Effects of nicotinic cholinergic system manipulations on paired-associate learning (PAL) in mice

Rationale: The ability to perform on the Cambridge Neuropsychological Test Automated Battery touchscreen paired-associate learning (PAL) test is predictive of Alzheimer’s disease and Mild Cognitive Impairment. Recently, an automated computer touchscreen PAL task for mice has been developed. Pharmacological validation of this task is warranted to establish it as a useful tool in future drug discovery pertaining to Alzheimer’s disease and Mild Cognitive Impairment. Objectives: This investigation provides a systematic analysis of nicotinic involvement within the PAL task for mice. Particularly, the effects of systemic administration of nicotinic cholinergic agents (agonist and antagonist) on PAL task performance in C57BL/6 mice were investigated. This was done to detect whether bidirectional modification of performance is consequent upon these manipulations. Methods: Upon acquiring the PAL task, nicotine (nicotinic receptor agonist; 0.1, 0.5, and 1.0 mg/kg) and mecamylamine (nicotinic receptor antagonist; 0.3, 1.0, and 3.0 mg/kg) were administered intraperitoneally to the mice in a within-subjects design, prior to daily sessions in the PAL task. Results: Nicotine did not have any significant effect on PAL performance improvement at any doses. However, mecamylamine did increase perseverative responding and reaction time in the mice. Such impairment effects are interpreted as being attentional in nature. Conclusion: This investigation indicates that mice indeed acquire the rodent PAL task, deeming it a valuable tool for future drug discovery. Further, the nicotinic cholinergic system appears to be implicated in PAL task performance, with greater effects seen with deactivation rather than activation of the system, and with these effects appearing to be of an attentional nature. Keywords: paired-associate learning (PAL); Alzheimer’s disease; nicotinic cholingeric system; touchscree

Effects of volunteerism and relationship status on empathy

Volunteerism and relationship status differences were examined for their relatedness to empathy level, in the context of a psychological instrument for measuring empathy, the Baron-Cohen and Wheelwright Empathy Quotient (EQ). This Likerttype scale consisting of 60 items was completed by each of 100 participants, who were categorized based on self-declared volunteerism (a volunteer or not) and relationship status (in a relationship or not). Volunteerism was found to be a statistically significant factor in empathy level: those who volunteered exhibited higher empathy levels than those who did not. Relationship status was additionally statistically significant, such that those in a relationship had higher empathy levels than those who were not. When analyzed together, the factors of volunteerism and relationship status showed significant interaction in their influence on the dependent variable, empathy. Keywords: volunteerism; relationship status; empath

Motivation and engagement during cognitive training for schizophrenia spectrum disorders

Background Motivation and engagement are important factors associated with therapeutic outcomes in cognitive training for schizophrenia. The goals of the present report were to examine relations between objective treatment engagement (number of sessions attended, amount of homework completed) and self-reported motivation (intrinsic motivation and perceived competence to complete cognitive training) with neurocognitive and functional outcomes from cognitive training. Methods Data from a clinical trial comparing two cognitive training approaches in schizophrenia-spectrum disorders were utilized in the current report (n = 38). Relations were examined between baseline intrinsic motivation, perceived competence, homework completion, and session attendance with improvements in neurocognition, functional competence, and community functioning. Results Number of sessions attended (r = 0.38) and time doing homework (r = 0.51) were significantly associated with improvements in neurocognition. Homework completion was associated with change in community functioning at a trend-level (r = 0.30). Older age was associated with greater treatment engagement (β = 0.37) and male biological sex was associated with greater self-reported motivation (β = 0.43). Homework completion significantly mediated the relationship between session attendance and neurocognitive treatment outcomes. Conclusions Objective measures of treatment engagement were better predictors of treatment outcomes than subjective measures of motivation. Homework completion was most strongly related to treatment outcomes and mediated the relationship between session attendance and treatment outcomes, suggesting continued engagement with cognitive stimulation may be an especially important component of cognitive remediation programs. Future research should examine methods to improve homework completion and session attendance to maximize therapeutic outcomes

Language Analytics for Assessment of Mental Health Status and Functional Competency

Automated language analysis is becoming an increasingly popular tool in clinical research involving individuals with mental health disorders. Previous work has largely focused on using high-dimensional language features to develop diagnostic and prognostic models, but less work has been done to use linguistic output to assess downstream functional outcomes, which is critically important for clinical care. In this work, we study the relationship between automated language composites and clinical variables that characterize mental health status and functional competency using predictive modeling. Conversational transcripts were collected from a social skills assessment of individuals with schizophrenia (n = 141), bipolar disorder (n = 140), and healthy controls (n = 22). A set of composite language features based on a theoretical framework of speech production were extracted from each transcript and predictive models were trained. The prediction targets included clinical variables for assessment of mental health status and social and functional competency. All models were validated on a held-out test sample not accessible to the model designer. Our models predicted the neurocognitive composite with Pearson correlation PCC = 0.674; PANSS-positive with PCC = 0.509; PANSS-negative with PCC = 0.767; social skills composite with PCC = 0.785; functional competency composite with PCC = 0.616. Language features related to volition, affect, semantic coherence, appropriateness of response, and lexical diversity were useful for prediction of clinical variables. Language samples provide useful information for the prediction of a variety of clinical variables that characterize mental health status and functional competency

Language Analytics for Assessment of Mental Health Status and Functional Competency

Abstract Background and Hypothesis Automated language analysis is becoming an increasingly popular tool in clinical research involving individuals with mental health disorders. Previous work has largely focused on using high-dimensional language features to develop diagnostic and prognostic models, but less work has been done to use linguistic output to assess downstream functional outcomes, which is critically important for clinical care. In this work, we study the relationship between automated language composites and clinical variables that characterize mental health status and functional competency using predictive modeling. Study Design Conversational transcripts were collected from a social skills assessment of individuals with schizophrenia (n = 141), bipolar disorder (n = 140), and healthy controls (n = 22). A set of composite language features based on a theoretical framework of speech production were extracted from each transcript and predictive models were trained. The prediction targets included clinical variables for assessment of mental health status and social and functional competency. All models were validated on a held-out test sample not accessible to the model designer. Study Results Our models predicted the neurocognitive composite with Pearson correlation PCC = 0.674; PANSS-positive with PCC = 0.509; PANSS-negative with PCC = 0.767; social skills composite with PCC = 0.785; functional competency composite with PCC = 0.616. Language features related to volition, affect, semantic coherence, appropriateness of response, and lexical diversity were useful for prediction of clinical variables. Conclusions Language samples provide useful information for the prediction of a variety of clinical variables that characterize mental health status and functional competency