Latent Factors Research Articles

Symptom Dimensions and Cognitive Impairments in Individuals at Clinical High Risk for Psychosis

BackgroundUnderstanding the intricate relationships between symptom dimensions, clusters, and cognitive impairments is crucial for early detection and intervention in individuals at clinical high risk for psychosis. This study delves into this complex interplay in a clinical high risk sample with the aim of predicting the conversion to psychosis. MethodsA comprehensive cognitive assessment was performed in 744 clinical high risk individuals. The study included a 3-year follow-up period to allow assessment of conversion to psychosis. Symptom profiles were determined using the Structured Interview for Prodromal Syndromes. By applying factor analysis, symptom dimensions were categorized as dominant negative symptoms (NSs), positive symptoms-stressful, and positive symptoms-odd. The factor scores were used to define 3 dominant symptom groups. Latent class analysis (LCA) and factor mixture model (FMM) were employed to identify discrete clusters based on symptom patterns. The 3-class solution was chosen for the LCA and FMM analysis. ResultsIndividuals in the dominant NS group exhibited significantly higher conversion rates to psychosis than those in the other groups. Specific cognitive variables, including performance on the Brief Visuospatial Memory Test–Revised (odds ratio = 0.702, p = .001) and Neuropsychological Assessment Battery Mazes Test (odds ratio = 0.776, p = .024), significantly predicted conversion to psychosis. Notably, cognitive impairments associated with NSs and positive symptoms-stressful affected different cognitive domains. LCA and FMM cluster 1, which was characterized by severe NSs and positive symptoms-odd, exhibited more impairments in cognitive domains than other clusters. No significant difference in the conversion rate was observed among the LCA and FMM clusters. ConclusionsThese findings highlight the importance of NSs in the development of psychosis and suggest specific cognitive domains that are affected by symptom dimensions.

The Inventory of Callous-Unemotional Traits (ICU) self-report version: Factor structure, measurement invariance, and predictive validity in justice-involved male adolescents.

The Inventory of Callous-Unemotional Traits (ICU) is a widely used measure of callous-unemotional (CU) traits that may aid in the assessment of the diagnostic specifier "with limited prosocial emotions," which has been added to diagnostic criteria for conduct disorder. Though there is substantial support for use of the ICU total score, the scale's factor structure has been highly debated. Inconsistencies in past factor analyses may be largely attributed to failure to control for method variance due to item wording (i.e., half of the items being worded in the callous direction and half worded in the prosocial direction). Thus, the present study used a multitrait-multimethod confirmatory factor analytic approach that models both trait and method variance to test the factor structure of the ICU self-report in a clinically relevant, high-risk sample of justice-involved male adolescents (N = 1,216). When comparing the fit of empirical and theoretical models, goodness of fit indices (χ² = 1105.877, df = 190, root-mean-square error of approximation = .063, comparative fit index = .916, Tucker-Lewis index = .878, standardized root-mean-square residual = .051) provided support for a hierarchical four-factor model (i.e., one overarching callous-unemotional factor, four latent trait factors) when accounting for method variance (i.e., covarying positively worded items). This factor structure is consistent with the way the ICU was constructed and with criteria for the limited prosocial emotions specifier. In addition, measurement invariance of this factor structure across age, race, and ethnicity was supported, and the predictive validity of the ICU was supported across these demographic groups in predicting self-reported antisocial behavior and rearrests over a 5-year period following an adolescent's first arrest. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Age-related differences in trait affect: Establishing measurement invariance of the Positive and Negative Affect Schedule (PANAS).

