Model‐based analysis of skin conductance responses: Towards causal models in psychophysiology

The empirical investigation of unobservable psychological processes usually rests on operational definitions. As an alternative, we propose the use of explicit causal models. This is particularly useful in psychophysiology, where formal models can be expressed mathematically, exploiting biophysical constraints, and inverted to yield estimates of unobservable processes. In psychophysiology, recent advances have been made in causal modeling for skin conductance responses, which we discuss to exemplify the development of such models. Empirical evidence suggests that these methods have a greater validity compared to operational approaches. This review concludes by considering the theoretical implications for the field of psychophysiology and benefits for practical data analysis.     

Teachers’ ability and help attributions and children's math performance and task persistence

The present longitudinal study examined the reciprocal relationships between teachers’ causal attributions and children's math performance and task persistence. In total, 760 elementary school children and their teachers participated in this study. The children were tested in math twice, at the end of the second and third grades. At both time points teachers also filled out questionnaires measuring their causal attributions and assessed children's task persistence. The results indicated that the more the teachers attributed children's math success to their own help or math failure to children's ability, the poorer the children's math performance was. Likewise, the lower the children's math performance was, the more teachers thought that their math success was due to teachers’ help or math failure was due to children's ability. In contrast, teachers’ perception of success attributions to ability was based on children's task persistence as well as math performance. Persistent behaviour in turn increased children's math performance and vice versa. The findings highlight the importance of teachers’ causal attributions in children's learning.     

Carcinogenicity assessment of water-soluble nickel compounds

IARC is reassessing the human carcinogenicity of nickel compounds in 2009. To address the inconsistencies among results from studies of water-soluble nickel compounds, we conducted a weight-of-evidence analysis of the relevant epidemiological, toxicological, and carcinogenic mode-of-action data. We found the epidemiological evidence to be limited, in that some, but not all, data suggest that exposure to soluble nickel compounds leads to increased cancer risk in the presence of certain forms of insoluble nickel. Although there is no evidence that soluble nickel acts as a complete carcinogen in animals, there is limited evidence that suggests it may act as a tumor promoter. The mode-of-action data suggest that soluble nickel compounds will not be able to cause genotoxic effects in vivo because they cannot deliver sufficient nickel ions to nuclear sites of target cells. Although the mode-of-action data suggest several possible non-genotoxic effects of the nickel ion, it is unclear whether soluble nickel compounds can elicit these effects in vivo or whether these effects, if elicited, would result in tumor promotion. The mode-of-action data equally support soluble nickel as a promoter or as not being a causal factor in carcinogenesis at all. The weight of evidence does not indicate that soluble nickel compounds are complete carcinogens, and there is only limited evidence that they could act as tumor promoters.     

On the Treatment Effect in Clinical Trials with Dropout

Patient's dropout often occurs in clinical trials with multiple scheduled visits, which results in a great challenge in the analysis of incomplete data. As the first step, one has to define a relevant treatment effect parameter, which is not straightforward in the presence of dropout. We discuss and compare two different treatment effect parameters that are adopted in most analyses of clinical data: the study-end treatment effect and the last-observed treatment effect. Some related issues, such as the estimability of causal parameters, the dependence of study parameters on the dropout patterns, and the use of the last observation carry forward, are also discussed.     

Remnant cholesterol as a cause of ischemic heart disease: Evidence,definition, measurement,atherogenicity, high risk patients,and present and future treatment

This review focuses on remnant cholesterol as a causal risk factor for ischemic heart disease (IHD), on its definition, measurement, atherogenicity, and levels in high risk patient groups; in addition, present and future pharmacological approaches to lowering remnant cholesterol levels are considered.     

Phenome-wide analysis highlights putative causal relationships between self-reported migraine and other complex traits

BackgroundMigraine is a complex neurological disorder that is considered the most common disabling brain disorder affecting 14 % of people worldwide. The present study sought to infer potential causal relationships between self-reported migraine and other complex traits, using genetic data and a hypothesis-free approach.MethodsWe leveraged available summary statistics from genome-wide association studies (GWAS) of 1,504 phenotypes and self-reported migraine and inferred pair-wise causal relationships using the latent causal variable (LCV) method.ResultsWe identify 18 potential causal relationships between self-reported migraine and other complex traits. Hypertension and blood clot formations were causally associated with an increased migraine risk, possibly through vasoconstriction and platelet clumping. We observed that sources of abdominal pain and discomfort might influence a higher risk for migraine. Moreover, occupational and environmental factors such as working with paints, thinner or glues, and being exposed to diesel exhaust were causally associated with higher migraine risk. Psychiatric-related phenotypes, including stressful life events, increased migraine risk. In contrast, ever feeling unenthusiastic / disinterested for a whole week, a phenotype related to the psychological well-being of individuals, was a potential outcome of migraine.ConclusionsOverall, our results suggest a potential vascular component to migraine, highlighting the role of vasoconstriction and platelet clumping. Stressful life events and occupational variables potentially influence a higher migraine risk. Additionally, a migraine could impact the psychological well-being of individuals. Our findings provide novel testable hypotheses for future studies that may inform the design of new interventions to prevent or reduce migraine risk and recurrence.Supplementary InformationThe online version contains supplementary material available at 10.1186/s10194-021-01284-w.     

Dynamic Causal Modeling applied to fMRI data shows high reliability

Sensitivity, specificity, and reproducibility are vital to interpret neuroscientific results from functional magnetic resonance imaging (fMRI) experiments. Here we examine the scan–rescan reliability of the percent signal change (PSC) and parameters estimated using Dynamic Causal Modeling (DCM) in scans taken in the same scan session, less than 5 min apart. We find fair to good reliability of PSC in regions that are involved with the task, and fair to excellent reliability with DCM. Also, the DCM analysis uncovers group differences that were not present in the analysis of PSC, which implies that DCM may be more sensitive to the nuances of signal changes in fMRI data.     

Warmer is healthier: Effects on mortality rates of changes in average fine particulate matter (PM2.5) concentrations and temperatures in 100 U.S. cities

Recent studies have indicated that reducing particulate pollution would substantially reduce average daily mortality rates, prolonging lives, especially among the elderly (age ? 75). These benefits are projected by statistical models of significant positive associations between levels of fine particulate matter (PM2.5) levels and daily mortality rates. We examine the empirical correspondence between changes in average PM2.5 levels and temperatures from 1999 to 2000, and corresponding changes in average daily mortality rates, in each of 100 U.S. cities in the National Mortality and Morbidity Air Pollution Study (NMMAPS) data base, which has extensive PM2.5, temperature, and mortality data for those 2 years. Increases in average daily temperatures appear to significantly reduce average daily mortality rates, as expected from previous research. Unexpectedly, reductions in PM2.5 do not appear to cause any reductions in mortality rates. PM2.5 and mortality rates are both elevated on cold winter days, creating a significant positive statistical relation between their levels, but we find no evidence that reductions in PM2.5 concentrations cause reductions in mortality rates. For all concerned, it is crucial to use causal relations, rather than statistical associations, to project the changes in human health risks due to interventions such as reductions in particulate air pollution.