Hostile mood and social strain during daily life: A test of the transactional model.

Hostility is a multidimensional construct related to cardiovascular (CV) disease risk. Daily hostile mood and social interactions may precipitate stress-related CV responses in hostile individuals. Purpose: Determine whether trait cognitive hostility best predicts daily hostile mood and social interactions relative to other trait hostility factors and explore the temporal links between these daily measure

Application of a single-objective, hybrid genetic algorithm approach to pharmacokinetic model building.

A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three compounds. The root mean squared error and absolute mean prediction error of the best single-objective hybrid genetic algorithm candidates were a median of 0.2 points higher (range of 38.9 point decrease to 27.3 point increase) and 0.02 points lower (range of 0.98 point decrease to 0.74 point increase), respectively, than that of the final stepwise models. In addition, the best single-objective, hybrid genetic algorithm candidate models had successful convergence and covariance steps for each compound, used the same compartment structure as the manual stepwise approach for 6 of 7 (86 %) compounds, and identified 54 % (7 of 13) of covariates included by the manual stepwise approach and 16 covariate relationships not included by manual stepwise models. The model parameter values between the final manual stepwise and best single-objective, hybrid genetic algorithm models differed by a median of 26.7 % (q₁ = 4.9 % and q₃ = 57.1 %). Finally, the single-objective, hybrid genetic algorithm approach was able to identify models capable of estimating absorption rate parameters for four compounds that the manual stepwise approach did not identify. The single-objective, hybrid genetic algorithm represents a general pharmacokinetic model building methodology whose ability to rapidly search the feasible solution space leads to nearly equivalent or superior model fits to pharmacokinetic data

Sleep duration partially accounts for race differences in diurnal cortisol dynamics

Objective: Emerging research demonstrates race differences in diurnal cortisol slope, an indicator of hypothalamic-pituitary-adrenocortical (HPA)-axis functioning associated with morbidity and mortality, with African Americans showing flatter diurnal slopes than their White counterparts. Sleep characteristics are associated with both race and with HPA-axis functioning. The present report examines whether sleep duration may account for race differences in cortisol dynamics. Method: Participants were 424 employed African American and White adults (mean age = 42.8 years, 84.2% White, 53.6% female) with no cardiovascular disease (Adult Health and Behavior Project—Phase 2 [AHAB-II] cohort, University of Pittsburgh). Cortisol slope was calculated using 4 salivary cortisol readings, averaged over each of 4 days. Demographic (age, sex), psychosocial (socioeconomic status [SES], affect, discrimination), and health behaviors (smoking, alcohol use, physical activity) variables were used as covariates, and sleep (self-report and accelerometry) was also assessed. Results: African Americans had flatter slopes than Whites (F(1, 411) = 10.45, B = .02, p = .001) in models adjusting for demographic, psychosocial, and health behavior covariates. Shorter actigraphy-assessed total sleep time was a second significant predictor of flatter cortisol slopes (F(1, 411) = 25.27, B = −.0002, p \u3c .0001). Total sleep time partially accounted for the relationship between race and diurnal slope [confidence interval = .05 (lower = .014, upper .04)]. Conclusions: African Americans have flatter diurnal cortisol slopes than their White counterparts, an effect that may be partially attributable to race differences in nightly sleep duration. Sleep parameters should be considered in further research on race and cortisol. (PsycINFO Database Record (c) 2017 APA, all rights reserved

Sleep duration partially accounts for race differences in diurnal cortisol dynamics

