Within-subject Variance Research Articles

Effect of thermodilution injectate volume and temperature on the accuracy and precision of cardiac output measurements for healthy anesthetized horses.

To compare the accuracy and precision of cardiac output (CO) measurements derived from 4 thermodilution protocols that used different injectate temperatures and volumes in healthy adult horses. 8 healthy adult horses. Horses were anesthetized and instrumented with Swan-Ganz catheters. The CO was derived from each of 4 thermodilution protocols (IV injection of physiologic saline [0.9% NaCl] solution chilled to < 5 °C at volumes of 1 mL/15 kg of body weight [protocol A; control], 1 mL/25 kg [protocol B], and 1 mL/35 kg [protocol C] or maintained at 17 °C at a volume of 1 mL/15 kg [protocol D]) 3 times during each of 5 measurement cycles, with a 30-minute interval between cycles. During each measurement cycle, protocol A was performed first, and protocols B, C, and D were performed in a randomized order. Mean CO and within-subject variance in CO were compared among the 4 protocols. Mean CO did not differ significantly among the 4 protocols. The within-subject variance for CO measurements derived from protocols C and D, but not protocol B, was significantly greater than that for protocol A (control). Results suggested that, in healthy adult horses, decreasing the thermodilution injectate volume to 1 mL/25 kg from the recommended volume of 1 mL/15 kg did not adversely affect the accuracy or precision of CO measurements. However, use of smaller injectate volumes or use of injectate at approximately room temperature is not recommended owing to a clinically unacceptable increase in CO measurement variability.

A Lasso and a Regression Tree Mixed-Effect Model with Random Effects for the Level, the Residual Variance, and the Autocorrelation

Research in psychology is experiencing a rapid increase in the availability of intensive longitudinal data. To use such data for predicting feelings, beliefs, and behavior, recent methodological work suggested combinations of the longitudinal mixed-effect model with Lasso regression or with regression trees. The present article adds to this literature by suggesting an extension of these models that—in addition to a random effect for the mean level—also includes a random effect for the within-subject variance and a random effect for the autocorrelation. After introducing the extended mixed-effect location scale (E-MELS), the extended mixed-effect location-scale Lasso model (Lasso E-MELS), and the extended mixed-effect location-scale tree model (E-MELS trees), we show how its parameters can be estimated using a marginal maximum likelihood approach. Using real and simulated example data, we illustrate how to use E-MELS, Lasso E-MELS, and E-MELS trees for building prediction models to forecast individuals’ daily nervousness. The article is accompanied by an R package (called mels) and functions that support users in the application of the suggested models.

WiSER: Robust and scalable estimation and inference of within-subject variances from intensive longitudinal data.

The availability of vast amounts of longitudinal data from electronic health records (EHRs) and personal wearable devices opens the door to numerous new research questions. In many studies, individual variability of a longitudinal outcome is as important as the mean. Blood pressure fluctuations, glycemic variations, and mood swings are prime examples where it is critical to identify factors that affect the within-individual variability. We propose a scalable method, within-subject variance estimator by robust regression (WiSER), for the estimation and inference of the effects of both time-varying and time-invariant predictors on within-subject variance. It is robust against the misspecification of the conditional distribution of responses or the distribution of random effects. It shows similar performance as the correctly specified likelihood methods but is 103 ∼ 105 times faster. The estimation algorithm scales linearly in the total number of observations, making it applicable to massive longitudinal data sets. The effectiveness of WiSER is evaluated in extensive simulation studies. Its broad applicability is illustrated using the accelerometry data from the Women's Health Study and a clinical trial for longitudinal diabetescare.

Biological variation of cardiac myosin-binding protein C in healthy individuals.