The present study examined trajectories of trait positive and trait negative affect as measured by the Positive and Negative Affect Schedule across the lifespan. Increasing levels of measurement invariance of the Positive and Negative Affect Schedule were tested across a wide age range (18-99 years of age; 10 age groups) in a large sample (N = 3,309; 65.82% women; 74.22% with a university entrance diploma; 92.23% with German as their mother tongue). Strong invariance was established so that the latent factor means of positive and negative affect could be meaningfully compared across age groups. Age had a small effect on positive affect, which showed a reversed U-shaped function with a maximum between the ages of 40 and 50. For negative affect, there was a strong cubic age effect. Specifically, negative affect decreased until about the age of 70 and then increased again. We also found a negative covariance between positive and negative affect, which first increased slightly and then decreased across age groups until becoming positive in the oldest age group. In summary, the present study supports previous findings on age trajectories of positive and negative affect but also highlights the importance of testing for measurement invariance. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Temporal Matrix Factorization: A polynomial approach to latent factor estimation

Matrix Factorization (MF) is a simple and efficient Machine Learning (ML) technique to discover latent factors that help in explaining the underlying behaviour of actors (for instance, in the domain of recommender systems the actors could be users/items). The technique uses observed data, generated by the actors, to derive the latent factors. With time, more and more observed data that spans across a wide range of time period gets accumulated. The abundance of this data, allows us to discover new behavioural patterns. One such pattern is that, the latent factor values change with time. Most of the existing matrix factorization techniques are agnostic to time of occurrence of the event and fail to foresee the temporal changes in the actor behaviour. Deep neural networks are effective in predicting the recommendations with time, however, the ability to explain the underlying pattern and the associated rationale is poor. In our work, we propose a novel matrix factorization technique to estimate the temporal changes in the user behaviour and demonstrate its effectiveness in achieving time dependent recommendations.

LatLBP: Spatial-spectral latent local binary pattern for hyperspectral image classification

The rich spatial and spectral information brings great potential for pixel-wise classification of hyperspectral image (HSI). Recently, Local Binary Pattern (LBP) as a prominent texture operator has been introduced for discriminative feature extraction of complex volumetric HSI. However, the popular two-dimensional LBP series can only capture spatial structure features and lack pattern descriptions of the spectral domain. The extended three-dimensional LBP series are capable of local multidimensional descriptions, while the information mining for narrow and continuous bands in the spectral domain is still limited. To tackle these issues, we develop a novel local binary pattern named LatLBP for investigating spatial-spectral latent information, which provides a low-dimensional, fine-grained, high-level representation of dual-domain latent semantic information by exploring the group structure of data. LatLBP consists of representative spatial latent (RSLat) LBP encoding and grouped spectral latent (GSLat) LBP encoding, where RSLat describes the local spatial potential features of each grouped representative band, and GSLat characterizes information about each group of latent spectral factors. Besides, a supporting neighborhood band grouping (NBG) algorithm is also designed to provide a finer group structure. Extensive experiments conducted on four HSI datasets indicate that the optimal classification results, i.e., 86.94% for Pavia University, 88.66% for Indian Pines, 77.75% for HanChuan, and 85.54% for HongHu, are achieved by either the proposed LatLBP or GSLat compared to competitive operators.

Associations of early adversity and negative emotionality with emerging adult latent internalizing

BackgroundEarly adversity (EA) predicts later internalizing, and trait diatheses also play a role in how this risk manifests. However, research is mixed on whether these factors function independently or interactively. Furthermore, recent work questions whether three trait diatheses thought to be distinct—neuroticism, dysfunctional attitudes, and brooding rumination—may be modeled as a single latent negative emotionality factor. The present study tests if brooding rumination and dysfunctional attitudes are better modeled with neuroticism as part of negative emotionality, as opposed to separate constructs, and whether negative emotionality and EA interact to predict latent internalizing or operate independently. Methods768 emerging adults (71.1% minoritized race/ethnicity) completed self-report surveys for this study. Latent moderated structural equation models were conducted to test associations between EA, trait diatheses, and internalizing. ResultsBrooding rumination and dysfunctional attitudes were best modeled as latent factors separate from—but highly correlated with—latent neuroticism. Latent trait diatheses and EA did not significantly interact to predict internalizing symptoms; however, there were significant main effects of latent brooding rumination and neuroticism. Although EA and dysfunctional attitudes had significant associations with internalizing when examined alone, their effects were negligible once neuroticism and brooding rumination were included in models. ConclusionsOur findings suggest neuroticism most strongly confers risk for internalizing, which may help to inform preventive intervention efforts.

