Depression is characterized by abnormalities in emotional processing, but the specific drivers of such emotional abnormalities are unknown. Computational work indicates that both surprising outcomes (prediction errors; PEs) and outcomes (values) themselves drive emotional responses, but neither has been consistently linked to affective disturbances in depression. As a result, the computational mechanisms driving emotional abnormalities in depression remain unknown.

Here, in 687 individuals, one-third of whom qualify as depressed via a standard self-report measure (the PHQ-9), we use high-stakes, naturalistic events – the reveal of midterm exam grades – to test whether individuals with heightened depression display a specific reduction in emotional response to positive PEs.

Using Bayesian mixed effects models, we find that individuals with heightened depression do not affectively benefit from surprising, good outcomes – that is, they display reduced affective responses to positive PEs. These results were highly specific: effects were not observed to negative PEs, value signals (grades), and were not related to generalized anxiety. This suggests that the computational drivers of abnormalities in emotion in depression may be specifically due to positive PE-based emotional responding.

Affective abnormalities are core depression symptoms, but the computational mechanisms underlying such differences are unknown. This work suggests that blunted affective reactions to positive PEs are likely mechanistic drivers of emotional dysregulation in depression.

Symptoms of serious mental illness are multidimensional and often interact in complex ways. Generative models offer value in elucidating the underlying relationships that characterise these networks of symptoms.

In this paper we use generative models to find unique interactions of schizophrenia symptoms as experienced on a moment-by-moment basis.

Self-reported mood, anxiety and psychosis symptoms, self-reported measurements of sleep quality and social function, cognitive assessment, and smartphone touch screen data from two assessments modelled after the Trail Making A and B tests were collected with a digital phenotyping app for 47 patients in active treatment for schizophrenia over a 90-day period. Patients were retrospectively divided up into various non-exclusive subgroups based on measurements of depression, anxiety, sleep duration, cognition and psychosis symptoms taken in the clinic. Associated transition probabilities for the patient cohort and for the clinical subgroups were calculated using state transitions between adjacent 3-day timesteps of pairwise survey domains.

The three highest probabilities for associated transitions across all patients were anxiety-inducing mood (0.357, P < 0.001), psychosis-inducing mood (0.276, P < 0.001), and anxiety-inducing poor sleep (0.268, P < 0.001). These transition probabilities were compared against a validation set of 17 patients from a pilot study, and no significant differences were found. Unique symptom networks were found for clinical subgroups.

Using a generative model using digital phenotyping data, we show that certain symptoms of schizophrenia may play a role in elevating other schizophrenia symptoms in future timesteps. Symptom networks show that it is feasible to create clinically interpretable models that reflect the unique symptom interactions of psychosis-spectrum illness. These results offer a framework for researchers capturing temporal dynamics, for clinicians seeking to move towards preventative care, and for patients to better understand their lived experience.

Abnormalities in reward learning in psychotic disorders have been proposed to be linked to dysregulated subcortical dopaminergic (DA) neurotransmission, which in turn is a suspected mechanism for predisposition to psychosis. We therefore explored the striatal dopaminergic modulation of reward processing and its behavioral correlates in individuals at familial risk for psychosis.

We performed a DA D2/3 receptor [18F]fallypride positron emission tomography scan during a probabilistic reinforcement learning task in 16 healthy first-degree relatives of patients with psychosis and 16 healthy volunteers, followed by a 6-day ecological momentary assessment study capturing reward-oriented behavior in the everyday life.

We detected significant reward-induced DA release in bilateral caudate, putamen and ventral striatum of both groups, with no group differences in its magnitude nor spatial extent. In both groups alike, greater extent of reward-induced DA release in all regions of interest was associated with better performance in the task, as well as in greater tendency to be engaged in reward-oriented behavior in the daily life.

These findings suggest intact striatal dopaminergic modulation of reinforcement learning and reward-oriented behavior in individuals with familial predisposition to psychosis. Furthermore, this study points towards a key link between striatal reward-related DA release and pursuit of ecologically relevant rewards.