan, K. E., & Vollenweider, F. X. (2013). Modeling Ketamine Effects on Synaptic Plasticity During the Mismatch Negativity. Cerebral Cortex. 23(10): 2394-2406.
[*: joint first authors]
A detailed publication list can be accessed here.
email: andreea.diaconescu@camh.ca
