Within-subject Variance Research Articles

Biological Variation of Hemostasis Analytes in Atrial Fibrillation Patients Using Dabigatran.

Analytical criteria for laboratory analysis based on biological variation are considered state-of-the-art. While biological variance should ideally be measured in patient populations for whom the tests are relevant, data are mostly only available from healthy individuals. We determined the biological variance of activated partial thromboplasmin time (APTT), prothrombin time (PT), fibrinogen, and trough dabigatran levels in patients with atrial fibrillation (AF) who were treated with dabigatran. Between 2019 and 2022, patients with AF treated >3 months with dabigatran were included. Blood was collected monthly up to 10 times for the measurement of APTT, PT, fibrinogen, and trough dabigatran levels. Between-subject variance (CVG), within-subject variance (CVI), and analytical variance (CVA) were calculated. Eighteen participants (median age 65.8 years, 22.2% women) were included, with 130 samples in total. For APTT, the CVG was 11.5%, the CVI 8.8%, and the CVA 1.1%. For PT, these values were 5.2%, 4.0%, and 1.0% and for fibrinogen 13.6%, 11.8%, and 1.6%, respectively. For the dabigatran levels, the percentages were 37.9%, 33.0%, and 3.4%, respectively. We assessed the biological variance of APTT, PT, fibrinogen, and dabigatran in a patient population with long-term dabigatran use. The analytical performances of coagulation laboratory tests in patients with AF treated with dabigatran were comparable to those in healthy volunteers.CCMO Registration Number: NL67304.078.18.

The Role of Remote Diagnostics to Better Assess Uroflow Variability: Insights From Combining at Home Uroflows and Frequency Volume Charts From 19,868 Voids Using a Novel, Hand Held, Cellular Embedded Device

ObjectiveTo examine how representative 24-hour data collection is of the overall patient experience utilizing a home uroflow device in men with benign prostatic hyperplasia. Materials and MethodsHome uroflow data was collected with the iO Urology CarePath device from men at a single urology clinic and retrospectively analyzed. Void characteristics were summarized for data collected over 24-hours compared to data collected over several days or weeks with the device (overall study excludes the 24-hour data). A linear mixed effects model was used to evaluate differences in average Qmax from voids collected during a single 24-hour period compared to the overall study. ResultsA total of 486 men (mean age 67.4) used the device resulting in 465 included in the analysis with a total of 15.521 voids in the overall study and 4,347 voids in the single day analysis. Average Qmax from the model was 11.2 ml/s (95% CI: 10.80, 11.65) and 11.2 ml/s (95% CI: 10.81, 11.64) in the 24-hour and overall study groups, respectively. Both groups had similar between- (20.3% vs 20.4%) and within-subject (12.0% vs 11.9%) variance. The difference in Qmax was not significantly different (p=0.970). A subgroup analysis including voids >150 ml yielded similar results. ConclusionsData shows variability in voiding parameters, but similar average Qmax collected in 24-hours as compared to several days/weeks. The combination of home uroflowmetry with frequency-volume chart information can provide objective data and a unique perspective on uroflow variability for clinician review to support the development of individualized patient treatment plans.

Evaluation of deep learning reconstruction on diffusion-weighted imaging quality and apparent diffusion coefficient using an ice-water phantom.

This study assessed the influence of deep learning reconstruction (DLR) on the quality of diffusion-weighted images (DWI) and apparent diffusion coefficient (ADC) using an ice-water phantom. An ice-water phantom with known diffusion properties (true ADC = 1.1 × 10-3 mm2/s at 0°C) was imaged at various b-values (0, 1000, 2000, and 4000s/mm2) using a 3T magnetic resonance imaging scanner with slice thicknesses of 1.5 and 3.0mm. All DWIs were reconstructed with or without DLR. ADC maps were generated using combinations of b-values 0 and 1000, 0 and 2000, and 0 and 4000s/mm2. Based on the quantitative imaging biomarker alliance profile, the signal-to-noise ratio (SNRs) in DWIs was calculated, and the accuracy, precision, and within-subject parameter variance (wCV) of the ADCs were evaluated. DLR improved the SNR in DWIs with b-values ranging from 0 to 2000s/mm2; however, its effectiveness was diminished at 4000s/mm2. There was no noticeable difference in the ADCs of images generated with or without implementing DLR. For a slice thickness of 1.5mm and combined b-values of 0 and 4000s/mm2, the ADC values were 0.97 × 10-3and 0.98 × 10-3mm2/s with and without DLR, respectively, both being lower than the true ADC value. Furthermore, DLR enhanced the precision and wCV of the ADC measurements. DLR can enhance the SNR, repeatability, and precision of ADC measurements; however, it does not improve their accuracies.