Objective: Emerging research demonstrates race differences in diurnal cortisol slope, an indicator of hypothalamic-pituitary-adrenocortical (HPA)-axis functioning associated with morbidity and mortality, with African Americans showing flatter diurnal slopes than their White counterparts. Sleep characteristics are associated with both race and with HPA-axis functioning. The present report examines whether sleep duration may account for race differences in cortisol dynamics. Method: Participants were 424 employed African American and White adults (mean age = 42.8 years, 84.2% White, 53.6% female) with no cardiovascular disease (Adult Health and Behavior Project—Phase 2 [AHAB-II] cohort, University of Pittsburgh). Cortisol slope was calculated using 4 salivary cortisol readings, averaged over each of 4 days. Demographic (age, sex), psychosocial (socioeconomic status [SES], affect, discrimination), and health behaviors (smoking, alcohol use, physical activity) variables were used as covariates, and sleep (self-report and accelerometry) was also assessed. Results: African Americans had flatter slopes than Whites (F(1, 411) = 10.45, B = .02, p = .001) in models adjusting for demographic, psychosocial, and health behavior covariates. Shorter actigraphy-assessed total sleep time was a second significant predictor of flatter cortisol slopes (F(1, 411) = 25.27, B = −.0002, p \u3c .0001). Total sleep time partially accounted for the relationship between race and diurnal slope [confidence interval = .05 (lower = .014, upper .04)]. Conclusions: African Americans have flatter diurnal cortisol slopes than their White counterparts, an effect that may be partially attributable to race differences in nightly sleep duration. Sleep parameters should be considered in further research on race and cortisol. (PsycINFO Database Record (c) 2017 APA, all rights reserved

Perigenual anterior cingulate morphology covaries with perceived social standing

Low socioeconomic status (SES) increases the risk for developing psychiatric and chronic medical disorders. A stress-related pathway by which low SES may affect mental and physical health is through the perception of holding a low social standing, termed low subjective social status. This proposal implicates overlapping brain regions mediating stress reactivity and socioemotional behaviors as neuroanatomical substrates that could plausibly link subjective social status to health-related outcomes. In a test of this proposal, we used a computational structural neuroimaging method (voxel-based morphometry) in a healthy community sample to examine the relationships between reports of subjective social status and regional gray matter volume. Results showed that after accounting for potential demographic confounds, subclinical depressive symptoms, dispositional forms of negative emotionality and conventional indicators of SES, self-reports of low subjective social status uniquely covaried with reduced gray matter volume in the perigenual area of the anterior cingulate cortex (pACC)—a brain region involved in experiencing emotions and regulating behavioral and physiological reactivity to psychosocial stress. The pACC may represent a neuroanatomical substrate by which perceived social standing relates to mental and physical health

Trait positive and negative emotionality differentially associate withdiurnal cortisol activity

Inter-individual variability in metrics of hypothalamic-pituitary-adrenocortical (HPA) activity, such asthe slope of the diurnal decline in cortisol, cortisol awakening response (CAR), and total cortisol out-put, have been found to associate inversely with trait ratings of extraversion and positive affect (E/PA)and positively with neuroticism and negative affect (N/NA) in some, but not all, investigations. Theseinconsistencies may partly reflect varied intensity of cortisol sampling among studies and reliance onself-rated traits, which are subject to reporting biases and limitations of introspection. Here, we furtherexamined dispositional correlates of HPA activity in 490 healthy, employed midlife volunteers (M age = 43years; 54% Female; 86% white). Trait ratings were requested from participants and 2 participant-electedinformants using the Positive and Negative Affect Schedule (PANAS) and Extraversion and Neuroticismdimensions of NEO personality inventories. CAR was assessed as percent increase in cortisol levels fromawakening to 30 min after awakening; and the diurnal slope and total output of cortisol [Area Underthe Curve (AUC)] were determined from cortisol measurements taken at awakening, +4 and +9 h later,and bedtime, across 3 workdays. Structural equation modeling was used to estimate multi-informantE/PA and N/NA factors. We used 3 days of measurement as indicators to model each of the three latentcortisol factors (slope, CAR, and AUC). With the two latent emotionality and three latent cortisol indicesincluded there was good fit to the data ( 2(200)= 278.38, p = 0.0002; RMSEA = 0.028, 90% CI = 0.02–0.04;CFI/TLI = 0.97/0.96; SRMR = 0.04). After controlling for covariates (age, sex, race), results showed higherlatent E/PA associated with a steeper diurnal slope (Standardized ˇ = −0.19, p = 0.02) and smaller CAR(Standardized ˇ = −0.26, p = 0.004), whereas N/NA did not associate with any cortisol metric (Standard-ized ˇ’s = −0.12 to 0.13, p’s = 0.10 to 0.53). These findings suggest that positive emotionality may be moreclosely associated with indices of diurnal cortisol release than negative emotionality

Sleep quality and neural circuit function supporting emotion regulation.

