Trajectories of brain volumes in young children are associated with maternal education.

Brain growth in early childhood is reflected in the evolution of proportional cerebrospinal fluid volumes (pCSF), grey matter (pGM), and white matter (pWM). We study brain development as reflected in the relative fractions of these three tissues for a cohort of 388 children that were longitudinally followed between the ages of 18 and 96 months. We introduce statistical methodology (Riemannian Principal Analysis through Conditional Expectation, RPACE) that addresses major challenges that are of general interest for the analysis of longitudinal neuroimaging data, including the sparsity of the longitudinal observations over time and the compositional structure of the relative brain volumes. Applying the RPACE methodology, we find that longitudinal growth as reflected by tissue composition differs significantly for children of mothers with higher and lower maternal education levels.

Testing homogeneity: the trouble with sparse functional data.

Testing the homogeneity between two samples of functional data is an important task. While this is feasible for intensely measured functional data, we explain why it is challenging for sparsely measured functional data and show what can be done for such data. In particular, we show that testing the marginal homogeneity based on point-wise distributions is feasible under some mild constraints and propose a new two-sample statistic that works well with both intensively and sparsely measured functional data. The proposed test statistic is formulated upon energy distance, and the convergence rate of the test statistic to its population version is derived along with the consistency of the associated permutation test. The aptness of our method is demonstrated on both synthetic and real data sets.

Modeling sparse longitudinal data in early neurodevelopment.

Early childhood is a period marked by rapid brain growth accompanied by cognitive and motor development. However, it remains unclear how early developmental skills relate to neuroanatomical growth across time with no growth quantile trajectories of typical brain development currently available to place and compare individual neuroanatomical development. Even though longitudinal neuroimaging data have become more common, they are often sparse, making dynamic analyses at subject level a challenging task. Using the Principal Analysis through Conditional Expectation (PACE) approach geared towards sparse longitudinal data, we investigate the evolution of gray matter, white matter and cerebrospinal fluid volumes in a cohort of 446 children between the ages of 1 and 120 months. For each child, we calculate their dynamic age-varying association between the growing brain and scores that assess cognitive functioning, applying the functional varying coefficient model. Using local Fréchet regression, we construct age-varying growth percentiles to reveal the evolution of brain development across the population. To further demonstrate its utility, we apply PACE to predict individual trajectories of brain development.

LCox: a tool for selecting genes related to survival outcomes using longitudinal gene expression data.

Longitudinal genomics data and survival outcome are common in biomedical studies, where the genomics data are often of high dimension. It is of great interest to select informative longitudinal biomarkers (e.g. genes) related to the survival outcome. In this paper, we develop a computationally efficient tool, LCox, for selecting informative biomarkers related to the survival outcome using the longitudinal genomics data. LCox is powerful to detect different forms of dependence between the longitudinal biomarkers and the survival outcome. We show that LCox has improved performance compared to existing methods through extensive simulation studies. In addition, by applying LCox to a dataset of patients with idiopathic pulmonary fibrosis, we are able to identify biologically meaningful genes while all other methods fail to make any discovery. An R package to perform LCox is freely available at https://CRAN.R-project.org/package=LCox.

Longitudinal associations between white matter maturation and cognitive development across early childhood.

From birth to 5 years of age, brain structure matures and evolves alongside emerging cognitive and behavioral abilities. In relating concurrent cognitive functioning and measures of brain structure, a major challenge that has impeded prior investigation of their time-dynamic relationships is the sparse and irregular nature of most longitudinal neuroimaging data. We demonstrate how this problem can be addressed by applying functional concurrent regression models (FCRMs) to longitudinal cognitive and neuroimaging data. The application of FCRM in neuroimaging is illustrated with longitudinal neuroimaging and cognitive data acquired from a large cohort (n = 210) of healthy children, 2-48 months of age. Quantifying white matter myelination by using myelin water fraction (MWF) as imaging metric derived from MRI scans, application of this methodology reveals an early period (200-500 days) during which whole brain and regional white matter structure, as quantified by MWF, is positively associated with cognitive ability, while we found no such association for whole brain white matter volume. Adjusting for baseline covariates including socioeconomic status as measured by maternal education (SES-ME), infant feeding practice, gender, and birth weight further reveals an increasing association between SES-ME and cognitive development with child age. These results shed new light on the emerging patterns of brain and cognitive development, indicating that FCRM provides a useful tool for investigating these evolving relationships.

