Reinforcement learning as an intermediate phenotype in psychosis? Deficits sensitive to illness stage but not associated with polygenic risk of schizophrenia in the general population

Abstract

Background: Schizophrenia is a complex disorder in which the causal relations between risk genes and observed clinical symptoms are not well understood and the explanatory gap is too wide to be clarified without considering an intermediary level. Thus, we aimed to test the hypothesis of a pathway from molecular polygenic influence to clinical presentation occurring via deficits in reinforcement learning.

Methods: We administered a reinforcement learning task (Go/NoGo) that measures reinforcement learning and the effect of Pavlovian bias on decision making. We modelled the behavioural data with a hierarchical Bayesian approach (hBayesDM) to decompose task performance into its underlying learning mechanisms. Study 1 included controls (n = 29, F|M = 0.81), At Risk Mental State for psychosis (ARMS, n = 23, F|M = 0.35) and FEP (First-episode psychosis, n = 26, F|M = 0.18). Study 2 included healthy adolescents (n = 735, F|M = 1.06), 390 of whom had their polygenic risk scores for schizophrenia (PRSs) calculated.

Results: Patients with FEP showed significant impairments in overriding Pavlovian conflict, a lower learning rate and a lower sensitivity to both reward and punishment. Less widespread deficits were observed in ARMS. PRSs did not significantly predict performance on the task in the general population, which only partially correlated with measures of psychopathology.

Conclusions: Reinforcement learning deficits are observed in first episode psychosis and, to some extent, in those at clinical risk for psychosis, and were not predicted by molecular genetic risk for schizophrenia in healthy individuals. The study does not support the role of reinforcement learning as an intermediate phenotype in psychosis.

Keywords: Bayesian; Computational psychiatry; Go/NoGo task; PRS; Psychosis; Reinforcement learning; Schizophrenia.

Fig. 1

Experimental paradigm schematic. Figure adapted from Guitart-Masip et al. (2012) and Moutoussis et al. (2018). Top-right figure shows a graphical representation of the four conditions of the modified Go/NoGo task crossing valence (y-axis) and action (x-axis). Yellow stars mark the Pavlovian congruent conditions, while the other two are the Pavlovian incongruent ones.

Fig. 2

Group differences in overall performance (percent for best outcome) on the four GNG (Go/NoGo) conditions. Controls n = 29, ARMS (At-Risk for Mental Health) n = 23 and FEP (First-episode psychosis) n = 26. Error bars indicate standard error of the mean. Stars indicate significant t-test group differences at p < 0.05 after ANOVA testing.

Fig. 3

ANOVA analysis of group differences of modelled parameters. Controls, ARMS (At-Risk for Mental Health) and FEP (First-episode psychosis). For Go Bias and Pavlovian Bias, values > 0 indicate the presence of such bias, those <0 indicate the opposite. Horizontal back bar = Median; mean = grey circle. Whiskers indicate the interquartile range and the cloud plot shows the probability distribution of the data. Significant results from the Bonferroni or Games-Howell corrected post-hoc analyses are shown after outliers removal (*p < 0.05, **p < 0.01, *** < 0.001).