“Time may change me, but you can’t trace time”: the (In)stability of psycho-social doping profiles among adolescent athletes

ABSTRACT The Adolescent Sport Doping Inventory (ASDI; [Nicholls, A. R., Levy, A. R., Meir, R., Sanctuary, C., Jones, L., Baghurst, T., Thompson, M. A., & Perry, J. L. (2019). The development and validation of the adolescent sport drug inventory (ASDI) among athletes from four continents. Psychological Assessment, 31(11), 1279–1293. https://doi.org/10.1037/pas0000750] was one of the first questionnaires designed specifically to measure psycho-social doping constructs among adolescent athletes. Little is known about the reliability of this scale, and nor the stability of the psychosocial variables that the ASDI assesses. The aim of this paper was to assess the reliability of the ASDI, along with the extent to which key psychosocial constructs are stable, and whether there was variance across four clusters. Three independent samples of athletes were recruited. Athletes completed the ASDI one week apart (Sample 1, 92 athletes), 8 weeks apart (Sample 2, 134 athletes), and 16 weeks apart (Sample 3, 86 athletes). Findings revealed that the ASDI is a robust and reliable measure. While there was little within-subject variance in the data assessed one week apart (Sample 1) and 8 weeks apart (Sample 2), correlation coefficients in Sample 3 were markedly lower than Samples 1 or 2. The data also revealed that there was movement between cluster profiles for the eight- and 16-week gap, but not the one-week gap. In the short-term, psychosocial variables such as attitudes doping and susceptibility towards doping are relatively stable among adolescent athletes, although there is some movement between doping clusters. This could infer that ongoing anti-doping education is required to prevent undesirable changes in these important factors on a regular basis within adolescence.

Investigating the day-level associations between affective variability and physical activity using ecological momentary assessment

BackgroundUnderstanding affect as a determinant of physical activity has gained increased attention in health behavior research. Fluctuations in affect intensity from moment-to-moment (i.e., affective variability) may interfere with cognitive and regulatory processes, making it difficult to engage in goal-directed behaviors such as physical activity. Preliminary evidence indicates that those with greater trait-level affective variability engage in lower levels of habitual physical activity. However, the extent to which daily fluctuations in affect variability are associated with same-day physical activity levels is unknown. This study used ecological momentary assessment (EMA) to investigate day-level associations between affective variability (i.e., within-subject variance) and physical activity. MethodsYoung adults (N = 231, M = 23.58 ± 3.02 years) provided three months of smartphone-based EMA and smartwatch-based activity data. Every two weeks, participants completed a 4-day EMA measurement burst (M = 5.17 ± 1.28 bursts per participant). Bursts consisted of hourly randomly-prompted EMA surveys assessing momentary positive-activated (happy, energetic), positive-deactivated (relaxed), negative-activated (tense, stressed), and negative-deactivated (sad, fatigued) affect. Participants continuously wore a smartwatch to measure physical activity across the three months. Mixed-effects location scale modeling examined the day-level associations of affective variability (i.e., positive-activated, positive-deactivated, negative-activated, and negative-deactivated) and physical activity, controlling for covariates such as mean levels of affect, between-subject effects of physical activity, time of day, day of week, day in study, and smartwatch wear time. ResultsThere were 41,546 completed EMA surveys (M = 182.22 ± 69.82 per participant) included in the analyses. Above and beyond mean levels of affect, greater day-level variability in positive-activated affect was associated with greater physical activity on that same day compared to other days (τ = 0.01, p < .001), whereas greater day-level variability in negative-deactivated affect was associated with less physical activity on that same day compared to other days (τ = −0.01, p < .001). Day-level variability in positive-deactivated affect or negative-activated affect were not associated with day-level physical activity (ps > .05) ConclusionsIndividuals were less active on days with greater variability in feeling sad and fatigued but more active on days with greater variability in feeling happy and energetic. Understanding the dynamic relationships of affective variability with day-level physical activity can strengthen physical activity interventions by considering how these processes differ within individuals and unfold within the context of daily life. Future research should examine causal pathways between affective variability and physical activity across the day.