What Emotion Regulation Strategies are Associated with a General Psychopathology Factor (p) in Adolescence? Identifying Possible Transdiagnostic Targets for Intervention.

Mental health problems in adolescence are increasing and there is an urgent call for transdiagnostic interventions, as most adolescents experience problems across multiple domains. Research has identified a general psychopathology factor (p) capturing the shared variance across mental health problems. In parallel, there is empirical support for the transdiagnostic nature of emotion regulation. In the current study, we explore the associations between p and emotion regulation strategies to identify which strategies may be promising targets for intervention. Data from the population-based Youth and Mental Health Study (YAMHS) (N = 2,464, mean age 13.7years, SD 0.6) was used. The fit of different structural models of emotion regulation and psychopathology were compared, and latent factors from the final models were included in multiple regression analyses to explore the associations between p and emotion regulation strategies. A bifactor exploratory structural equation model (B-ESEM) showed best fit for modelling p. For emotion regulation strategies, an ESEM model showed best fit, including Rumination, Reactive Impulsivity, Distraction, Non-Acceptance, Problem Solving, Social Support, and Proactive Behavior. Results showed that a higher score on p was predicted by higher scores on the emotion regulation strategies Rumination (β = .38, p < .001) and Reactive Impulsivity (β = .35, p < .001), and lower scores on Problem Solving (β = -.15, p < .001). The results indicate that Rumination and Reactive Impulsivity have the strongest associations with p and may be promising targets for mental health interventions for adolescents.

Parental domestic violence and abuse, mental ill-health, and substance misuse and the impact on child mental health: a secondary data analysis using the UK Millennium Cohort Study

BackgroundParental domestic violence and abuse (DVA), mental ill-health (MH), and substance misuse (SU) can have a negative impact on both parents and children. However, it remains unclear if and how parental DVA, MH, and SU cluster and the impacts this clustering might have. We examined how parental DVA, MH, and SU cluster during early childhood, the demographic/contextual profiles of these clusters, and how these clusters relate to child MH trajectories.MethodsWe examined data from 15,377 families in the UK Millennium Cohort Study. We used: (1) latent class analysis to create groups differentially exposed to parental DVA, MH, and SU at age three; (2) latent growth curve modelling to create latent trajectories of child MH from ages 3–17; and (3) a case-weight approach to relate latent classes to child MH trajectories.ResultsWe identified three latent classes: high-frequency alcohol use (11.9%), elevated adversity (3.5%), and low-level adversity (84.6%). Children in the elevated adversity class had higher probabilities of being from low-socioeconomic backgrounds and having White, younger parents. Children exposed to elevated adversity displayed worse MH at age three (intercept = 2.274; p < 0.001) compared the low-level adversity (intercept = 2.228; p < 0.001) and high-frequency alcohol use class (intercept = 2.068; p < 0.001). However, latent growth factors (linear and quadratic terms) of child MH did not differ by latent class.ConclusionsParental DVA, MH, and SU cluster during early childhood and this has a negative impact on children’s MH at age three, leading to similar levels of poor MH across time. Intervening early to prevent the initial deterioration, using a syndemic-approach is essential.

Anhedonia is associated with higher functional connectivity between the nucleus accumbens and paraventricular nucleus of thalamus

