Understanding the Mechanisms Of Psychedelic Therapy Using Computational Modelling

Psychedelics like psilocybin and lysergic acid diethylamide (LSD) have emerged as promising treatment candidates for a variety of mental illnesses, including alcohol and tobacco dependence, post-traumatic stress disorder (PTSD), and major depression. Despite the excitement elicited by the possibility of psychedelic therapy, predicting who will benefit from it remains a challenge. In both clinical and healthy populations, psychedelic consumption has been accompanied by improvements in mood, and sometimes lasting changes in beliefs, including increased cognitive flexibility. However, it remains largely unknown which aspect of learning or belief formation is affected by serotonergic 5-HT2A receptor agonists like psilocybin and LSD. Thus, more research examining the computational and neural mechanisms underlying the therapeutic effects of psychedelics is needed.

This Research Topic aims to highlight research examining the computational and neural mechanisms of psychedelics like psilocybin and LSD, in particular for the treatment of mental illness. We are also interested in research that identifies computational or neural markers that could be leveraged to predict subjective effects of psychedelics, which may be associated with subsequent treatment responses.

We are particularly interested in submissions that focus on treatment response prediction, treatment guidance, treatment and risk monitoring of psychedelic therapy.

At the technology/approach level, we are particularly interested in the following methods:

(i) computational models of behavior (including reinforcement learning and Bayesian models)(ii) noninvasive measures of brain activity including neuroimaging and electroencephalography, (iii) data- and theory-driven computational models of brain activity.