NPP-digital psychiatry and neuroscience最新文献

Attention deficits, a hallmark of many neuropsychiatric disorders, significantly impair quality of life and functional outcome for patients. Continuous Performance Tests (CPTs) are widely used to assess attentional function in clinical settings and have been adapted for mice as the rodent Continuous Performance Test (rCPT). In this study, we combined traditional analyses of rCPT performance with markerless pose estimation using DeepLabCut and visual field analysis (VFA) to objectively measure the orientation of mice toward stimuli during rCPT sessions. Additionally, we extended the session length to assess performance decrements over time. Our findings show that extending rCPT sessions from 45-90 min results in a significant decline in performance in male mice, which aligns with performance decrements observed in clinical research. Importantly, physical engagement with the task remained relatively stable throughout the session, even as performance deteriorated. This suggests that the performance decline specifically reflects a time-on-task (TOT)-dependent vigilance decrement rather than physical disengagement. We also investigated the effects of amphetamine, an FDA-approved treatment for attention-deficit/hyperactivity disorder (ADHD), on rCPT performance. Amphetamine significantly reduced false alarms without affecting orientation or physical engagement with task stimuli in both male and female mice. This improved rCPT performance in males, but not in females because hit rate also declined following amphetamine administration in females. Collectively, these findings validate a behavioral tracking platform for objectively measuring physical engagement in the rCPT and a task modification that accentuates TOT-dependent performance decrements, enhancing the translational value of the rCPT for studies related to human neuropsychiatric disorders.

Poor introspective accuracy (IA), defined as inaccurate judgments of one's abilities and performance, is a strong and independent predictor of functional impairment in people with psychotic disorders. However, there are currently no treatments that directly target IA in this population as a primary outcome. We describe a protocol for a clinical trial to test a newly developed blended digital intervention, Improving Thinking through Everyday SelfAssessment Training (iTEST), aimed at improving IA in people with psychotic disorders to improve functional outcomes. iTEST involves daily training consisting of feedback on IA in mobile cognitive tests, coupled with individual coaching that applies improved IA to participant-identified recovery goals. Following the NIMH experimental therapeutics paradigm, the first step in the evaluation of iTEST is an open trial in 60 individuals with psychotic disorders to assess 1) feasibility and acceptability, and 2) whether the intervention leads to clinically significant improvement in an objective target: IA on trained tasks along with transfer to an untrained task-based measure of IA. After programming of the mobile intervention and the creation of treatment manuals, enrollment for an open trial started in November 2023 and will be completed by April 2025. If effective, iTEST could be integrated with cognitive training and other rehabilitative interventions to boost the impact on functional outcomes. Trial registration: ClinicalTrials.gov NCT05899348.

Reinforcement learning studies propose that decision-making is guided by a tradeoff between computationally cheaper model-free (habitual) control and costly model-based (goal-directed) control. Greater model-based control is typically used under highly rewarding conditions to minimize risk and maximize gain. Although prior studies have shown impairments in sensitivity to reward value in individuals with frequent alcohol use, it is unclear how these individuals arbitrate between model-free and model-based control based on the magnitude of reward incentives. In this study, 81 individuals (47 frequent Alcohol Users and 34 Alcohol Non-Users) performed a modified 2-step learning task where stakes were sometimes high, and other times they were low. Maximum a posteriori fitting of a dual-system reinforcement-learning model was used to assess the degree of model-based control, and a utility model was used to assess risk sensitivity for the low- and high-stakes trials separately. As expected, Alcohol Non-Users showed significantly higher model-based control in higher compared to lower reward conditions, whereas no such difference between the two conditions was observed for the Alcohol Users. Additionally, both groups were significantly less risk-averse in higher compared to lower reward conditions. However, Alcohol Users were significantly less risk-averse compared to Alcohol Non-Users in the higher reward condition. Lastly, greater model-based control was associated with a less risk-sensitive approach in Alcohol Users. Taken together, these results suggest that frequent Alcohol Users may have impaired metacontrol, making them less flexible to varying monetary rewards and more prone to risky decision-making, especially when the stakes are high.