BackgroundAnhedonia stands as a life-threatening transdiagnostic feature of many mental illnesses, most notably major depression and involves neural circuits for processing reward information. The paraventricular nucleus of the thalamus (PVT) is associated with reward-seeking behavior, however, links between the PVT circuit and anhedonia have not been investigated in humans. MethodsIn a sample of adults with and without psychiatric symptoms (n = 75, 18–41 years, 55 female), we generated an anhedonia factor score for each participant using a latent factor analysis, utilizing data from depression and anxiety assessments. Functional connectivity between the PVT and the nucleus accumbens (NAc) was calculated from high-resolution (1.5 mm) resting state fMRI. ResultsAnhedonia factor scores showed a positive relationship with functional connectivity between the PVT and the NAc, principally in males and in those with psychiatric symptoms. In males, connectivity between other midline thalamic nuclei and the NAc did not show these relationships, suggesting that this link may be specific to PVT. LimitationsThis cohort was originally recruited to study depression and not anhedonia per se. The distribution of male and female participants in our cohort was not equal. Partial acquisition in high-resolution fMRI scans restricted regions of interest outside of the thalamus and reward networks. ConclusionsWe report evidence that anhedonia is associated with enhanced functional connectivity between the PVT and the NAc, regions that are relevant to reward processing. These results offer clues as to the potential prevention and prevention and treatment of anhedonia.

Evaluation of the psychometric properties of the UBACC questionnaire in a multi-country psychiatric study in Africa

BackgroundThe University of California, San Diego Brief Assessment of Capacity to Consent (UBACC) is a tool to assess the capacity of participants to consent in psychiatric research. However, little is known about the psychometric properties in low and middle-income countries. This study aimed to examine the psychometric properties of the UBACC. MethodsWe examined the reliability, latent factor structure, and item response of the first attempt of the UBACC items in a sample of 32,208 adults (16,467 individuals with psychosis and 15,741 controls) in Ethiopia, Kenya, South Africa, and Uganda; exploring these properties in the full sample and stratified by country, diagnostic status, sex, and ethnolinguistic language groups. ResultsExploratory factor analysis (EFA) suggested a two-factor model for the overall sample. However, a three-factor model was more appropriate when examining the latent structure across country, language, and sex. Confirmatory factor analyses (CFA) revealed an adequately fitting three-factor model for the full sample and across country, sex, and language. A two-factor model, however, was more appropriate for English and Amharic languages. Across all groups, the internal consistency of the UBACC was low, indicating below-threshold reliability (Cronbach's α (95 % CI = 0.58 (0.57–0.59). Using a multidimensional item-response theory framework for the full sample revealed that UBACC item 8, measuring understanding of the benefits of study participation, was the most discriminating item. Many of the other items had below-threshold discriminating characteristics. ConclusionEFA and CFA converged towards a two and three-dimensional structure for the UBACC, in line with the developers of the original scale. The differences in properties between populations and language groups, low internal consistency, and below-threshold item functioning suggest that investigations into the cultural and linguistic nuances are still warranted. Understanding the utility of consent tools, such as the UBACC, in underrepresented populations will be a part of the larger process which ensures that research participants are adequately protected.

Alternating nonnegative least squares-incorporated regularized symmetric latent factor analysis for undirected weighted networks

An Undirected Weighted Network (UWN) can be precisely quantified as an adjacency matrix whose inherent characteristics are fully considered in a Symmetric Nonnegative Latent Factor (SNLF) model for its good representation accuracy. However, an SNLF model uses a sole latent factor matrix to precisely describe the topological characteristic of a UWN, i.e., symmetry, thereby impairing its representation learning ability. Aiming at addressing this issue, this paper proposes an Alternating nonnegative least squares-incorporated Regularized Symmetric Latent factor analysis (ARSL) model. First of all, equation constraints composed of multiple matrices are built in its learning objective for well describing the symmetry of a UWN. Note that it adopts an L2-norm-based regularization scheme to relax such constraints for making such a symmetry-aware learning objective solvable. Then, it designs an alternating nonnegative least squares-incorporated algorithm for optimizing its parameters efficiently. Empirical studies on four UWNs demonstrate that an ARSL model outperforms the state-of-the-art models in terms of representation accuracy, as well as achieves promising computational efficiency.

