The incidence of adolescent depressive disorder is globally skyrocketing in recent decades, albeit the causes and the decision deficits depression incurs has yet to be well-examined. With an instrumental learning task, the aim of the current study is to investigate the extent to which learning behavior deviates from that observed in healthy adolescent controls and track the underlying mechanistic channel for such a deviation.

We recruited a group of adolescents with major depression and age-matched healthy control subjects to carry out the learning task with either gain or loss outcome and applied a reinforcement learning model that dissociates valence (positive v. negative) of reward prediction error and selection (chosen v. unchosen).

The results demonstrated that adolescent depressive patients performed significantly less well than the control group. Learning rates suggested that the optimistic bias that overall characterizes healthy adolescent subjects was absent for the depressive adolescent patients. Moreover, depressed adolescents exhibited an increased pessimistic bias for the counterfactual outcome. Lastly, individual difference analysis suggested that these observed biases, which significantly deviated from that observed in normal controls, were linked with the severity of depressive symoptoms as measured by HAMD scores.

By leveraging an incentivized instrumental learning task with computational modeling within a reinforcement learning framework, the current study reveals a mechanistic decision-making deficit in adolescent depressive disorder. These findings, which have implications for the identification of behavioral markers in depression, could support the clinical evaluation, including both diagnosis and prognosis of this disorder.

Neuroimaging- and machine-learning-based brain-age prediction of schizophrenia is well established. However, the diagnostic significance and the effect of early medication on first-episode schizophrenia remains unclear.

To explore whether predicted brain age can be used as a biomarker for schizophrenia diagnosis, and the relationship between clinical characteristics and brain-predicted age difference (PAD), and the effects of early medication on predicted brain age.

The predicted model was built on 523 diffusion tensor imaging magnetic resonance imaging scans from healthy controls. First, the brain-PAD of 60 patients with first-episode schizophrenia, 60 healthy controls and 21 follow-up patients from the principal data-set and 40 pairs of individuals in the replication data-set were calculated. Next, the brain-PAD between groups were compared and the correlations between brain-PAD and clinical measurements were analysed.

The patients showed a significant increase in brain-PAD compared with healthy controls. After early medication, the brain-PAD of patients decreased significantly compared with baseline (P < 0.001). The fractional anisotropy value of 31/33 white matter tract features, which related to the brain-PAD scores, had significantly statistical differences before and after measurements (P < 0.05, false discovery rate corrected). Correlation analysis showed that the age gap was negatively associated with the positive score on the Positive and Negative Syndrome Scale in the principal data-set (r = −0.326, P = 0.014).

The brain age of patients with first-episode schizophrenia may be older than their chronological age. Early medication holds promise for improving the patient's brain ageing. Neuroimaging-based brain-age prediction can provide novel insights into the understanding of schizophrenia.

Patients with schizophrenia and individuals with schizotypy, a subclinical group at risk for schizophrenia, have been found to have impairments in cognitive control. The Dual Mechanisms of Cognitive Control (DMC) framework hypothesises that cognitive control can be divided into proactive and reactive control. However, it is unclear whether individuals with schizotypy have differential behavioural impairments and neural correlates underlying these two types of cognitive control.

Twenty-five individuals with schizotypy and 26 matched healthy controls (HCs) completed both reactive and proactive control tasks with electroencephalographic data recorded. The proportion of congruent and incongruent trials was manipulated in a classic colour-word Stroop task to induce proactive or reactive control. Proactive control was induced in a context with mostly incongruent (MI) trials and reactive control in a context with mostly congruent (MC) trials. Two event-related potential (ERP) components, medial frontal negativity (MFN, associated with conflict detection) and conflict sustained potential (conflict SP, associated with conflict resolution) were examined.

There was no significant difference between the two groups in terms of behavioural results. In terms of ERP results, in the MC context, HC exhibited significantly larger MFN (360–530 ms) and conflict SP (600–1000 ms) amplitudes than individuals with schizotypy. The two groups did not show any significant difference in MFN or conflict SP in the MI context.

The present findings provide initial evidence for dissociation of neural activation between proactive and reactive cognitive control in individuals with schizotypy. These findings help us understand cognitive control deficits in the schizophrenia spectrum.