Similar functional networks predict performance in both perceptual and value-based decision tasks.

There are numerous commonalities between perceptual and preferential decision processes. For instance, previous studies have shown that both of these decision types are influenced by context. Also, the same computational models can explain both. However, the neural processes and functional connections that underlie these similarities between perceptual and value-based decisions are still unclear. Hence, in the current study, we examine whether perceptual and preferential processes can be explained by similar functional networks utilizing data from the Human Connectome Project. We used resting-state functional magnetic resonance imaging data to predict performance of 2 different decision-making tasks: a value-related task (the delay discounting task) and a perceptual task (the flanker task). We then examined the existence of shared predictive-network features across these 2 decision tasks. Interestingly, we found a significant positive correlation between the functional networks, which predicted the value-based and perceptual tasks. In addition, a larger functional connectivity between visual and frontal decision brain areas was a critical feature in the prediction of both tasks. These results demonstrate that functional connections between perceptual and value-related areas in the brain are inherently related to decision-making processes across domains.

The longitudinal structure of negative symptoms in treatment resistant schizophrenia.

BACKGROUND AND HYPOTHESIS: The negative symptoms of schizophrenia are strong prognostic factors but remain poorly understood and treated. Five negative symptom domains are frequently clustered into the motivation and pleasure (MAP) and emotional expression (EE) 'dimensions', but whether this structure remains stable and behaves as a single entity or not remains unclear. STUDY DESIGN: We examined a cohort of 153 patients taking clozapine for treatment-resistant schizophrenia in a regional mental health clinic. Patients were assessed longitudinally over a mean period of 45 months using validated scales for positive, negative and mood symptoms. Network analyses were performed to identify symptom 'communities' and their stability over time. The influence of common causes of secondary negative symptoms as well as centrality measures were also examined. STUDY RESULTS: Across patients at baseline, two distinct communities matching the clinical domains of MAP and EE were found. These communities remained highly stable and independent over time. The communities remained stabled when considering psychosis, depression, and sedation severity, and these causes of secondary negative symptoms were clustered into the MAP community. Centrality measures also remained stable over time, with similar centrality measures across symptoms. CONCLUSIONS: Our results suggest that MAP and EE are independent dimensions that remain highly stable over time in chronic schizophrenia patients treated with clozapine. Common causes of secondary negative symptoms mapped onto the MAP dimension. Our results emphasise the need for clinical trials to address either MAP or EE, and that treating causes of secondary negative symptoms may improve MAP.

Using Topic Modeling to Understand Patients' and Caregivers' Perspectives About Left Ventricular Assist Device: Thematic Analysis.

BACKGROUND: Heart failure (HF) is a significant global clinical and public health challenge, impacting 64.3 million individuals worldwide. To address the scarcity of donor organs, left ventricular assist device (LVAD) implantation has become a crucial intervention for managing end-stage HF, serving as a bridge to heart transplantation or as a destination therapy. Web-based health forums, such as MyLVAD.com, play a vital role as trusted sources of information for individuals with HF symptoms and their caregivers. OBJECTIVE: We aim to uncover the latent topics within the posts shared by users on the MyLVAD.com website. METHODS: Using the latent Dirichlet allocation algorithm and a visualization tool, our objective was to uncover latent topics within the posts shared on the MyLVAD.com website. Through the application of topic modeling techniques, we analyzed 459 posts authored by recipients of LVAD and their family members from 2015 to 2023. RESULTS: This study unveiled 5 prominent themes of concern among patients with LVAD and their family members. These themes included family support (39.5% weight value), encompassing subthemes such as family caregiving roles and emotional or practical support; clothing (23.9% weight value), with subthemes related to comfort, normalcy, and functionality; infection (18.2% weight value), covering driveline infections, prevention, and care; power (12% weight value), involving challenges associated with power dependency; and self-care maintenance, monitoring, and management (6.3% weight value), which included subthemes such as blood tests, monitoring, alarms, and device management. CONCLUSIONS: These findings contribute to a better understanding of the experiences and needs of patients implanted with LVAD, providing valuable insights for health care professionals to offer tailored support and care. By using latent Dirichlet allocation to analyze posts from the MyLVAD.com forum, this study sheds light on key topics discussed by users, facilitating improved patient care and enhanced patient-provider communication.

Promoting head CT exams in the emergency department triage using a machine learning model.

PURPOSE: In this study, we aimed to develop a novel prediction model to identify patients in need of a non-contrast head CT exam during emergency department (ED) triage. METHODS: We collected data of all adult ED visits in our institution for five consecutive years (1/2013-12/2017). Retrieved variables included the following: demographics, mode of arrival to the ED, comorbidities, home medications, structured and unstructured chief complaints, vital signs, pain scale score, emergency severity index, ED wing assignment, documentation of previous ED visits, hospitalizations and CTs, and current visit non-contrast head CT usage. A machine learning gradient boosting model was trained on data from the years 2013-2016 and tested on data from 2017. Area under the curve (AUC) was used as metrics. Single-variable AUCs were also determined. Youden's index evaluated optimal sensitivity and specificity of the models. RESULTS: The final cohort included 595,561 ED visits. Non-contrast head CT usage rate was 11.8%. Each visit was coded into an input vector of 171 variables. Single-variable analysis showed that chief complaint had the best single predictive analysis (AUC = 0.87). The best model showed an AUC of 0.93 (95% CI 0.931-0.936) for predicting non-contrast head CT usage at triage level. The model had a sensitivity of 88.1% and specificity of 85.7% for non-contrast head CT utilization. CONCLUSION: The developed model can identify patients that need to undergo head CT exam already in the ED triage level and by that allow faster diagnosis and treatment.