FMEM: Functional Mixed Effects Models for Longitudinal Functional Responses.

The aim of this paper is to conduct a systematic and theoretical analysis of estimation and inference for a class of functional mixed effects models (FMEM). Such FMEMs consist of fixed effects that characterize the association between longitudinal functional responses and covariates of interest and random effects that capture the spatial-temporal correlations of longitudinal functional responses. We propose local linear estimates of refined fixed effect functions and establish their weak convergence along with a simultaneous confidence band for each fixed-effect function. We propose a global test for the linear hypotheses of varying coefficient functions and derive the associated asymptotic distribution under the null hypothesis and the asymptotic power under the alternative hypothesis are derived. We also establish the convergence rates of the estimated spatial-temporal covariance operators and their associated eigenvalues and eigenfunctions. We conduct extensive simulations and apply our method to a white-matter fiber data set from a national database for autism research to examine the finite-sample performance of the proposed estimation and inference procedures.

Lysozyme-rich milk mitigates effects of malnutrition in a pig model of malnutrition and infection.

Malnutrition remains a leading contributor to the morbidity and mortality of children under the age of 5 years and can weaken the immune system and increase the severity of concurrent infections. Livestock milk with the protective properties of human milk is a potential therapeutic to modulate intestinal microbiota and improve outcomes. The aim of this study was to develop an infection model of childhood malnutrition in the pig to investigate the clinical, intestinal and microbiota changes associated with malnutrition and enterotoxigenic Escherichia coli (ETEC) infection and to test the ability of goat milk and milk from genetically engineered goats expressing the antimicrobial human lysozyme (hLZ) milk to mitigate these effects. Pigs were weaned onto a protein-energy-restricted diet and after 3 weeks were supplemented daily with goat, hLZ or no milk for a further 2 weeks and then challenged with ETEC. The restricted diet enriched faecal microbiota in Proteobacteria as seen in stunted children. Before infection, hLZ milk supplementation improved barrier function and villous height to a greater extent than goat milk. Both goat and hLZ milk enriched for taxa (Ruminococcaceae) associated with weight gain. Post-ETEC infection, pigs supplemented with hLZ milk weighed more, had improved Z-scores, longer villi and showed more stable bacterial populations during ETEC challenge than both the goat and no milk groups. This model of childhood disease was developed to test the confounding effects of malnutrition and infection and demonstrated the potential use of hLZ goat milk to mitigate the impacts of malnutrition and infection.

Young Pigs Consuming Lysozyme Transgenic Goat Milk Are Protected from Clinical Symptoms of Enterotoxigenic Escherichia coli Infection.

Background: Diarrheal diseases in infancy and childhood are responsible for substantial morbidity and mortality in developing nations. Lysozyme, an antimicrobial component of human milk, is thought to play a role in establishing a healthy intestinal microbiota and immune system. Consumption of breast milk has been shown to prevent intestinal infections and is a recommended treatment for infants with diarrhea.Objective: This study aimed to examine the ability of lysozyme-rich goat milk to prevent intestinal infection.Methods: Six-week-old Hampshire-Yorkshire pigs were assigned to treatment groups balanced for weight, sex, and litter and were fed milk from nontransgenic control goats (GM group) or human lysozyme transgenic goats (hLZM group) for 2 wk before they were challenged with porcine-specific enterotoxigenic Escherichia coli (ETEC). Fecal consistency, complete blood counts, intestinal histology, and microbial populations were evaluated.Results: Pigs in the hLZM group had less severe diarrhea than did GM pigs at 24 and 48 h after ETEC infection (P = 0.01 and 0.05, respectively), indicating a less severe clinical disease state. Relative to baseline, postmilk hLZM pigs had 19.9% and 137% enrichment in fecal Bacteroidetes (P = 0.028) and Paraprevotellaceae (P = 0.003), respectively, and a 93.8% reduction in Enterobacteriaceae (P = 0.007), whereas GM pigs had a 60.9% decrease in Lactobacillales (P = 0.003) and an 83.3% enrichment in Burkholderiales (P = 0.010). After ETEC infection, hLZM pigs tended to have lower amounts (68.7% less) of fecal Enterobacteriaceae than did GM pigs (P = 0.058). There were 83.1% fewer bacteria translocated into the mesenteric lymph nodes of hLZM pigs than into those of GM pigs (P = 0.039), and hLZM pigs had 34% lower mucin 1 and 61% higher tumor necrosis factor-α expression in the ileum than did GM pigs (P = 0.046 and 0.034, respectively).Conclusion: Results of this study indicate that human lysozyme milk consumption before and during ETEC infection has a positive effect on clinical disease, intestinal mucosa, and gut microbiota in young pigs.