Improving the explainability of autoencoder factors for commodities through forecast-based Shapley values

Autoencoders are dimension reduction models in the field of machine learning which can be thought of as a neural network counterpart of principal components analysis (PCA). Due to their flexibility and good performance, autoencoders have been recently used for estimating nonlinear factor models in finance. The main weakness of autoencoders is that the results are less explainable than those obtained with the PCA. In this paper, we propose the adoption of the Shapley value to improve the explainability of autoencoders in the context of nonlinear factor models. In particular, we measure the relevance of nonlinear latent factors using a forecast-based Shapley value approach that measures each latent factor’s contributions in determining the out-of-sample accuracy in factor-augmented models. Considering the interesting empirical instance of the commodity market, we identify the most relevant latent factors for each commodity based on their out-of-sample forecasting ability.

What drives labor force participation rate variability? The case of West Virginia

This study examines the dynamics of labor force participation rates across counties in West Virginia to better understand local labor market integration and the factors influencing fluctuations in participation. Drawing on county-level data from January 1990 to July 2020, the research employs a dynamic factor model to decompose labor force participation rates into latent factors at the state, metropolitan/non-metropolitan, and county levels. The findings reveal a general lack of labor market integration across West Virginia, highlighting potential opportunities for growth through enhanced integration. Further analysis using panel data models identifies key determinants of labor force participation, including personal income, education, infrastructure, and the prominence of industries such as agriculture and natural gas. The results underscore the necessity for targeted county-level policies to bolster employment and promote economic expansion within the state.

Tilts, developmental modules, and cognitive differentiation-integration effort: A multi-study response to

Sorjonen et al. (2024) critique a recently published finding that cognitive tilts are heritable, which was advanced as a line of evidence supporting their substantive (rather than artefactual) nature. These researchers claim: i) that the heritability of tilts is simply a function of the heritabilities of the specific cognitive dimensions used in their estimation, and ii) that spuriously heritable tilts can be recovered using difference scores between psychometric, anthropometric, and even random number variables. Here, multiple studies employing three behavior genetic datasets are used to test these claims. Even when cognitive tilts are residualized for their association with their constituent abilities, they still exhibit small, but non-zero heritabilities. Shared environmentality (C) accounts for the largest proportion of variance among these residuals. Tilts generated using random numbers are, by contrast, in all cases associated with AE models, exhibiting near 100 % E variance, corresponding to error. In the Swedish Twin Registry, the tilt residual is positively correlated with a measure of life history speed (Mini-K score), suggesting that tilts capture cognitive differentiation-integration effort conditioned developmentally by C variance. Distinct latent factors among psychometric and anthropometric variables in the Georgia Twin Study are also found. These indicate the presence of distinct developmental modules, meaning that tilts estimated using manifest variables associated with different modules lack theoretical credibility, as also evidenced by weak cross loadings.

Concurrent and longitudinal neurostructural correlates of irritability in children

Irritability, or an increased proneness to frustration and anger, is common in youth; however, few studies have examined neurostructural correlates of irritability in children. The purpose of the current study was to examine concurrent and longitudinal associations between brain structure and irritability in a large sample of 9–10-year-old children. Participants included 10,647 children from the Adolescent Brain Cognitive Developmentsm Study (ABCD Study®). We related a latent irritability factor to gray matter volume, cortical thickness, and surface area in 68 cortical regions and to gray matter volume in 19 subcortical regions using structural equation modeling. Multiple comparisons were adjusted for using the false discovery rate (FDR). After controlling for age, sex, race/ethnicity, scanner model, parent’s highest level of education, medication use, and total intracranial volume, irritability was associated with smaller volumes in primarily temporal and parietal regions at baseline. Longitudinal analyses showed that baseline gray matter volume did not predict irritability symptoms at the 3rd-year follow-up. No significant associations were found for cortical thickness or surface area. The current study demonstrates inverse associations between irritability and volume in regions implicated in emotional processing/social cognition, attention allocation, and movement/perception. We advance prior research by demonstrating that neurostructural differences associated with irritability are already apparent by age 9–10 years, extending this work to children and supporting theories positing socioemotional deficits as a key feature of irritability.