UNLABELLED: BACKGROUND: Recent laboratory studies employing an extended sleep deprivation model have mapped sleep-related changes in behavior onto functional alterations in specific brain regions supporting emotion, suggesting possible biological mechanisms for an association between sleep difficulties and deficits in emotion regulation. However, it is not yet known if similar behavioral and neural changes are associated with the more modest variability in sleep observed in daily life. METHODS: We examined relationships between sleep and neural circuitry of emotion using the Pittsburgh Sleep Quality Index and fMRI data from a widely used emotion regulation task focusing on cognitive reappraisal of negative emotional stimuli in an unselected sample of 97 adult volunteers (48 women; mean age 42.78±7.37 years, range 30-54 years old). RESULTS: Emotion regulation was associated with greater activation in clusters located in the dorsomedial prefrontal cortex (dmPFC), left dorsolateral prefrontal cortex (dlPFC), and inferior parietal cortex. Only one subscale from the Pittsburgh Sleep Quality Index, use of sleep medications, was related to BOLD responses in the dmPFC and dlPFC during cognitive reappraisal. Use of sleep medications predicted lesser BOLD responses during reappraisal, but other aspects of sleep, including sleep duration and subjective sleep quality, were not related to neural activation in this paradigm. CONCLUSIONS: The relatively modest variability in sleep that is common in the general community is unlikely to cause significant disruption in neural circuits supporting reactivity or regulation by cognitive reappraisal of negative emotion. Use of sleep medication however, may influence emotion regulation circuitry, but additional studies are necessary to determine if such use plays a causal role in altering emotional responses

Using confirmatory factor analysis to measure contemporaneous activation of defined neuronal networks in functional magnetic resonance imaging

a b s t r a c t a r t i c l e i n f o Functional neuroimaging often generates large amounts of data on regions of interest. Such data can be addressed effectively with a widely-used statistical technique based on measurement theory that has not yet been applied to neuroimaging. Confirmatory factor analysis is a convenient hypothesis-driven modeling environment that can be used to conduct formal statistical tests comparing alternative hypotheses regarding the elements of putative neuronal networks. In such models, measures of each activated region of interest are treated as indicators of an underlying latent construct that represents the contemporaneous activation of the elements in the network. As such, confirmatory factor analysis focuses analyses on the activation of hypothesized networks as a whole, improves statistical power by modeling measurement error, and provides a theory-based approach to data reduction with a robust statistical basis. This approach is illustrated using data on seven regions of interest in a hypothesized mesocorticostriatal reward system in a sample of 262 adult volunteers assessed during a card-guessing reward task. A latent construct reflecting contemporaneous activation of the reward system was found to be significantly associated with a latent construct measuring impulsivity, particularly in males

Genetic variation in human NPY expression affects stress response and emotion

Understanding inter- individual differences in stress response requires the explanation of genetic influences at multiple phenotypic levels, including complex behaviours and the metabolic responses of brain regions to emotional stimuli. Neuropeptide Y ( NPY) is anxiolytic(1,2) and its release is induced by stress(3). NPY is abundantly expressed in regions of the limbic system that are implicated in arousal and in the assignment of emotional valences to stimuli and memories(4-6). Here we show that haplotype- driven NPY expression predicts brain responses to emotional and stress challenges and also inversely correlates with trait anxiety. NPY haplotypes predicted levels of NPY messenger RNA in postmortem brain and lymphoblasts, and levels of plasma NPY. Lower haplotype- driven NPY expression predicted higher emotion- induced activation of the amygdala, as well as diminished resiliency as assessed by pain/ stress- induced activations of endogenous opioid neurotransmission in various brain regions. A single nucleotide polymorphism ( SNP rs16147) located in the promoter region alters NPY expression in vitro and seems to account for more than half of the variation in expression in vivo. These convergent findings are consistent with the function of NPY as an anxiolytic peptide and help to explain inter- individual variation in resiliency to stress, a risk factor for many diseases.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62768/1/nature06858.pd