Background: The choroid plexus is an important structure within the ventricular system. Schizophrenia has been associated with morphological changes to the choroid plexus but the presence and extent of alterations at different illness stages is unclear.
Methods: We examined choroid plexus volumes in participants at clinical high-risk for psychosis (N = 110), participants with first-episode psychosis (N = 37), participants with schizophrenia (N = 28), clinical (N = 38) and non-clinical controls (N = 75).
Hippocampal dysfunctions are a core feature of schizophrenia, but conflicting evidence exists whether volumetric and morphological changes are present in early-stage psychosis and to what extent these deficits are related to clinical trajectories. In this study, we recruited individuals at clinical high risk for psychosis (CHR-P) (n = 108), patients with a first episode of psychosis (FEP) (n = 37), healthy controls (HC) (n = 70) as well as a psychiatric control group with substance abuse and affective disorders (CHR-N: n = 38). MRI-data at baseline were obtained and volumetric as well as vertex analyses of the hippocampus were carried out.
View Article and Find Full Text PDFA growing body of evidence suggests that, during decision-making, BOLD signal in the ventromedial prefrontal cortex (VMPFC) correlates both with motivational variables - such as incentives and expected values - and metacognitive variables - such as confidence judgments - which reflect the subjective probability of being correct. At the behavioral level, we recently demonstrated that the value of monetary stakes bias confidence judgments, with gain (respectively loss) prospects increasing (respectively decreasing) confidence judgments, even for similar levels of difficulty and performance. If and how this value-confidence interaction is reflected in the VMPFC remains unknown.
View Article and Find Full Text PDFPoor functional outcomes are common in individuals at clinical high-risk for psychosis (CHR-P), but the contribution of cognitive deficits remains unclear. We examined the potential utility of cognitive variables in predictive models of functioning at baseline and follow-up with machine learning methods. Additional models fitted on baseline functioning variables were used as a benchmark to evaluate model performance.
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