Cardiac myosin-binding protein C (cMyC) is a novel biomarker of myocardial injury, with a promising role in the triage and risk stratification of patients presenting with acute cardiac disease. In this study, we assess the weekly biological variation of cMyC, to examine its potential in monitoring chronic myocardial injury, and to suggest analytical quality specification for routine use of the test in clinical practice. Thirty healthy volunteers were included. Non-fasting samples were obtained once a week for ten consecutive weeks. Samples were tested in duplicate on the Erenna® platform by EMD Millipore Corporation. Outlying measurements and subjects were identified and excluded systematically, and homogeneity of analytical and within-subject variances was achieved before calculating the biological variability (CVI and CVG), reference change values (RCV) and index of individuality (II). Mean age was 38 (range, 21-64) years, and 16 participants were women (53%). The biological variation, RCV and II with 95% confidence interval (CI) were: CVA (%) 19.5 (17.8-21.6), CVI (%) 17.8 (14.8-21.0), CVG (%) 66.9 (50.4-109.9), RCV (%) 106.7 (96.6-120.1)/-51.6 (-54.6 to-49.1) and II 0.42 (0.29-0.56). There was a trend for women to have lower CVG. The calculated RCVs were comparable between genders. cMyC exhibits acceptable RCV and low II suggesting that it could be suitable for disease monitoring, risk stratification and prognostication if measured serially. Analytical quality specifications based on biological variation are similar to those for cardiac troponin and should be achievable at clinically relevant concentrations.

Mitigation of the convergence issues associated with semi-replicated bioequivalence data.

Semi-replicated designs for investigation of bioequivalence constitute a challenge when mixed models are applied. With the commonly available packages and regardless of choice of covariance structure the software may force variance components into the covariance matrix that render it over-specified. This may give rise to arbitrary estimates of certain variance components, lack of convergence or warnings. Classically the covariance matrix is decomposed as V=ZGZt +R, with G containing the between-subject variance components, Z being the design matrix for the random effects and R containing the within-subject variance components. By abandoning the definitions of G and R, and instead working directly in V, it is possible to specify a correct model with only the variance components of interest. Proof-of-concept for this idea is delivered with a script in the statistical language R. The script is available as supplementary material(Data S1).

Assessing the Reliability of Intraocular Pressure Measurements Using Rebound Tonometry.

In a trio of prospective studies, the iCare rebound tonometer demonstrated significantly lower test-retest variability than Goldmann tonometry with good interoperator and interdevice reproducibility, supporting its value in monitoring intraocular pressure (IOP) changes over time. The purpose of this study was to characterize intraoperator and interoperator and interdevice reliability of IOP measurements with rebound tonometry (RT, ic100). Three prospective cross-sectional studies were conducted in distinct sample of adult patients with established glaucoma, suspected glaucoma, or no glaucoma at the West Virginia University Eye Institute. Participants in study 1 underwent 5 RT measurements in one randomly selected eye and 5 Goldmann tonometry measurements in the fellow eye by 1 operator; intraoperator variability was compared using the F test. In study 2, 3 operators each obtained 3 RT measurements in participants in randomized operator order. In study 3, a single operator collected 3 measurements each with 3 RTs in randomized device order. Between-operator and between-device reproducibility were characterized using intraclass correlation coefficients (ICCs). Overall, 28, 19, and 25 subjects participated in the 3 respective studies. Within-subject variance across subjects was 0.757 in RT measurements and 2.471 in Goldmann measurements (P=0.0035). Interoperator reproducibility of RT measurements was good in both eyes [ICC for right eyes 0.78, 95% confidence interval (CI): 0.60-0.85; ICC for left eyes 0.75, 95% CI: 0.50-0.83]. Interdevice reproducibility of RT measurements was good approaching excellent (ICC for right eyes 0.87, 95% CI: 0.83-0.90; ICC for left eyes 0.89, 95% CI: 0.86-0.91). The RT's lower measurement variability and good interoperator and interdevice reproducibility suggest that it can characterize IOP changes over time more robustly than Goldmann tonometry, aiding clinicians in assessing the effectiveness of glaucoma therapy and the consistency of IOP control.

