The allostatic load model has proven useful to study the physiological toll of chronic stress on the brain and body among people with mood disorders. Allostatic load is typically indexed as a sum of ...(dys)functional biomarkers based on the sample’s distribution. This method has helped explain how chronic stress correlates to mood disorders. However, using sum scores to assess mood disorders may oversimplify interactions between symptoms and allostatic load. To address this limitation, we propose a method using network modeling to better capture the interactions between individual symptoms that can be studied in relation to allostatic load.
Seventy-six patients with mood disorders were recruited at a psychiatric emergency. Allostatic load was indexed using 15 biomarkers of neuroendocrine (DHEA-s, progesterone, testosterone), immune/inflammatory (IL-6, TNF-α, CRP), cardiovascular (SBP, DBP, pulse), and metabolic (glucose, cholesterol, HDL cholesterol, triglycerides, insulin, BMI) functioning. Anxiety/depressive symptoms were measured with self-reports. Gaussian Graphical Models and centrality were estimated with R.
Conditioning on interactions between allostatic load and psychiatric symptoms, we found that allostatic load was directly connected to loss of interest, sleep problems, and feeling tense. Interestingly, allostatic load was not a strong node in the overall network (strength = -2.481), nor did it influence average paths between other psychiatric symptoms (betweenness = -1.500).
Strong inter-correlations among psychiatric symptoms, but only with specific interactions with allostatic load biomarkers. Future studies should investigate how specific symptoms (anhedonia, sleep problems, tension) may be optimal targets for interventions aimed at decreasing allostatic load in mood disorders.
Adolescent students often report experiencing anxiety in school. This may lead to negative consequences for their well-being. Given that students in a classroom tend to imitate the behavior of their ...peers (a process called homophilia), the current exploratory study (1) assessed the association between the anxious responses of a student and the level of anxiety of classmates and (2) examined whether this association differed for boys and girls of different ages.
During two consecutives school years, 1044 Canadian students (415 boys and 619 girls) from six elementary schools and seven high schools completed questionnaires measuring different forms of normative anxiety (trait/state anxiety, test anxiety and anxiety sensitivity).
Multilevel analyses revealed a same-sex peer effect of classmates’ anxiety only in girls, so that the anxious responses of girls were related to the anxiety level of other girls in the same class (b = 0.31, p <.001). This effect was similar for both elementary and high school girls (b = 0.07, p =.27). Interestingly, no association was found for boys for same-sex (b = 0.15, p =.10) or opposite-sex peers (b = -0.03, p = 1.00).
Girls and boys seemed to differ in their sensitivity to the anxiety of their peers. More specifically, girls appeared to have a targeted responsiveness to the anxiety of the other girls in the classroom. This study reiterated the far-reaching effect of peers during adolescence and suggest that factors related to sex may reinforce anxiety in school settings.
We developed and validated RetinaVR, an affordable and immersive virtual reality simulator for vitreoretinal surgery training, using the Meta Quest 2 VR headset. We focused on four core fundamental ...skills: core vitrectomy, peripheral shaving, membrane peeling, and endolaser application. The validation study involved 10 novice ophthalmology residents and 10 expert vitreoretinal surgeons. We demonstrated construct validity, as shown by the varying user performance in a way that correlates with experimental runs, age, sex, and expertise. RetinaVR shows promise as a portable and affordable simulator, with potential to democratize surgical simulation access, especially in developing countries.
StepMix is an open-source Python package for the pseudo-likelihood estimation (one-, two- and three-step approaches) of generalized finite mixture models (latent profile and latent class analysis) ...with external variables (covariates and distal outcomes). In many applications in social sciences, the main objective is not only to cluster individuals into latent classes, but also to use these classes to develop more complex statistical models. These models generally divide into a measurement model that relates the latent classes to observed indicators, and a structural model that relates covariates and outcome variables to the latent classes. The measurement and structural models can be estimated jointly using the so-called one-step approach or sequentially using stepwise methods, which present significant advantages for practitioners regarding the interpretability of the estimated latent classes. In addition to the one-step approach, StepMix implements the most important stepwise estimation methods from the literature, including the bias-adjusted three-step methods with Bolk-Croon-Hagenaars and maximum likelihood corrections and the more recent two-step approach. These pseudo-likelihood estimators are presented in this paper under a unified framework as specific expectation-maximization subroutines. To facilitate and promote their adoption among the data science community, StepMix follows the object-oriented design of the scikit-learn library and provides an additional R wrapper.
Cette étude aborde le thème de l’utilisation des modèles de mélange de lois pour analyser des données de comportements et d’habiletés cognitives mesurées à plusieurs moments au cours du développement ...des enfants. L’estimation des mélanges de lois multinormales en utilisant l’algorithme EM est expliquée en détail. Cet algorithme simplifie beaucoup les calculs, car il permet
d’estimer les paramètres de chaque groupe séparément, permettant ainsi de modéliser plus facilement la covariance des observations à travers le temps. Ce dernier point est souvent mis de côté dans les analyses de mélanges. Cette étude porte sur les conséquences d’une mauvaise spécification de la covariance sur l’estimation du nombre de groupes formant un mélange. La conséquence principale est la surestimation du nombre de groupes, c’est-à-dire qu’on estime des groupes qui n’existent pas. En particulier, l’hypothèse d’indépendance des observations à travers le temps lorsque ces dernières étaient corrélées résultait en l’estimation de plusieurs groupes qui n’existaient pas. Cette surestimation du nombre de groupes entraîne aussi une surparamétrisation, c’est-à-dire qu’on utilise plus de paramètres qu’il n’est nécessaire pour modéliser les données. Finalement, des modèles de mélanges ont été estimés sur des données de comportements et d’habiletés cognitives. Nous avons estimé les mélanges en supposant d’abord une structure de covariance puis l’indépendance. On se rend compte que dans la plupart des cas l’ajout d’une structure de covariance a pour conséquence d’estimer moins de groupes et les résultats sont plus simples et plus clairs à interpréter.
This study is about the use of mixture to model behavioral and cognitive data measured repeatedly across development in children. Estimation of multinormal mixture models using the EM algorithm is explained in detail. This algorithm simplifies computation of mixture models because the parameters in each group are estimated separately, allowing to model covariance across time more easily. This last point is often disregarded when estimating mixture models. This study focused on the consequences of a misspecified covariance matrix when estimating the number of groups in a mixture. The main consequence is an overestimation of the number of groups, i.e. we estimate groups that do not exist. In particular, the independence assumption of the observations across time when they were in fact correlated resulted in estimating many non existing groups. This overestimation of the number of groups also resulted in an overfit of the model, i.e. we used more parameters than necessary. Finally mixture models were fitted to behavioral and cognitive data. We fitted the data first assuming a covariance structure, then assuming independence. In most cases, the analyses conducted assuming a covariance structure ended up having fewer groups and the results were simpler and clearer to interpret.
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