Povilas Karvelis, PhD
Computational modelling of vulnerability to suicidality
Suicide is a global phenomenon and occurs throughout the lifespan. Currently, psychiatric practice lacks reliable predictive tools and a sufficiently detailed mechanistic understanding of suicidality to provide timely and personalized interventions. Furthermore, the prevailing theories of suicidality are specified verbally and rely on intercorrelated constructs, which limits their predictive power and makes it hard to corroborate or disconfirm the theories. To address these problems, we are developing a model of vulnerability to suicidality grounded in normative theories of learning and decision making in computational neuroscience. Our approach integrates across behavioral, cognitive, and neural levels of analysis and allows for the formalization of our understanding of suicidality. In research context such a model serves as a vehicle for the generation of testable hypotheses across different levels, while in the clinic it can serve as a diagnostic tool to guide personalized interventions. Our work starts with model simulations, which establish face validity of the model. It then proceeds with applying the model to analyze behavioral and neural data in suicidality population to establish construct validity and to refine the model. Finally, the predictive validity and clinical utility of the model are established in cross-sectional prospective studies.
Recent Publications:
Karvelis, P., & Diaconescu, A. O. (2022). A Computational Model of Hopelessness and Active-Escape Bias in Suicidality. Computational Psychiatry, 6(1), 34–59. DOI: http://doi.org/10.5334/cpsy.80
Karvelis, P. *, Charlton, C. E. *, Allohverdi, S. G., Bedford, P., Hauke, D. J., Diaconescu, A. O. Computational Approaches to Treatment Response Prediction in Major Depression Using Brain Activity and Behavioral Data: A Systematic Review. Network Neuroscience 2022; doi: https://doi.org/10.1162/netn_a_00233
Richards, K. L.*, Karvelis, P. *, Lawrie, S. M., & Seriès, P. (2020). Visual statistical learning and integration of perceptual priors are intact in attention deficit hyperactivity disorder. PloS one, 15(12), e0243100.
Valton, V. *, Karvelis, P. *, Richards, K. L., Seitz, A. R., Lawrie, S. M., & Series, P. (2019). Acquisition of visual priors and induced hallucinations in chronic schizophrenia. Brain, 142(8), 2523-2537.
Karvelis, P., Seitz, A. R., Lawrie, S. M., & Series, P. (2018). Autistic traits, but not schizotypy, predict increased weighting of sensory information in Bayesian visual integration. eLife, 7, e34115.
[*: joint first authors]
A detailed publication list can be accessed here.
email: povilas.karvelis@camh.ca