500 Correlation of the Idiopathic Hypersomnia Severity Scale With Other Measures of Sleep Parameters in a Phase 3 Trial

Abstract Introduction The Idiopathic Hypersomnia Severity Scale (IHSS) is a 14-item, self-reported questionnaire that assesses core Idiopathic Hypersomnia (IH) symptom severity. The relationship between IHSS scores and other IH symptom measures, such as the Epworth Sleepiness Scale (ESS; measures daytime sleepiness), sleep inertia visual analog scale (VAS-SI; measures sleep inertia), and Patient Global Impression of Change (PGIc; measures patients’ self-assessed change in symptoms), has not been established. This post hoc analysis analyzed correlations between the IHSS and ESS, VAS-SI, and PGIc using data from a clinical trial (NCT03533114) evaluating lower-sodium oxybate (LXB; Xywav™) for the treatment of IH. Methods During a clinical trial, the IHSS (0–50 score range) and ESS (0–24 score range) were completed at baseline; the IHSS, ESS, and PGIc (ordinal, 7 categories) were completed during an open-label treatment titration and optimization period (OLTTOP), after the OLTTOP, after a stable-dose period (SDP), and after a double-blind, randomized withdrawal period (DBRWP). The VAS-SI (0–100 score range) was completed during screening, SDP, and DBRWP. Correlation coefficients (rs) for IHSS vs ESS and VAS-SI were estimated from the within-subject variance matrix using a repeated-measures linear mixed model (LMM). The correlation between changes in IHSS score and PGIc was assessed using a Kruskal-Wallis test. Results IHSS scores correlated positively with ESS scores (rs [95% CI], 0.77 [0.73, 0.81]) and VAS-SI scores (0.69 [0.63, 0.75]). IHSS total score change was correlated with PGIc rank (chi-square with 6 degrees of freedom = 595.8, nominal P&amp;lt;0.001). The LMM showed that a 3-point change in ESS score corresponded to an average 3.99-point change in IHSS score; a 10-point change in VAS-SI score corresponded to an average 3-point change in IHSS score. Participants very much improved in the PGIc had a −12.13 (95% CI: −13.23, −11.04) expected change in IHSS score. Participants much improved in the PGIc had a −8.39 (95% CI: −9.30, −7.48) expected change in IHSS score. Conclusion IHSS scores or score changes strongly correlated with individual instruments assessing excessive daytime sleepiness, sleep inertia, and self-reported global symptoms, suggesting that the IHSS is a reliable, comprehensive measure of these symptoms of IH. Support (if any) Jazz Pharmaceuticals

The Precision of the Overall Data-Model Fit for Different Design Features in Confirmatory Factor Analysis

Factor Analysis (FA) is the study of variance within a group. Within-Subject Variance (WSV) is affected by multiple features in a study context such as the Experimental Design (ED) or the Sampling Design (SD). The aim of this study is to provide an empirical evaluation of the influence of different aspects of ED and SD on WSV in the context of FA in terms of model precision. The study results showed that the precisions of the overall model fit indices TLI and CFI, as functions of VTF, STV, h2, and their interaction, varied, as did the precisions of the overall model fit indices GFI, AGFI, and RMSEA as functions of VTF, STV, and their interactions. Overall, when the VTF is 4:1 or 7:1, the required STV is 16:1 or above 32:1 or above to show precision in factor solution.

A Mixed Effect Location-Scale Model with Mixture Distributed Scale Random Effects to Analyze (Near) Identical Entries in Ecological Momentary Assessments

Ecological Momentary Assessment (EMA) studies provide sufficient information to model both the mean (location) and the within-subject variance (scale) using the mixed effect location-scale model (M...

Biological variation of total thyroxine (T4), free T4 and thyroid-stimulating hormone in 11 clinically healthy cats.

