Schizophrenia is a disorder characterized by pervasive deficits in cognitive functioning. However, few well-powered studies have examined the degree to which cognitive performance is impaired even among individuals with schizophrenia not currently on antipsychotic medications using a wide range of cognitive and reinforcement learning measures derived from cognitive neuroscience. Such research is particularly needed in the domain of reinforcement learning, given the central role of dopamine in reinforcement learning, and the potential impact of antipsychotic medications on dopamine function.

The present study sought to fill this gap by examining healthy controls (N = 75), unmedicated (N = 48) and medicated (N = 148) individuals with schizophrenia. Participants were recruited across five sites as part of the CNTRaCS Consortium to complete tasks assessing processing speed, cognitive control, working memory, verbal learning, relational encoding and retrieval, visual integration and reinforcement learning.

Individuals with schizophrenia who were not taking antipsychotic medications, as well as those taking antipsychotic medications, showed pervasive deficits across cognitive domains including reinforcement learning, processing speed, cognitive control, working memory, verbal learning and relational encoding and retrieval. Further, we found that chlorpromazine equivalency rates were significantly related to processing speed and working memory, while there were no significant relationships between anticholinergic load and performance on other tasks.

These findings add to a body of literature suggesting that cognitive deficits are an enduring aspect of schizophrenia, present in those off antipsychotic medications as well as those taking antipsychotic medications.

Indexing left ventricular mass to body surface area or height2.7 leads to inaccuracies in diagnosing left ventricular hypertrophy in obese children. Lean body mass predictive equations provide the opportunity to determine the utility of lean body mass in indexing left ventricular mass. Our objectives were to compare the diagnostic accuracy of predicted lean body mass, body surface area, and height in detecting abnormal left ventricle mass in obese children.

Obese non-hypertensive patients aged 4–21 years were recruited prospectively. Dual-energy X-ray absorptiometry was used to measure lean body mass. Height, weight, sex, race, and body mass index z-score were used to calculate predicted lean body mass.

We enrolled 328 patients. Average age was 12.6 ± 3.8 years. Measured lean body mass had the strongest relationship with left ventricular mass (R2 = 0.84, p < 0.01) compared to predicted lean body mass (R2 = 0.82, p < 0.01), body surface area (R2 = 0.80, p < 0.01), and height2.7 (R2 = 0.65, p < 0.01). Of the clinically derived variables, predicted lean body mass was the only measure to have an independent association with left ventricular mass (β = 0.90, p < 0.01). Predicted lean body mass was the most accurate scaling variable in detecting left ventricular hypertrophy (positive predictive value = 88%, negative predictive value = 99%).

Lean body mass is the strongest predictor of left ventricular mass in obese children. Predicted lean body mass is the most accurate anthropometric scaling variable for left ventricular mass in left ventricular hypertrophy detection. Predicted lean body mass should be considered for clinical use as the body size correcting variable for left ventricular mass in obese children.