Within-subject pharmacokinetic variability has a strong influence on individual exposure ratios in bioequivalence studies, hence on drug formulation interchangeability.

Bioequivalence between a reference and a generic drug is based on the hypothesis that a ± 20% change in blood exposure (or ± 10% for drugs with narrow therapeutic index, NTI) following the generic/reference switch will not have any therapeutic consequences. However, the individual exposure ratio between generic and reference can be higher than 1.20 (or 1.10). This study aims to analyse the different parameters influencing the individual exposure ratio, hence the conditions for reference/generic interchangeability. Bioequivalence studies with a double cross-over design for a virtual drug were simulated using 100 random sets of 12, 24, 48 or 100 pairs of areas under the curve (AUC), varying the generic/reference AUC geometric mean ratios between 0.80 and 1.25 and the within-subject exposure variance of the reference and the generic formulations. The proportion of subjects with an exposure generic/reference ratio outside the ± 10% or ± 20% acceptance intervals increases when (1) the reference within-subject variance increases; (2) the ratio of the generic within-subject variance on the reference within-subject variance increases; and (3) the generic/reference mean AUC ratio diverges from 1.0. When only considering replicated administrations of the reference, the individual exposure ratio increases with the within-subject variance, yielding values outside the usually accepted individual exposure ratio range of 0.5 to 2 for drugs with narrow therapeutic index as soon as the within-subject variance standard deviation is ≥ 0.25 (equivalent to within-patient CV% > 25%). Interchangeability between reference and generic formulations, especially for drugs with narrow therapeutic index can only be assumed if, the within-subject variance of generic is less or equal to the within-subject variance of reference or, if this is not the case, if the distribution of the generic/generic individual exposure ratios is included within the therapeutic margins of the reference drug.

Personal exposure to aldehydes and potential health risks among schoolchildren in the city.

Schoolchildren are sensitive to airborne aldehyde exposures. The knowledge regarding inhalation exposure to aldehydes and the factors influencing exposure in schoolchildren is limited. This study aimed to assess the variability and potential health risks of exposure to aldehydes (including formaldehyde) in schoolchildren. The important factors affecting personal exposure to aldehydes were also explored. Forty schoolchildren were recruited from the urban and suburban areas of Taiwan for aldehyde samplings and questionnaire surveys. Personal and indoor home samples of aldehydes were collected simultaneously during warm and cold seasons. We also identified the potential variables associated with aldehyde exposure based on the participant's responses to the questionnaires using mixed-effects models. The dominant three abundant aldehydes identified in personal exposure samples were formaldehyde (geometric mean, GM = 12.2µg/m3), acetaldehyde (GM = 5.53µg/m3), and hexaldehyde (GM = 8.79µg/m3), accounting for approximately 80% of the total selected aldehydes. Higher personal exposure to aldehydes was observed during the warm season. Moreover, the within-subject variance was predominant, accounting for 66.6 to > 99.9% of the total variance in exposure. Schoolchildren had a high probability of overexposure to formaldehyde and acrolein, which resulted in an incremental lifetime cancer risk of 1.59 × 10-4 (95th percentile = 4.64 × 10-4). Season, location, household refurbishment, and indoor ventilation variables were significantly associated with personal exposure to aldehydes. The results can improve our understanding of aldehyde exposure among schoolchildren to propose mitigation strategies. These findings may be applied to further epidemiological studies.