The aim of this study was to investigate the biological variation of total thyroxine (T4), free T4 (fT4) and thyroid-stimulating hormone (TSH) in 11 clinically healthy cats aged between 3 and 15 years old, in Sydney, Australia. Blood was collected weekly for up to 6 weeks and serum T4, fT4 and TSH concentrations were analysed using canine-specific reagents. Restricted maximum likelihood was used to estimate within-subject, between-subject and analytical variance components, which were recorded in terms of the related coefficients of variation. The index of individuality and reference change values were then calculated for each analyte. T4 and TSH had intermediate individuality, indicating both subject-based and population-based reference intervals (RIs) could be used, with the knowledge that population-based RIs are suboptimally sensitive. fT4 had high individuality, indicating subject-based RIs are more appropriate than population-based RIs. This study has demonstrated that subject-based RIs could be more sensitive than population-based RIs for the diagnosis of thyroid dysfunction in cats.

Modeling population and subject-specific growth in a latent trait measured by multiple instruments over time using a hierarchical Bayesian framework

Psychometric growth curve modeling techniques are used to describe a person’s latent ability and how that ability changes over time based on a specific measurement instrument. However, the same instrument cannot always be used over a period of time to measure that latent ability. This is often the case when measuring traits longitudinally in children. Reasons may be that over time some measurement tools that were difficult for young children become too easy as they age resulting in floor effects or ceiling effects or both. We propose a Bayesian hierarchical model for such a scenario. Within the Bayesian model we combine information from multiple instruments used at different age ranges and having different scoring schemes to examine growth in latent ability over time. The model includes between-subject variance and within-subject variance and does not require linking item specific difficulty between the measurement tools. The model’s utility is demonstrated on a study of language ability in children from ages one to ten who are hard of hearing where measurement tool specific growth and subject-specific growth are shown in addition to a group level latent growth curve comparing the hard of hearing children to children with normal hearing.

Abstract 2342: Tobacco exposure and somatic mutations in normal bronchial epithelia

Abstract Background Lung cancer is the leading cause of cancer death, of which 80-90% are attributed to tobacco smoking. Our understanding of how tobacco exposure affects mutational burden, mutational signatures, driver mutations and clonal dynamics in normal lung tissue is limited. Also, genetic differences between normal cells and cancer cells in lung has not been fully elucidated. Methods To access the landscape of somatic mutations in normal bronchial epithelia, we sequenced whole genomes of 632 single-cell-derived colonies of lung basal cells from 16 patients, including 3 children, 4 never-smokers, 6 ex-smokers and 3 current smokers. Five patients had squamous cell carcinomas or carcinoma in situ, which we also sequenced to compare the genetic alteration between normal and cancer cells. Results A positive correlation between the number of base substitutions and age was observed, and 22 somatic mutations accumulated per year (95% confidence interval:20-25; P=10−8). Previous or current smoking significantly increased mutational burden: 2330 substitutions in ex-smokers and 5300 in current smokers. In addition, tobacco smoking is massively increasing both between-subject and within-subject variance of mutation burden. A population of cells in subjects with smoking history had mutation burdens equivalent to that expected for never-smokers: these cells had less damage from tobacco-specific mutational processes, and were four-fold more frequent in ex-smokers than current smokers. Signature analysis revealed that the same mutational signatures seen in lung cancers operate both in patients with and without smoking history: endogeneous signatures, including COSMIC signatures 1 and 5, and APOBEC-related signatures. Three mutational signatures were largely restricted to current or ex-smokers, including known smoking-related COSMIC signature 4 characterized by C&amp;gt;A transversions, signature 16 characterized by T&amp;gt;C mutations with extremely strong transcription strand bias and a new signature characterized by T&amp;gt;A and T&amp;gt;C mutations. dN/dS method identified driver genes in normal bronchial epithelium, including NOTCH1, TP53 and FAT1, which were overlapped with those seen in squamous cell lung cancers and other normal squamous tissues such as esophagus and skin. Driver mutations increased in frequency with age, affecting 4-14% of cells in middle-aged never-smokers. In current smokers, ≥25% of cells carried driver mutations and 0-6% cells had 2 or even 3 drivers. Layering driver mutations onto phylogenetic trees revealed that driver mutations occurred in early life. Compared to the normal bronchial epithelial cells, lung cancers and precancerous lesions were characterized by extensive copy number changes or structural variants and distinct set of driver mutations. Conclusions Tobacco smoking increases mutation burden, cell-to-cell heterogeneity and driver mutations. Our data of genetic lesions in normal bronchial cells provides insights into genetic alterations that drive carcinogenesis in lung. Citation Format: Kenichi Yoshida, Kate HC Gowers, Henry Lee-Six, Deepak P. Chandrasekharan, Tim Coorens, Elizabeth F. Maughan, Kathryn Beal, Andrew Menzies, Fraser R. Millar, Elizabeth Anderson, Sarah E. Clarke, Adam Pennycuick, Ricky M. Thackeray, Colin R. Butler, Nobuyuki Kakiuchi, Tomonori Hirano, Robert E. Hynds, Michael R. Stratton, Inigo Martincorena, Sam M. Janes, Peter J. Campbell. Tobacco exposure and somatic mutations in normal bronchial epithelia [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2342.

