Schmack lab
Neural Circuits and Immunity in Psychosis Laboratory
:
Computational psychiatry
Content
We work on a cross-species approach to define computational-behavioural markers that predict how psychosis patients respond to treatment. Our rationale is that a computational theory of how the brain generates behaviour is crucial for successful translation between humans and mice.
Psychosis is exclusively defined by subjective symptoms such as hallucinations. Because psychotic experiences are challenging to measure in model organisms, the underlying biological mechanisms remain poorly understood. As a result, there are currently no mechanistic biomarkers for psychosis that can inform clinical practice.
We recently developed a computational-behavioural approach to measure psychosis in mice and humans, and identified two biological mechanisms that could explain psychotic experiences (see Figure). We are now applying this computational-behavioural approach to psychosis patients.
We adapt our quantitative behavioural tasks for use in psychosis patients, and collect data using in-person experiments as well as smartphone-based measures. Theory-driven computational modelling allows us to map fine-grained behavioural phenotypes of psychosis onto biological mechanisms in mice. The goal is to identify biologically-defined subgroups of patients that respond differentially to existing and future treatments.
Figure
Computational-behavioural approach to measure psychosis across species.
In humans and mice (middle panel), a behavioral task models hallucinations as high-confidence false percepts. Computational modeling (not shown) suggests that the behavior is driven by comparable mental computations in humans and mice. In humans (left panel), such hallucination-like percepts are correlated with self-reported hallucinations. In mice (right panel), hallucination-like percepts are mediated by striatal dopamine.
Reproduced with permission from AAAS from Schmack et al., Science 2021.