Alternative covariance structures in mixed-effects models: Addressing intra- and inter-individual heterogeneity

Mixed-effects models for repeated measures and longitudinal data include random coefficients that are unique to the individual, and thus permit subject-specific growth trajectories, as well as direct study of how the coefficients of a growth function vary as a function of covariates. Although applications of these models often assume homogeneity of the within-subject residual variance that characterizes within-person variation after accounting for systematic change and the variances of the random coefficients of a growth model that quantify individual differences in aspects of change, alternative covariance structures can be considered. These include allowing for serial correlations between the within-subject residuals to account for dependencies in data that remain after fitting a particular growth model or specifying the within-subject residual variance to be a function of covariates or a random subject effect to address between-subject heterogeneity due to unmeasured influences. Further, the variances of the random coefficients can be functions of covariates to relax the assumption that these variances are constant across subjects and to allow for the study of determinants of these sources of variation. In this paper, we consider combinations of these structures that permit flexibility in how mixed-effects models are specified to understand within- and between-subject variation in repeated measures and longitudinal data. Data from three learning studies are analyzed using these different specifications of mixed-effects models.

Poorer White Matter Microstructure Predicts Slower and More Variable Reaction Time Performance: Evidence for a Neural Noise Hypothesis in a Large Lifespan Cohort.

Most prior research has focused on characterizing averages in cognition, brain characteristics, or behavior, and attempting to predict differences in these averages among individuals. However, this overwhelming focus on mean levels may leave us with an incomplete picture of what drives individual differences in behavioral phenotypes by ignoring the variability of behavior around an individual's mean. In particular, enhanced white matter (WM) structural microstructure has been hypothesized to support consistent behavioral performance by decreasing Gaussian noise in signal transfer. Conversely, lower indices of WM microstructure are associated with greater within-subject variance in the ability to deploy performance-related resources, especially in clinical populations. We tested a mechanistic account of the "neural noise" hypothesis in a large adult lifespan cohort (Cambridge Centre for Ageing and Neuroscience) with over 2500 adults (ages 18-102; 1508 female; 1173 male; 2681 behavioral sessions; 708 MRI scans) using WM fractional anisotropy to predict mean levels and variability in reaction time performance on a simple behavioral task using a dynamic structural equation model. By modeling robust and reliable individual differences in within-person variability, we found support for a neural noise hypothesis (Kail, 1997), with lower fractional anisotropy predicted individual differences in separable components of behavioral performance estimated using dynamic structural equation model, including slower mean responses and increased variability. These effects remained when including age, suggesting consistent effects of WM microstructure across the adult lifespan unique from concurrent effects of aging. Crucially, we show that variability can be reliably separated from mean performance using advanced modeling tools, enabling tests of distinct hypotheses for each component of performance.SIGNIFICANCE STATEMENT Human cognitive performance is defined not just by the long-run average, but trial-to-trial variability around that average. However, investigations of cognitive abilities and changes during aging have largely ignored this variability component of behavior. We provide evidence that white matter (WM) microstructure predicts individual differences in mean performance and variability in a sample spanning the adult lifespan (18-102). Unlike prior studies of cognitive performance and variability, we modeled variability directly and distinct from mean performance using a dynamic structural equation model, which allows us to decouple variability from mean performance and other complex features of performance (e.g., autoregression). The effects of WM were robust above the effect of age, highlighting the role of WM in promoting fast and consistent performance.

Dysbiosis in Head and Neck Cancer: Determining Optimal Sampling Site for Oral Microbiome Collection.