Mixed-effects Location Scale Modeling For The Analysis Of Accelerometry Data

PURPOSE: To introduce a statistical technique, the mixed-effects location scale model, for analysis of longitudinal accelerometer-based physical activity (PA) data. This approach jointly models both the mean (location) and within-subject variability (scale) of participants’ PA over time as a function of covariates, since within-person variability may be an important construct to explore in PA interventions. Random effects are included in both models to allow for subject-specific deviations beyond the effect of covariates. These random effects can be correlated. METHODS: Participants (N=204, 77% female, age=33±11y, BMI=28.2±7.1 kg/m2) in the Make Better Choices Study were randomized to one of two activity-related intervention arms: 1) increase moderate-to-vigorous PA (MVPA) (PA group) or 2) decrease sedentary active control (SB group). Physical activity was measured by accelerometer for 5 weeks: a 2 week baseline assessment phase and a 3 week intervention follow-up phase: week 1 (rx1) and weeks 2 and 3 (rx23). The outcome MVPA min/d was analyzed using the mixed-effects location scale model in the MIXREGLS software program in STATA. RESULTS: The mean model shows a significant group by time interaction (MVPA group by rx1: B=6.32 (95%CI: 3.93, 8.7) MVPA group by rx23: B=9.85 (95% CI: 7.59, 12.10)) indicating that those in the PA group had significantly greater MVPA min/d at rx1 and rx23 compared to the SB group. The PA group by rx23 interaction was significant in the within-subject variance model, suggesting that those in the PA group had significantly more variability in MVPA min/d during follow-up phase rx23 compared to the SB group. The random-location effect is positively associated with the within subject variance, participants with higher mean min/d MVPA tend to have higher min/d MVPA variability (τι=0.70 (95% CI: 0.60, 0.80)). The scale standard deviation is significant indicating that some participant’s MVPA min/d are significantly more dispersed than other participants even after adjusting for group and time effects (σω=0.60 (95% CI: 0.55, 0.64)). CONCLUSIONS: The location-scale mixed model provides a new approach for examining the mean and variability of min/d of MVPA in longitudinal data. To demonstrate, we applied this model to a randomized controlled trial to increase PA in inactive adults.

Using Variance to Explore the Diagnostic Utility of Baseline Concussion Testing.