From tweets to tensions: exploring the roots of political polarization in Turkish constitutional referendum

ABSTRACT This study examines between-group and within-group polarization in online social networks to uncover latent factors and ideological changes over time. The study used 2017 Turkish constitutional referendum as a case focusing on Twitter dynamics between the yes-vote and no-vote communities, examining divergence of worldviews and partisan responses. We examined how changes in partisan asymmetries over time shape echo-chambers in opinion, network, and user polarization providing evidence of polarization at group, sub-group, and individual levels. Findings show differences in polarization between groups at different time periods. Before the referendum, yes-voting users are main drivers of polarization and more inward-looking than no-voting users. In contrast, considering the gap between winners and losers of the referendum and associated mood swings, in the days following the referendum no-voting users are main drivers of polarization. This demonstrates fine-grained details of polarization and changes in users’ sentiment as a latent factor behind seasonal behavior differences observed between two groups. Overall, yes-voting community was ideologically more coherent with a relatively denser network structure and more polarized body of users.

The Early Pretending Survey (EPS): A reliable parent-report measure of pretense type development for 4- to 47-month-olds

We present a new 18-item parent-report measure of pretense development for 4- to 47-month-olds: the Early Pretending Survey (EPS). Study 1 (N = 231) demonstrated good internal reliability, and a strongly correlated 2-factor structure for 4- to 47-month-olds. Study 2 (N = 587) also showed good internal reliability, and found the two factors loaded onto a latent second-order Pretend Play factor. Study 3 (N = 84) found the EPS correlated with a researcher-led pretense experiment, however it no longer correlated when controlling for age. Additional analyses using subsamples of parents from Studies 2 and 3 showed good inter-observer reliability between parents (N = 29), and good longitudinal stability after 6 months (N = 196). Additional analyses combining Study 1–3 participants (N ≤ 902) found no item functioning differences across demographic variables, including child age, country, and socio-economic factors. Children’s EPS scores increased as they aged; girls had higher scores than boys; and children of younger parents had higher scores. Finally, we examined the age patterns of each EPS item, demonstrating when 25 %, 50 %, and 75 % of children were predicted to pass each pretense type. The EPS is useful for researchers to better understand how pretending relates to other areas of development (e.g., cognition, language). The EPS could also help parents, early years educators, and children’s media professionals decide which pretense types to use with children of different ages.

Neurocognitive Latent Factors Associate With Early Tobacco and Alcohol Use Among Adolescent Brain Cognitive Development Study Youth

PurposeProspective associations between preadolescent neurocognitive structure and onset of substance use in adolescence have not been examined. This study investigated associations between cognitive structure among youth aged 9 – 10 years and the likelihood of experimentation with tobacco and alcohol by ages 13–14 years. MethodsA principal component (PC) analysis of nine neurocognitive assessments was used to identify the cognitive structure of unrelated adolescent brain cognitive development study participants (n = 9,655). We modeled associations between neurocognitive PCs and odds of tobacco or alcohol use by ages 13–14 years using generalized linear mixed models with a logit link and random intercept for recruitment sites. Demographics, family conflict, neighborhood safety, and externalizing and internalizing behavior were considered covariates. ResultsFour neurocognitive PCs were identified and labeled general ability, executive function, learning and memory, and mental rotation. Mental rotation [odds ratio [OR] = 0.88, p-value = .013] was associated with lower odds of youth tobacco use; the association was stronger among female youth. General ability [OR = 1.20, p-value < .0001] among both males and females, and learning and memory [OR = 1.11, p-value = .024] among females, were associated with increased odds of youth alcohol use. DiscussionAmong youth, higher neurocognitive performance was protective for tobacco use but increased the likelihood of alcohol use. Potential sex differences were identified. The role of cognition in processing the social contexts surrounding tobacco and alcohol use in the United States may contribute to the formation of disparate youth expectancies for tobacco and alcohol use.