Recent research suggests that dysbiosis of the oral microbial community is associated with head and neck cancer (HNC). It remains unclear whether this dysbiosis causes chemo-radiotherapy (CRT)-related complications. However, to address this question, it is essential to determine the most representative oral site for microbiome sampling. In this study, our purpose was to determine the optimal site for oral sample collection and whether the presence of HNC is associated with altered oral microbiome from this site. In 21 newly diagnosed HNC patients and 27 healthy controls, microbiome samples were collected from saliva, swabs from buccal mucosa, tongue, hard palate, faucial pillars and all mucosal sites combined. Microbial DNA was extracted and underwent 16S rRNA amplicon gene sequencing. In healthy controls, analysis of observed taxonomic units detected differences in alpha- and beta-diversity between sampling sites. Saliva was found to have the highest intra-community microbial diversity and lowest within-subject (temporal) and between-subject variance. Feature intersection showed that most species were shared between all sites, with saliva demonstrating the most unique species as well as highest overlap with other sites. In HNC patients, saliva was found to have the highest diversity but differences between sites were not statistically significant. Across all sites, HNC patients had lower alpha diversity than healthy controls. Beta-diversity analysis showed HNC patients' microbiome to be compositionally distinct from healthy controls. This pattern was confirmed when the salivary microbiome was considered alone. HNC patients exhibited reduced diversity of the oral microbiome. Salivary samples demonstrate temporal stability, have the richest diversity and are sufficient to detect perturbation due to presence of HNC. Hence, they can be used as representative oral samples for microbiome studies in HNC patients.

Day-to-Day Variability of Clinical Feeding and Swallowing Performance in School-Age Self-Feeding Children With Cerebral Palsy.

We aimed to examine the day-to-day variability of feeding and swallowing performance and mealtime duration in school-age self-feeding children with spastic cerebral palsy (SCP) across 15 days. Thirteen children with SCP (ages 5;10 [years;months]-17;6) participated. Children were divided into unilateral (UCP, n = 6) and bilateral (BCP, n = 7) SCP groups. Feeding/swallowing assessments using the Dysphagia Disorder Survey (DDS) were conducted and total mealtime durations (TMDs) were calculated for all days. DDS Part 1 (factors related to feeding) and DDS Part 2 (signs of oropharyngeal difficulties) components were rated. Mixed-effects models were used to compare group means and estimate between- and within-subject variances in each group. Likelihood ratio tests were used to determine best covariance structure and compare variance types across groups. Within-subject variance for all three variables, DDS Part 1, 2, and TMD, across days was larger in the BCP group than the UCP group (Part 1: p = .0036, Part 2: p = .0002, and TMD: p = .0005) and the between-subject variance was larger in the BCP group for DDS Part 2 (p = .0362). The UCP group presented with lower (milder) DDS scores (Part 1: p = .0160; Part 2: p = .0141) and shorter TMD (p = .0077) than the BCP group across days. Furthermore, both groups exhibited greater variability in DDS Part 2 than 1 (p < .0001). These preliminary results emphasize the need to account for day-to-day variability when evaluating swallowing especially in children with BCP and provide preliminary ranges of performance that could be useful for clinical prognosis and future treatment research. https://doi.org/10.23641/asha.21669611.

Interchangeability between Generic and Reference Products: Limits of Average Bioequivalence Methodology.

Marketing authorisation of generic drugs is based on a demonstration of the "average" bioequivalence (ABE), with acceptance limits of 0.8-1.25 for the 90% confidence interval (CI) of the ratio (generic versus reference) of geometric means of exposure (whole blood, serum or plasma drug concentration). However, when interchangeability of reference by one generic is considered during treatment of a given patient, such methodology cannot guarantee the lack of therapeutic impact especially for drugs with narrow therapeutic index. This review article describes the basis and limits of ABE methodology, and the adaptations that have been proposed by regulatory agencies. For highly variable drugs, given their large therapeutic margin, regulatory agencies even allow widening of the bioequivalence acceptance limits. For drugs with a narrow therapeutic index, the average bioequivalence methodology has been amended differently by regulatory agencies. The European Medicine Agency only requires the narrowing of the ABE acceptance limits to the 0.9-1.10 range. The US Food and Drug Administration (FDA) has proposed to narrow the ABE acceptance limits according to the reference within-subject variance. The FDA requires a fully replicate cross-over study (with four periods) which allows one to compare the within-subject variance between generic and reference drug, and to detect any subject-by-formulation interaction. Indeed, any within-subject variance difference or subject by formulation interactionis an obstacle to interchangeability at the individual level. These amendments for the ABE do not fundamentally change the fact that individual ratios of exposure (generic/reference) will vary to a larger extent than the ratio of their means. For these reasons, since true individual bioequivalence studies cannot be performed for practical reasons and statistical issues, one can suggest that, in addition to the usual average bioequivalence criteria, the limits of the 95% confidence interval of the individual generic/reference exposure ratios could be used to allow interchangeability during treatment (at least for narrow therapeutic index drugs). Limit values of such CI for interchangeability acceptance should be scaled to the therapeutic margin of the reference drug. Regulatory agencies could conduct calculations based on real datasets of bioequivalence studies to determine if such criteria could be acceptable to allow interchangeability.