The Graded Symptom Checklist (GSC), Standardized Assessment of Concussion (SAC), Balance Error Scoring System (BESS), and King-Devick Test (KDT) are considered important components of concussion assessment. Whether baseline testing improves the diagnostic utility of these tests remains unclear. We performed an observational cohort study to investigate the within-subject and between-subjects variability of these tests over repeated assessments during two football seasons to examine whether baseline testing reduces variability in test performance. Thirty-five male collegiate football players completed weekly clinical concussion assessments over two seasons. Within-subject (week-to-week) and between-subjects (player-to-player) variability for each test were compared using a bootstrap analysis. Within-subject and between-subjects proportions of overall variance for each test score were calculated. Mixed-model analyses were used to quantify practice effects resulting from repeated testing. For the GSC and BESS, within-subject and between-subjects variability did not significantly differ. For the KDT, the proportion of within-subject variance (20.2%) was significantly less than the between-subjects variance (79.8%). For SAC, however, the proportion of within-subject variance (66.8%) was significantly greater than the between-subjects variance (33.8%). A small, but significant, practice effect was observed for the BESS and KDT tests. When athletes are evaluated during a football season for concussion using the GSC, SAC, and BESS, comparing their scores to baseline performance is likely no more beneficial than comparing them to normative population data for identifying neurological changes associated with concussion. For the KDT, comparison to baseline testing is likely beneficial because of significantly higher between-subjects variability.

Vergence Eye Movements: From Basic Science to Clinical Application - Foreword to the Special Issue.

The abstract book of the last European Conference on Eye Movements [1] lists abstracts of 373 presentations, but less than five percent investigate vergence eye movements, i.e. the coordination of the right and left eye. Why then a special issue on this neglected issue? Human vision under natural conditions involves both eyes in coordination controlled by interacting processes subsumed under the concept of vergence.. Further, vergence is important for people in their daily lives since disorders of vergence can have serious consequences: ophthalmologists deal with squinting patients on the basis of heterophoria and heterotropia testing, eye strain or visual complaints can be related to impaired vergence dynamic or less accurate static vergence, remediation by optometrist includes vergence training or prism eye glasses, etc.

Characteristics and determinants of personal exposure to PM2.5 mass and components in adult subjects in the megacity of Guangzhou, China

Understanding the heterogeneity between ambient concentration and personal exposure is crucial in studies regarding the health risks of air pollution exposure. We performed a panel study with 4–19 (average = 10) repeated personal monitoring in 16 adult subjects (ages 18–30) for three consecutive weeks during the winter and summer of 2011–2012 in the Chinese megacity of Guangzhou. Also, we conducted simultaneous ambient measurements at eight districts (including five urban sites, two suburban locations, and one rural site) of Guangzhou. Significant seasonal variations were shown in personal PM2.5 exposure for most of the analyzed components (p < 0.05), with higher levels in winter than in summer. Average personal exposures exhibited a pattern of central urban > suburban > rural areas for PM2.5 mass and most of the constituents (e.g., carbonaceous aerosols, ions). We applied mixed-effects models to estimate within- and between-subject variance components and determinants of personal PM2.5 exposure after adjusting for potential confounders. The within-subject variance component dominated the total variability (63.7–95.6%) for most of the investigated PM2.5 components. Ambient PM2.5 mass and its components were the dominant predictors and contributors of the corresponding personal exposures (0.11 <Rc2 < 0.97; p < 0.05). The results indicate that season and district type affect personal PM2.5 exposure and its components, contributing to 4.9–51.6% and 8.0–77.8% of the variability. Time indoors and outdoors were also factors affecting personal exposure. The study findings revealed ambient concentrations at a fixed monitoring station underestimated residents’ true exposure levels. In conclusion, the current study emphasizes the need for incorporating spatio-temporal activity patterns complementing evenly-distributed air quality monitoring networks to increase the estimation power in epidemiological analysis linking true personal exposure to health effects.