SEMIPARAMETRIC EFFICIENT ESTIMATION FOR SHARED-FRAILTY MODELS WITH DOUBLY-CENSORED CLUSTERED DATA.

In this paper, we investigate frailty models for clustered survival data that are subject to both left- and right-censoring, termed "doubly-censored data". This model extends current survival literature by broadening the application of frailty models from right-censoring to a more complicated situation with additional left censoring. Our approach is motivated by a recent Hepatitis B study where the sample consists of families. We adopt a likelihood approach that aims at the nonparametric maximum likelihood estimators (NPMLE). A new algorithm is proposed, which not only works well for clustered data but also improve over existing algorithm for independent and doubly-censored data, a special case when the frailty variable is a constant equal to one. This special case is well known to be a computational challenge due to the left censoring feature of the data. The new algorithm not only resolves this challenge but also accommodate the additional frailty variable effectively. Asymptotic properties of the NPMLE are established along with semi-parametric efficiency of the NPMLE for the finite-dimensional parameters. The consistency of Bootstrap estimators for the standard errors of the NPMLE is also discussed. We conducted some simulations to illustrate the numerical performance and robustness of the proposed algorithm, which is also applied to the Hepatitis B data.

Standard error estimation using the EM algorithm for the joint modeling of survival and longitudinal data.

Joint modeling of survival and longitudinal data has been studied extensively in the recent literature. The likelihood approach is one of the most popular estimation methods employed within the joint modeling framework. Typically, the parameters are estimated using maximum likelihood, with computation performed by the expectation maximization (EM) algorithm. However, one drawback of this approach is that standard error (SE) estimates are not automatically produced when using the EM algorithm. Many different procedures have been proposed to obtain the asymptotic covariance matrix for the parameters when the number of parameters is typically small. In the joint modeling context, however, there may be an infinite-dimensional parameter, the baseline hazard function, which greatly complicates the problem, so that the existing methods cannot be readily applied. The profile likelihood and the bootstrap methods overcome the difficulty to some extent; however, they can be computationally intensive. In this paper, we propose two new methods for SE estimation using the EM algorithm that allow for more efficient computation of the SE of a subset of parametric components in a semiparametric or high-dimensional parametric model. The precision and computation time are evaluated through a thorough simulation study. We conclude with an application of our SE estimation method to analyze an HIV clinical trial dataset.

Spontaneous neural fluctuations predict decisions to attend.

Ongoing variability in neural signaling is an intrinsic property of the brain. Often this variability is considered to be noise and ignored. However, an alternative view is that this variability is fundamental to perception and cognition and may be particularly important in decision-making. Here, we show that a momentary measure of occipital alpha-band power (8-13 Hz) predicts choices about where human participants will focus spatial attention on a trial-by-trial basis. This finding provides evidence for a mechanistic account of decision-making by demonstrating that ongoing neural activity biases voluntary decisions about where to attend within a given moment.

FMEM: functional mixed effects modeling for the analysis of longitudinal white matter Tract data.

Many longitudinal imaging studies have collected repeated diffusion tensor magnetic resonance imaging data to understand white matter maturation and structural connectivity pattern in normal controls and diseased subjects. There is an urgent demand for the development of statistical methods for the analysis of diffusion properties along fiber tracts and clinical data obtained from longitudinal studies. Jointly analyzing repeated fiber-tract diffusion properties and covariates (e.g., age or gender) raises several major challenges including (i) infinite-dimensional functional response data, (ii) complex spatial-temporal correlation structure, and (iii) complex spatial smoothness. To address these challenges, this article is to develop a functional mixed effects modeling (FMEM) framework to delineate the dynamic changes of diffusion properties along major fiber tracts and their association with a set of covariates of interest and the structure of the variability of these white matter tract properties in various longitudinal studies. Our FMEM consists of a functional mixed effects model for addressing all three challenges, an efficient method for spatially smoothing varying coefficient functions, an estimation method for estimating the spatial-temporal correlation structure, a test procedure with local and global test statistics for testing hypotheses of interest associated with functional response, and a simultaneous confidence band for quantifying the uncertainty in the estimated coefficient functions. Simulated data are used to evaluate the finite sample performance of FMEM and to demonstrate that FMEM significantly outperforms the standard pointwise mixed effects modeling approach. We apply FMEM to study the spatial-temporal dynamics of white-matter fiber tracts in a clinical study of neurodevelopment.

