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.

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.

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.

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 . The utility of machine learning was investigated as a computational framework for extracting the most relevant features from EEG data empirically.