MixWILD: A program for examining the effects of variance and slope of time-varying variables in intensive longitudinal data

The use of intensive sampling methods, such as ecological momentary assessment (EMA), is increasingly prominent in medical research. However, inferences from such data are often limited to the subject-specific mean of the outcome and between-subject variance (i.e., random intercept), despite the capability to examine within-subject variance (i.e., random scale) and associations between covariates and subject-specific mean (i.e., random slope). MixWILD (Mixed model analysis With Intensive Longitudinal Data) is statistical software that tests the effects of subject-level parameters (variance and slope) of time-varying variables, specifically in the context of studies using intensive sampling methods, such as ecological momentary assessment. MixWILD combines estimation of a stage 1 mixed-effects location-scale (MELS) model, including estimation of the subject-specific random effects, with a subsequent stage 2 linear or binary/ordinal logistic regression in which values sampled from each subject’s random effect distributions can be used as regressors (and then the results are aggregated across replications). Computations within MixWILD were written in FORTRAN and use maximum likelihood estimation, utilizing both the expectation-maximization (EM) algorithm and a Newton–Raphson solution. The mean and variance of each individual’s random effects used in the sampling are estimated using empirical Bayes equations. This manuscript details the underlying procedures and provides examples illustrating standalone usage and features of MixWILD and its GUI. MixWILD is generalizable to a variety of data collection strategies (i.e., EMA, sensors) as a robust and reproducible method to test predictors of variability in level 1 outcomes and the associations between subject-level parameters (variances and slopes) and level 2 outcomes.

Extending the mixed-effects model to consider within-subject variance for Ecological Momentary Assessment data.

Ecological Momentary Assessment data present some new modeling opportunities. Typically, there are sufficient data to explicitly model the within-subject (WS) variance, and in many applications, it is of interest to allow the WS variance to depend on covariates as well as random subject effects. We describe a model that allows multiple random effects per subject in the mean model (eg, random location intercept and slopes), as well as random scale in the error variance model. We present an example of the use of this model on a real dataset and a simulation study that shows the benefit of this model, relative to simpler approaches.

NEW ACCELEROMETRY PATTERNS IN FRAILTY: HOURLY ACTIVITY AND VARIANCE.

Wearable sensors may improve our ability to identify frailty in the community. Frailty has been historically defined, in part, by reduced average activity; however, new analytic methods of aggregate, free-living accelerometry data suggest that frailty may be more fully characterized above and beyond reduced average activity. Using mixed-effect regression models of awake hourly activity from the National Social Life, Health and Aging Project dataset, we have shown that frail adult activity is most reduced in the morning relative to pre- and non-frail adults rather than the afternoon or evening. High residual between- and within-subject activity variance in this model prompted further study of activity variance. A follow-up analysis using a mixed-effect location-scale model of hourly activity data revealed that increasing frailty in older adults is associated with greater between-subject as well as within-subject hourly activity variability, particularly in the morning and afternoon. Study implications and future directions will be discussed.

White Matter Asymmetry: A Reflection of Pathology in Traumatic Brain Injury.

Comparisons of white matter (WM) fractional anisotropy (FA) values between mild traumatic brain injury (mTBI) patients and controls have revealed inconsistencies in the directions of the resulting FA changes. To address these discrepancies, we examined hemispheric FA symmetry levels across WM tracts in 150 mTBI patients relative to 96 military controls. Automated fiber quantification was used to extract 18 WM tracts with 100 FA values, which were used to compute correlation strengths between the nine bilateral tract pairs. The Fisher z-transformed Pearson's r values were entered into an analysis of covariance examining the effects of group (mTBI and controls) and age on symmetry levels within each tract pair. The mTBI group displayed lower symmetry levels in the corticospinal tract and the inferior longitudinal fasciculus. Interactions between age and group were detected in the inferior fronto-occipital (IFOF), uncinate (UF), and superior longitudinal fasciculi (SLF). A similar pattern emerged in the IFOF and the UF, revealing age-related symmetry decreases in the mTBI patients despite stable levels of symmetry across ages in controls. In contrast, although the control group's symmetry levels actually increased with age in the SLF, no age-related symmetry changes were detected across the mTBI participants. Here, we proposed WM symmetry measures as a potential means of circumventing directional inconsistencies of trauma-related FA changes, as well as capturing more within-tract and within-subject variances of diffusion tensor imaging (DTI) metrics. Further, we demonstrated the method's utility in detecting mTBI-specific effects and their associated interactions with age.