Risk of dementia after anaesthesia and surgery.

BACKGROUND: The potential relationship between anaesthesia, surgery and onset of dementia remains elusive. AIMS: To determine whether the risk of dementia increases after surgery with anaesthesia, and to evaluate possible associations among age, mode of anaesthesia, type of surgery and risk of dementia. METHOD: The study cohort comprised patients aged 50 years and older who were anaesthetised for the first time since 1995 between 1 January 2004 and 31 December 2007, and a control group of randomly selected patients matched for age and gender. Patients were followed until 31 December 2010 to identify the emergence of dementia. RESULTS: Relative to the control group, patients who underwent anaesthesia and surgery exhibited an increased risk of dementia (hazard ratio = 1.99) and a reduced mean interval to dementia diagnosis. The risk of dementia increased in patients who received intravenous or intramuscular anaesthesia, regional anaesthesia and general anaesthesia. CONCLUSIONS: The results of our nationwide, population-based study suggest that patients who undergo anaesthesia and surgery may be at increased risk of dementia.

Network evolution of regional brain volumes in young children reflects neurocognitive scores and mother's education.

The maturation of regional brain volumes from birth to preadolescence is a critical developmental process that underlies emerging brain structural connectivity and function. Regulated by genes and environment, the coordinated growth of different brain regions plays an important role in cognitive development. Current knowledge about structural network evolution is limited, partly due to the sparse and irregular nature of most longitudinal neuroimaging data. In particular, it is unknown how factors such as mother's education or sex of the child impact the structural network evolution. To address this issue, we propose a method to construct evolving structural networks and study how the evolving connections among brain regions as reflected at the network level are related to maternal education and biological sex of the child and also how they are associated with cognitive development. Our methodology is based on applying local Fréchet regression to longitudinal neuroimaging data acquired from the RESONANCE cohort, a cohort of healthy children (245 females and 309 males) ranging in age from 9 weeks to 10 years. Our findings reveal that sustained highly coordinated volume growth across brain regions is associated with lower maternal education and lower cognitive development. This suggests that higher neurocognitive performance levels in children are associated with increased variability of regional growth patterns as children age.

Predictive effect of serial serum alanine aminotransferase levels on spontaneous HBeAg seroconversion in chronic genotype B and C HBV-infected children.

OBJECTIVE: The present study aimed to investigate the association between serial serum alanine aminotransferase (ALT) and spontaneous hepatitis B e antigen (HBeAg) seroconversion age in chronic hepatitis B virus (HBV)-infected children. PATIENTS AND METHODS: One hundred four HBeAg-positive chronic genotype B or C HBV-infected patients were included in this long-term prospective cohort study (mean initial age 7.20 years). Serial serum ALT levels and HBV serology markers were measured every 6 to 12 months. The 104 subjects made a total of 2525 visits during the study period, and the majority (93.6%) of visits were within a 1-year interval apart from previous visits. Cox proportional hazards model with time-dependent covariates was used in the survival analysis of HBeAg in these subjects. RESULTS: During the chronic course of HBV infection, the median remaining times to spontaneous HBeAg seroconversion were 8.35, 5.14, 4.25, 3.95, and 2.80 years after the ALT levels crossed 20, 30, 40, 60, and 150 IU/L, respectively. The incidence rate of spontaneous HBeAg seroconversion within 6 months when a subject entered the phase of ALT between 60 and 150 IU/L was 5.57 times that of the phase with ALT < 60 IU/L. The incidence rate of HBeAg seroconversion once ALT levels were above 150 IU/L was 9.87 times that of the phase of ALT < 60 IU/L. CONCLUSIONS: The ALT levels above 30 IU/L served as a cutoff of the inflammatory phase in chronic genotype B and C HBV-infected patients. Serial ALT levels in chronic HBV-infected subjects offer a predicted effect on the occurrence of spontaneous HBeAg seroconversion.

MODELING LEFT-TRUNCATED AND RIGHT-CENSORED SURVIVAL DATA WITH LONGITUDINAL COVARIATES.