Assessment of the medical school learning environment at United Arab Emirates University

BackgroundThe purpose of the article was to assess students’ perception of the learning environment at the College of Medicine and Health Sciences, United Arab Emirates University using the ‘Medical School Learning Environment Survey’ (MSLES). Evaluating the learning environment and working towards its improvement is crucial for the physical and mental well-being of medical students, as it contributes to fostering an optimal learning environment.MethodsStudents participated in four groups: Year-1 (pre-medical), Year-2 (pre-medical), Year-3/Year-4 (pre-clinical), and Year-5/Year-6 (clinical). MSLES data was collected from each group using an online survey tool (Qualtrics XM). Latent factor structures in the learning environment constructs were assessed using ‘exploratory factor analysis’; and reliability was measured by Cronbach’s alpha. Differences among groups were assessed by ‘single factor ANOVA’.ResultsThree hundred seventy-seven (65%) of the 584 eligible students completed the survey. Exploratory factor analysis revealed four factors: (Genn J. AMEE Medical Education Guide 23 (Part 1): Curriculum, environment, climate, quality and change in medical education–a unifying perspective. Med Teach. 2001;23(4):337–44.) Learning experience (∝=0.71), (Maudsley RF. Role models and the learning environment: essential elements in effective medical education. Acad Med. 2001;76(5):432–4.) Student-student interaction (∝=0.69), (Gruppen LD, Irby DM, Durning SJ, Maggio LA. Conceptualizing learning environments in the health professions. Acad Med. 2019;94(7):969–74.) Student-faculty interaction (∝=0.62), and (Soemantri D, Herrera C, Riquelme A. Measuring the educational environment in health professions studies: A systematic review. Med Teach. 2010;32(12):947–52.) Academic support (∝=0.62). Students in Y3-Y6 rated the learning environment statistically significantly lower than that in Y1-Y2. Student-student interaction in Y2 was significantly lower than that in other years. Student-faculty interaction in Y1 was significantly higher than that in Y2. Academic support was significantly higher in Y1 than that in Y2-Y6.ConclusionThe MSLES revealed variabilities in learning domains across the years. Improvement efforts should foster student-student collaboration in Y2 and improve academic support approaches for Y2-6. These findings provide valuable insights for medical educators to enhance the medical school learning environment and foster an optimal learning environment in lifelong medical education.

Individual suicide risk factors with resting-state brain functional connectivity patterns in bipolar disorder patients based on latent Dirichlet allocation model

BackgroundThe widespread problem of suicide and its severe burden in bipolar disorder (BD) necessitate the development of objective risk markers, aiming to enhance individual suicide risk prediction in BD. MethodsThis study recruited 123 BD patients (61 patients with prior suicide attempted history (PSAs), 62 without (NSAs)) and 68 healthy controls (HEs). The Latent Dirichlet Allocation (LDA) model was used to decompose the resting state functional connectivity (RSFC) into multiple hyper/hypo-RSFC patterns. Thereafter, according to the quantitative results of individual heterogeneity over latent factor dimensions, the correlations were analyzed to test prediction ability. ResultsModel constructed without introducing suicide-related labels yielded three latent factors with dissociable hyper/hypo-RSFC patterns. In the subsequent analysis, significant differences in the factor distributions of PSAs and NSAs showed biases on the default-mode network (DMN) hyper-RSFC factor (factor 3) and the salience network (SN) and central executive network (CEN) hyper-RSFC factor (factor 1), indicating predictive value. Correlation analysis of the individuals' expressions with their Nurses' Global Assessment of Suicide Risk (NGASR) revealed factor 3 positively correlated (r = 0.4180, p < 0.0001) and factor 1 negatively correlated (r = − 0.2492, p = 0.0055) with suicide risk. Therefore, it could be speculated that patterns more associated with suicide reflected hyper-connectivity in DMN and hypo-connectivity in SN, CEN. ConclusionsThis study provided individual suicide-associated risk factors that could reflect the abnormal RSFC patterns, and explored the suicide related brain mechanisms, which is expected to provide supports for clinical decision-making and timely screening and intervention for individuals at high risks of suicide.