Reproducibility of Neuroretinal Rim Measurements Obtained from High-Density Spectral Domain Optical Coherence Tomography Volume Scans.

PurposeTo compare the reproducibility of two-dimensional (2D) peripapillary retinal nerve fiber layer (RNFL) thickness and three-dimensional (3D) neuroretinal rim measurements using spectral domain optical coherence tomography (SDOCT) in normal and glaucoma subjects.MethodsOne eye per subject for 27 normal and 40 glaucoma subjects underwent repeat SDOCT RNFL thickness scans and optic nerve volume scans on the same day. From the volume scan, custom software calculated five neuroretinal rim parameters: 3D minimum distance band (MDB) thickness, 3D MDB area, 3D rim volume, 2D rim area, and 2D rim thickness. Within-subject variance (Sw), coefficient of variation (CV), and intraclass correlation coefficient (ICC) were analyzed.ResultsMDB thickness and RNFL thickness have similar reproducibility among normal and glaucoma subjects (eg, global MDB thickness CVs of 2.4% and 3.6%, and global RNFL thickness CVs of 1.3% and 2.2%; P > 0.05 for both comparisons). Reproducibility of MDB thickness was lower in glaucoma patients for the superior and inferior quadrants compared to normal subjects (CVs of 9.6% versus 3.4% and 6.9% versus 2.7%; P < 0.05, respectively). There were no statistically significant differences between both groups for RNFL thickness in the four quadrants. For both patient groups and for all regions, MDB thickness had the lowest CVs among all five neuroretinal rim parameters (eg, global MDB thickness CVs of 2.4% and 3.6% versus 3.0% and 18.9% for the other four neuroretinal rim parameters).ConclusionGlobal MDB and global RNFL thickness are similarly reproducible among normal and glaucoma subjects, though MDB thickness for the superior and inferior quadrants is less reproducible among glaucoma subjects.

Exposure variability and determining factors of urinary metals for schoolchildren in Taiwan.

Urinary heavy metal levels in schoolchildren vary greatly over time, and research on the determinant variables explaining variance components in urinary metal exposure is limited. This study assessed metal concentrations and variability in the urine of schoolchildren and explored their important determining factors. We collected spot urine samples from schoolchildren (n=321) living in urban, suburban, and rural areas during the warm and cold seasons. The toxic (As, Cd, Ni, Cd, and Pb) and essential (Co, Cu, Mo, and V) elements of urine samples were analyzed by inductively coupled plasma mass spectrometry. We assessed the within- and between-subject variability in urinary metal concentrations, calculated the "probability of overexposure (θ)," compared with the reference values. We evaluated the spatiotemporal and biological factors that determine average exposure by using a mixed-effects model. The within-subject variance accounting for 63.8%-95.4% of the total variance in exposures was predominant for selected urinary metals. Urinary As levels (subject-specific mean=69.0μg/g creatinine) in schoolchildren were remarkably high and presented ∼99.9% of θ. After adjusting for the selected variables, we found that urban schoolchildren had significantly higher urinary metal levels (As, Cr, Co, and Ni) than suburban and rural schoolchildren. The urinary levels of most metals (except for Cu and Pb) increased during the cold season. Girls had higher urinary Cr, Co, Cu, Ni, and Pb levels than boys. Body mass index and urinary creatinine could also affect urinary metal levels. Those variables explained 15.8% (Pb), 11.6% (Cr), and 6.5% (Cd) of total variance in urinary concentrations. The repeated measurements of spot urine samples across seasons in individuals for long-term exposure estimates of metals were suggested. This biomonitoring survey of a large number of urine samples provided useful information on toxic metals and on the important determinants of exposure in schoolchildren.

