Randomized controlled trial of the glycine transporter 1 inhibitor PF-03463275 to enhance cognitive training and neuroplasticity in schizophrenia.

N-methyl-d-aspartate glutamate receptor (NMDAR) hypofunction is implicated in the impaired neuroplasticity and cognitive impairments associated with schizophrenia (CIAS). We hypothesized that enhancing NMDAR function by inhibiting the glycine transporter-1 (GLYT1) would improve neuroplasticity and thereby augment benefits of non-pharmacological cognitive training (CT) strategies. This study examined whether co-administration of a GLYT1 inhibitor and computerized CT would have synergistic effects on CIAS. Stable outpatients with schizophrenia participated in this double-blind, placebo-controlled, within-subject, crossover augmentation study. Participants received placebo or GLYT1 inhibitor (PF-03463275) for two 5-week periods separated by 2 weeks of washout. PF-03463275 doses (40 or 60 mg twice daily) were selected to produce high GLYT1 occupancy. To limit pharmacodynamic variability, only cytochrome P450 2D6 extensive metabolizers were included. Medication adherence was confirmed daily. Participants received 4 weeks of CT in each treatment period. Cognitive performance (MATRICS Consensus Cognitive Battery) and psychotic symptoms (Positive and Negative Syndrome Scale) were assessed in each period. 71 participants were randomized. PF-03463275 in combination with CT was feasible, safe, and well-tolerated at the doses prescribed but did not produce greater improvement in CIAS compared to CT alone. PF-03463275 was not associated with improved CT learning parameters. Participation in CT was associated with improvement in MCCB scores.

Paranoia and belief updating during the COVID-19 crisis.

The COVID-19 pandemic has made the world seem less predictable. Such crises can lead people to feel that others are a threat. Here, we show that the initial phase of the pandemic in 2020 increased individuals' paranoia and made their belief updating more erratic. A proactive lockdown made people's belief updating less capricious. However, state-mandated mask-wearing increased paranoia and induced more erratic behaviour. This was most evident in states where adherence to mask-wearing rules was poor but where rule following is typically more common. Computational analyses of participant behaviour suggested that people with higher paranoia expected the task to be more unstable. People who were more paranoid endorsed conspiracies about mask-wearing and potential vaccines and the QAnon conspiracy theories. These beliefs were associated with erratic task behaviour and changed priors. Taken together, we found that real-world uncertainty increases paranoia and influences laboratory task behaviour.

Stability of mismatch negativity event-related potentials in a multisite study.

OBJECTIVES: Mismatch negativity (MMN), an auditory event-related potential sensitive to deviance detection, is smaller in schizophrenia and psychosis risk. In a multisite study, a regression approach to account for effects of site and age (12-35 years) was evaluated alongside the one-year stability of MMN. METHODS: Stability of frequency, duration, and frequency + duration (double) deviant MMN was assessed in 167 healthy subjects, tested on two occasions, separated by 52 weeks, at one of eight sites. Linear regression models predicting MMN with age and site were validated and used to derive standardized MMN z-scores. Variance components estimated for MMN amplitude and latency measures were used to calculate Generalizability (G) coefficients within each site to assess MMN stability. Trait-like aspects of MMN were captured by averaging across occasions and correlated with subject traits. RESULTS: Age and site accounted for less than 7% of MMN variance. G-coefficients calculated at electrode Fz were stable (G = 0.63) across deviants and sites for amplitude measured in a fixed window, but not for latency (G = 0.37). Frequency deviant MMN z-scores averaged across tests negatively correlated with averaged global assessment of functioning. CONCLUSION: MMN amplitude is stable and can be standardized to facilitate longitudinal multisite studies of patients and clinical features.

One-Month Stability of Cyberball Post-Exclusion Ostracism Distress in Adolescents.

We examined one-month reliability, internal consistency, and validity of ostracism distress (Need Threat Scale) to simulated social exclusion during Cyberball. Thirty adolescents (13-18 yrs.) completed the Cyberball task, ostracism distress ratings, and measures of related clinical symptoms, repeated over one month. Need Threat Scale ratings of ostracism distress showed adequate test-retest reliability and internal consistency at both occasions. Construct validity was demonstrated via relationships with closely related constructs of anxiety, anxiety sensitivity, and emotion dysregulation, and weaker associations with more distal constructs of state paranoia and subclinical psychosis-like experiences. While ratings of ostracism distress and anxiety were significantly attenuated at retest, most participants continued to experience post-Cyberball ostracism distress at one-month follow-up, which indicates that the social exclusion induction of Cyberball persisted despite participants' familiarity with the paradigm. Overall, results suggest that the primary construct of ostracism distress is preserved over repeated administration of Cyberball, with reliability sufficient for usage in longitudinal research. These findings have important implications for translating this laboratory simulation of social distress into developmental and clinical intervention studies.

Machine learning identification of EEG features predicting working memory performance in schizophrenia and healthy adults.

BACKGROUND: With millisecond-level resolution, electroencephalographic (EEG) recording provides a sensitive tool to assay neural dynamics of human cognition. However, selection of EEG features used to answer experimental questions is typically determined a priori. The utility of machine learning was investigated as a computational framework for extracting the most relevant features from EEG data empirically. METHODS: Schizophrenia (SZ; n = 40) and healthy community (HC; n = 12) subjects completed a Sternberg Working Memory Task (SWMT) during EEG recording. EEG was analyzed to extract 5 frequency components (theta1, theta2, alpha, beta, gamma) at 4 processing stages (baseline, encoding, retention, retrieval) and 3 scalp sites (frontal-Fz, central-Cz, occipital-Oz) separately for correctly and incorrectly answered trials. The 1-norm support vector machine (SVM) method was used to build EEG classifiers of SWMT trial accuracy (correct vs. incorrect; Model 1) and diagnosis (HC vs. SZ; Model 2). External validity of SVM models was examined in relation to neuropsychological test performance and diagnostic classification using conventional regression-based analyses. RESULTS: SWMT performance was significantly reduced in SZ (p < .001). Model 1 correctly classified trial accuracy at 84 % in HC, and at 74 % when cross-validated in SZ data. Frontal gamma at encoding and central theta at retention provided highest weightings, accounting for 76 % of variance in SWMT scores and 42 % variance in neuropsychological test performance across samples. Model 2 identified frontal theta at baseline and frontal alpha during retrieval as primary classifiers of diagnosis, providing 87 % classification accuracy as a discriminant function. CONCLUSIONS: EEG features derived by SVM are consistent with literature reports of gamma's role in memory encoding, engagement of theta during memory retention, and elevated resting low-frequency activity in schizophrenia. Tests of model performance and cross-validation support the stability and generalizability of results, and utility of SVM as an analytic approach for EEG feature selection.