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Spite sensitivity provides a valuable construct to understand persecutory ideation and its underlying neural mechanisms. We examined the relationship between persecution and spite sensitivity in psychosis to identify their neural substrates.

People with psychosis and mood disorders experience disruptions in working memory; however, the underlying mechanism remains unknown. We focused on 2 potential mechanisms: first, poor attentional engagement should be associated with elevated levels of prestimulus alpha-band activity within the electroencephalogram (EEG), whereas impaired working memory encoding should be associated with reduced poststimulus alpha suppression.

People with schizophrenia (PSZ) have profound deficits in context processing, an executive process that guides adaptive behaviors according to goals and stored contextual information. Although various neural processes are involved in context processing and are affected in PSZ, the core underlying neural dysfunction is unclear.

Thalamic projections to the prefrontal cortex (PFC) are critical for cognition, and disruptions in these circuits are thought to underlie the pathophysiology of schizophrenia. Cognitive remediation training (REM) is a behavioral intervention that holds promise for improving cognition and functioning in schizophrenia; however, the extent to which it affects thalamo-prefrontal connections has not been researched. This study sought to determine whether patients with schizophrenia who undergo a placebo-controlled trial of REM show increased functional connectivity between the thalamus and PFC, and whether these changes correspond to improvements in cognition.

Psychotic-like experiences (PLEs) are considered the subclinical portion of the psychosis continuum. Research suggests that there are resting-state functional connectivity (rsFC) substrates of PLEs, yet it is unclear if the same substrates underlie more severe psychosis. Here, to our knowledge, we report the first study to build a cross-validated rsFC model of PLEs in a large community sample and directly test its ability to explain psychosis in an independent sample of patients with psychosis and their relatives.

Background:As resting-state functional connectivity (rsFC) research moves toward the study of individual differences, test–retest reliability is increasingly important to understand. Previous literature supports the test–retest reliability of rsFC derived with independent component analysis (ICA) and dual regression, yet the impact of dimensionality (i.e., the number of components to extract from group-ICA) remained obscure in the current context of large-scale data sets.Methods: To provide principled guidelines on this issue, ICA at dimensionalities varying from 25 to 350 was applied to the cortical surface with resting-state functional magnetic resonance imaging data from 1003 participants in the Human Connectome Project. The reliability of two rsFC measures, (within-component) coherence and (between-component) connectivity, was estimated.Results:Reliability and its change with dimensionality varied by network: the cognitive (frontoparietal, cingulo-opercular, dorsal attention, and default) networks were measured with the highest reliability, which improved with increased dimensionality until at least 150; the visual and somatomotor networks were measured with lower reliability, which benefited mildly from increased dimensionality; and the temporal pole/orbitofrontal cortex (TP/OFC) network was measured with the lowest reliability. Overall, ICA reliability was optimized at dimensionalities of 150 or above. Compared with two popular binary, nonoverlapping cortical atlases, ICA and dual regression resulted in higher reliability for the cognitive networks, lower reliability for the somatomotor network, and similar reliability for the visual and TP/OFC networks.Discussion:These findings highlight analytical decisions that maximize the reliability of rsFC measures and how they depend on one's networks of interest.Impact statementIndependent component analysis (ICA) and dual regression is a popular approach to resting-state functional connectivity (rsFC) analysis. Yet there is little consensus around the optimal ICA dimensionality, i.e., how many brain components to extract from group-ICA. We proposed that rsFC test-retest reliability is an important criterion when choosing dimensionality. The present study compares rsFC reliability across various dimensionalities in the state-of-the-art Human Connectome Project data. We also compared reliability based on ICA versus two popular brain atlases. The findings are of interest to both researchers who study brain parcellation and those who use rsFC to examine brain-behavior relationship.

Psychiatric diagnosis and treatment have historically taken a symptom-based approach, with less attention on identifying underlying symptom-producing mechanisms. Recent efforts have illuminated the extent to which different underlying circuitry can produce phenotypically similar symptomatology (e.g. psychosis in bipolar disorder vs schizophrenia). Computational modelling makes it possible to identify and mathematically differentiate behaviorally-unobservable, specific reinforcement-learning (RL) differences in schizophrenia (SZ) patients versus other disorders, likely due to a higher reliance on prediction-error(PE)-driven learning associated with basal ganglia, and under-reliance on explicit value representations associated with OFC.

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