Atypical processing of uncertainty in individuals at risk for psychosis
David M. Cole,
Andreea O. Diaconescu,
Ulrich J Pfeiffer,
Kay H Brodersen,
Christoph D Mathys,
Klaas E Stephan
Posted 07 Oct 2019
bioRxiv DOI: 10.1101/796300 (published DOI: 10.1016/j.nicl.2020.102239)
Posted 07 Oct 2019
Background Current theories of psychosis highlight the role of abnormal learning signals, i.e., prediction errors (PEs) and uncertainty, in the formation of delusional beliefs. We employed computational analyses of behaviour and functional magnetic resonance imaging (fMRI) to examine whether such abnormalities are evident in at-risk mental state (ARMS) individuals. Methods Non-medicated ARMS individuals ( n =13) and control participants ( n =13) performed a probabilistic learning paradigm during fMRI data acquisition. We used a hierarchical Bayesian model to infer subject-specific computations from behaviour – with a focus on PEs and uncertainty (or its inverse, precision) at different levels, including environmental ‘volatility’ – and used these computational quantities for analyses of fMRI data. Results Computational modelling of ARMS individuals’ behaviour indicated volatility estimates converged to significantly higher levels than in controls. Model-based fMRI demonstrated increased activity in prefrontal and insular regions of ARMS individuals in response to precision-weighted low-level outcome PEs, while activations of prefrontal, orbitofrontal and anterior insula cortex by higher-level PEs (that serve to update volatility estimates) were reduced. Additionally, prefrontal cortical activity in response to outcome PEs in ARMS was negatively associated with clinical measures of global functioning. Conclusions Our results suggest a multi-faceted learning abnormality in ARMS individuals under conditions of environmental uncertainty, comprising higher levels of volatility estimates combined with reduced cortical activation, and abnormally high activations in prefrontal and insular areas by precision-weighted outcome PEs. This atypical representation of high- and low-level learning signals might reflect a predisposition to delusion formation.
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