Using recurrent neural network to estimate irreducible stochasticity in human choice behavior.

Theoretical computational models are widely used to describe latent cognitive processes. However, these models do not equally explain data across participants, with some individuals showing a bigger predictive gap than others. In the current study, we examined the use of theory-independent models, specifically recurrent neural networks (RNNs), to classify the source of a predictive gap in the observed data of a single individual. This approach aims to identify whether the low predictability of behavioral data is mainly due to noisy decision-making or misspecification of the theoretical model. First, we used computer simulation in the context of reinforcement learning to demonstrate that RNNs can be used to identify model misspecification in simulated agents with varying degrees of behavioral noise. Specifically, both prediction performance and the number of RNN training epochs (i.e., the point of early stopping) can be used to estimate the amount of stochasticity in the data. Second, we applied our approach to an empirical dataset where the actions of low IQ participants, compared with high IQ participants, showed lower predictability by a well-known theoretical model (i.e., Daw's hybrid model for the two-step task). Both the predictive gap and the point of early stopping of the RNN suggested that model misspecification is similar across individuals. This led us to a provisional conclusion that low IQ subjects are mostly noisier compared to their high IQ peers, rather than being more misspecified by the theoretical model. We discuss the implications and limitations of this approach, considering the growing literature in both theoretical and data-driven computational modeling in decision-making science.

Analyzing online forums to identify information needs and knowledge gaps in patients with left ventricular assist devices: a qualitative study.

AIM: To explore the knowledge and unmet informational needs of candidates for left ventricular assist devices (LVADs), as well as of patients, caregivers, and family members, by analyzing social media data from the MyLVAD.com website. METHODS AND RESULTS: A qualitative content analysis method was employed, systematically examining and categorizing forum posts and comments published on the MyLVAD.com website from March 2015 to February 2023. The data was collected using an automated script to retrieve threads from MyLVAD.com, focusing on genuine questions reflecting information and knowledge gaps. The study received approval from an ethics committee. The research team developed and continuously updated categorization matrices to organize information into categories and subcategories systematically. From 856 posts and comments analyzed, 435 contained questions representing informational needs, of which six main categories were identified: clothing, complications/adverse effects, LVAD pros and cons, self-care, therapy, and recent LVAD implantation. The self-care category, which includes managing the driveline site and understanding equipment functionality, was the most prominent, reflecting nearly half of the questions. Other significant areas of inquiry included complications/adverse effects and the pros and cons of LVAD. CONCLUSION: The analysis of social media data from MyLVAD.com reveals significant unmet informational needs among LVAD candidates, patients, and their support networks. Unlike traditional data, this social media-based research provides an unbiased view of patient conversations, offering valuable insights into their real-world concerns and knowledge gaps. The findings underscore the importance of tailored educational resources to address these unmet needs, potentially enhancing LVAD patient care.

Initial motor skill performance predicts future performance, but not learning.

People show vast variability in skill performance and learning. What determines a person's individual performance and learning ability? In this study we explored the possibility to predict participants' future performance and learning, based on their behavior during initial skill acquisition. We recruited a large online multi-session sample of participants performing a sequential tapping skill learning task. We used machine learning to predict future performance and learning from raw data acquired during initial skill acquisition, and from engineered features calculated from the raw data. Strong correlations were observed between initial and final performance, and individual learning was not predicted. While canonical experimental tasks developed and selected to detect average effects may constrain insights regarding individual variability, development of novel tasks may shed light on the underlying mechanism of individual skill learning, relevant for real-life scenarios.

Association of normal systolic blood pressure in the emergency department with higher in-hospital mortality among hypertensive patients.

Blood pressure is commonly elevated at the hospital emergency department (ED), especially among hypertensive patients. The aim of the study was to determine the association between ED systolic blood pressure (SBP) and in-hospital mortality among hypertensive patients. The authors retrospectively retrieved records of hypertensive patients who were hospitalized during a seven-year period. The authors examined the association between SBP and in-hospital mortality rate, adjusted for demographics, heart rate, comorbidities, laboratory results, and hospital ward. Overall, 96 423 patients were included. Compared to patients with SBP 110-139 mm Hg, the adjusted odds ratios were 4.1 (95% CI, 3.7-4.6) with SBP <90, 1.6 (95% CI, 1.4-1.7) with SBP 90-109, 0.7 (95% CI, 0.6-0.7) with SBP 140-159, 0.7 (95% CI, 0.6-0.7) with SBP 160-179, 0.7 (95% CI, 0.6-0.8) with SBP 180-199, 0.9 (95% CI, 0.7-1.1) with SBP 200-219, and 1.1 (95% CI, 0.7-1.7) with SBP ≥220 mm Hg. Thus, SBP levels of 110-139 mm Hg were associated with higher in-hospital mortality in comparison with elevated SBP up to 200 mm Hg.