Multilevel hybrid principal components analysis for region-referenced functional electroencephalography data.

Electroencephalography experiments produce region-referenced functional data representing brain signals in the time or the frequency domain collected across the scalp. The data typically also have a multilevel structure with high-dimensional observations collected across multiple experimental conditions or visits. Common analysis approaches reduce the data complexity by collapsing the functional and regional dimensions, where event-related potential (ERP) features or band power are targeted in a pre-specified scalp region. This practice can fail to portray more comprehensive differences in the entire ERP signal or the power spectral density (PSD) across the scalp. Building on the weak separability of the high-dimensional covariance process, the proposed multilevel hybrid principal components analysis (M-HPCA) utilizes dimension reduction tools from both vector and functional principal components analysis to decompose the total variation into between- and within-subject variance. The resulting model components are estimated in a mixed effects modeling framework via a computationally efficient minorization-maximization algorithm coupled with bootstrap. The diverse array of applications of M-HPCA is showcased with two studies of individuals with autism. While ERP responses to match vs mismatch conditions are compared in an audio odd-ball paradigm in the first study, short-term reliability of the PSD across visits is compared in the second. Finite sample properties of the proposed methodology are studied in extensive simulations.

Hormonal milieu drives economic demand for cocaine in female rats.

There are substantial sex differences in drug abuse, and a key feature of cocaine addiction is pathologically high motivation for drug. We investigated the role of ovarian hormones on cocaine demand in female rats using a within-session threshold behavioral economics (BE) procedure, which allows us to compare motivation for drug across hormonal states and sex while controlling for differences in dose and intake. This approach quantifies demand elasticity (α) and free consumption (Q0, consumption at null effort) to determine motivation for cocaine. Overall, female rats showed greater motivation for cocaine compared to males. However, this difference was cycle phase-dependent - motivation for cocaine when females were in proestrus was lower compared to the same animals across cycle phases, and overall similar to that of males. Hormonal cycle phase accounted for 70% of the within-subject variance in demand elasticity, obscuring other individual differences in female demand. High serum progesterone (P4; e.g., in proestrus) predicted decreased cocaine motivation (high demand elasticity), whereas serum estradiol (E2) correlated to greater intake at null effort (Q0). However, individual differences were revealed across OVX females, who displayed a range of demand elasticity, as seen in males. E2 replacement in OVX females increased motivation for cocaine, whereas P4 replacement decreased motivation. We also found that as few as 4 weeks of cocaine self-administration accelerated estropause in female rats as young as 12 weeks old. By 13 weeks of self-administration, proestrus epochs were no longer observed, and cocaine demand was potentiated by persistent estrus in all females. Thus, P4 signaling is a key modulator of cocaine demand in females that may underlie previously observed sex differences in addiction phenotypes.

Ultra-Widefield Indocyanine Green Angiography Reveals Patterns of Choroidal Venous Insufficiency Influencing Pachychoroid Disease.

PurposeTo compare patterns of choroidal venous drainage in eyes with pachychoroid disease to those of healthy subjects using ultra-widefield indocyanine green angiography (UWF ICGA).MethodsPatients with pachychoroid disease and healthy controls were recruited at two referral centers. UWF ICGA images were used to evaluate the proportion of the postequatorial fundus drained by major vortex vein systems in each quadrant and to study the incidence and topography of choroidal vascular hyperpermeability (CVH) and intervortex venous anastomoses. Widefield swept-source optical coherence tomography (SS-OCT) was used to evaluate choroidal thickness at the posterior pole in eyes with pachychoroid disease.ResultsFifty-two pachychoroid eyes and 26 healthy eyes were evaluated. Eyes with pachychoroid disease showed a significant within-subject variance in the proportion of the postequatorial fundus drained by each vortex vein system (range, 4.1%–48.1%; P < 0.0001) that was not seen in controls (range, 17.3%–31.7%; P = 0.11). CVH was present in all pachychoroid disease eyes and three of 26 controls. Intervortex venous anastomoses were present in 46 of 52 pachychoroid disease eyes and nine of 26 control eyes. Vortex vein systems with large drainage areas showed greater density of CVH spots. SS-OCT demonstrated asymmetric choroidal drainage in the macula of 59% of pachychoroid eyes. CVH and intervortex venous anastomoses were more prominent in areas showing maximal choroidal thickness.ConclusionsIn eyes with pachychoroid disease, imbalanced choroidal venous drainage with congestion of specific vortex vein systems may contribute to a state of choroidal venous insufficiency characterized by regional choroidal thickening, CVH and remodeling of venous drainage routes.

An extension of the mixed-effects growth model that considers between-person differences in the within-subject variance and the autocorrelation.

Experience sampling methods have led to a significant increase in the availability of intensive longitudinal data. Typically, this type of data is analyzed with a mixed-effects model that allows to examine hypotheses concerning between-person differences in the mean structure by including multiple random effects per individual (eg, random intercept and random slopes). Here, we describe an extension of this model that-in addition to the random effects for the mean structure-also includes a random effect for the within-subject variance and a random effect for the autocorrelation. After the description of the model, we show how its parameters can be efficiently estimated using a marginal maximum likelihood (ML) approach. We then illustrate the model using a real data example. We also present the results of a small simulation study in which we compare the ML approach with a Bayesian estimation approach.

Digitally characterizing the dynamics of multiple health behavior change.

We applied the ORBIT model to digitally define dynamic treatment pathways whereby intervention improves multiple risk behaviors. We hypothesized that effective intervention improves the frequency and consistency of targeted health behaviors and that both correlate with automaticity (habit) and self-efficacy (self-regulation). Study 1: Via location scale mixed modeling we compared effects when hybrid mobile intervention did versus did not target each behavior in the Make Better Choices 1 (MBC1) trial (n = 204). Participants had all of four risk behaviors: low moderate-vigorous physical activity (MVPA) and fruit and vegetable consumption (FV), and high saturated fat (FAT) and sedentary leisure screen time (SED). Models estimated the mean (location), between-subjects variance, and within-subject variance (scale). Treatment by time interactions showed that location increased for MVPA and FV (Bs = 1.68, .61; ps < .001) and decreased for SED and FAT (Bs = -2.01, -.07; ps < .05) more when treatments targeted the behavior. Within-subject variance modeling revealed group by time interactions for scale (taus = -.19, -.75, -.17, -.11; ps < .001), indicating that all behaviors grew more consistent when targeted. Study 2: In the MBC2 trial (n = 212) we examined correlations between location, scale, self-efficacy, and automaticity for the three targeted behaviors. For SED, higher scale (less consistency) but not location correlated with lower self-efficacy (r = -.22, p = .014) and automaticity (r = -.23, p = .013). For FV and MVPA, higher location, but not scale, correlated with higher self-efficacy (rs = .38, .34, ps < .001) and greater automaticity (rs = .46, .42, ps < .001). Location scale mixed modeling suggests that both habit and self-regulation changes probably accompany acquisition of complex diet and activity behaviors. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Spanking and externalizing problems: Examining within-subject associations.

This study examined the effects of spanking on externalizing on a within-subject level, while excluding causally irrelevant between-subject variance. Results from two longitudinal studies which used participants from the Child Development Project (n=585) were reanalyzed with a random-intercept cross-lagged panel model using yearly measurements over ages 6-8. After removing between-subject variance, there were no significant effects of general spanking on externalizing (β=.06, .07). However, when done without objects and at a rate of about once per month or less, spanking showed beneficial effects (β=-.17, -.21). Results suggest that previous findings may be due to a failure to separate between-subject and within-subject variance. Additionally, results illustrate the need to examine limited spanking separately from more general forms of physical punishment.