There is a surge in medical follow-up studies that include longitudinal covariates in the modeling of survival data. So far, the focus has been largely on right censored survival data. We consider survival data that are subject to both left truncation and right censoring. Left truncation is well known to produce biased sample. The sampling bias issue has been resolved in the literature for the case which involves baseline or time-varying covariates that are observable. The problem remains open however for the important case where longitudinal covariates are present in survival models. A joint likelihood approach has been shown in the literature to provide an effective way to overcome those difficulties for right censored data, but this approach faces substantial additional challenges in the presence of left truncation. Here we thus propose an alternative likelihood to overcome these difficulties and show that the regression coefficient in the survival component can be estimated unbiasedly and efficiently. Issues about the bias for the longitudinal component are discussed. The new approach is illustrated numerically through simulations and data from a multi-center AIDS cohort study.

Mean and Covariance Estimation for Functional Snippets.

We consider estimation of mean and covariance functions of functional snippets, which are short segments of functions possibly observed irregularly on an individual specific subinterval that is much shorter than the entire study interval. Estimation of the covariance function for functional snippets is challenging since information for the far off-diagonal regions of the covariance structure is completely missing. We address this difficulty by decomposing the covariance function into a variance function component and a correlation function component. The variance function can be effectively estimated nonparametrically, while the correlation part is modeled parametrically, possibly with an increasing number of parameters, to handle the missing information in the far off-diagonal regions. Both theoretical analysis and numerical simulations suggest that this hybrid strategy is effective. In addition, we propose a new estimator for the variance of measurement errors and analyze its asymptotic properties. This estimator is required for the estimation of the variance function from noisy measurements.

Seasonal trends in Ceratitis capitata reproductive potential derived from live-caught females in Greece.

Reproductive data of individual insects are extremely hard to collect under natural conditions, thus the study of research questions related to oviposition has not advanced. Patterns of oviposition are often inferred only indirectly, through monitoring of host infestation, whereas the influence of age structure and several other factors on oviposition remains unknown. Using a new approach, in this article, we live-trapped wild Ceratitis capitata (Wiedemann) (Diptera: Tephritidae) females on the Greek island of Chios during two field seasons. For their remaining lifetime, these females were placed individually in small cages and their daily oviposition was monitored. Reproduction rates between cohorts from different collection dates were then compared. The results showed that in the different captive cohorts the average remaining lifetime and reproduction were highly variable within and between seasons. Multivariate regression analysis showed that the month of capture had a significant effect on captive life span, average daily reproduction, and patterns of egg laying. The effect of year was significant on reproduction, but not on captive life span. These differences between sampling periods probably reflect differences in the availability of hosts and other factors that vary during the season and affect age structure and reproduction. Using a non-parametric generalized additive model, we found a statistically significant correlation between the captive life span and the average daily reproduction. These findings and the experimental approach have several important implications.

Timeliness and follow-up patterns of cervical cancer detection in a cohort of medically underserved California women.

INTRODUCTION: This study examines factors associated with timely follow-up after Pap test in a program providing cervical cancer detection services to medically underserved California women. METHODS: Data between 01 January 1992 and 30 June 2007 were analyzed. Cox proportional hazard regression was used to identify subgroups of women with delayed time to diagnosis or treatment scheduling. The probability of being scheduled for final diagnosis and treatment was assessed using logistic regression analysis. Demographic and clinical characteristics of the women lost to follow up were examined. RESULTS: Time from screening to final diagnosis scheduling differed according to age group, race/ethnicity, and Pap test result. Race/ethnicity and age were associated with whether treatment was scheduled or not. While loss to follow up among those scheduled for final diagnosis was associated with certain patients' characteristics, no such association was found among those who were scheduled for treatment. CONCLUSIONS: Patient's demographic characteristics determine the odds of being scheduled for final diagnosis and treatment as well as timeliness of follow-up from screening to final diagnosis. Findings suggest that the dual goal of reducing health disparities and cost-effective detection and treatment of precancerous disease to prevent cervical cancers cannot be achieved without consideration of racial/ethnic differences and needs.

Mountaineers on Mount Everest: Effects of age, sex, experience, and crowding on rates of success and death.

Mount Everest is an extreme environment for humans. Nevertheless, hundreds of mountaineers attempt to summit Everest each year. In a previous study we analyzed interview data for all climbers (2,211) making their first attempt on Everest during 1990-2005. Probabilities of summiting were similar for men and women, declined progressively for climbers about 40 and older, but were elevated for climbers with experience climbing in Nepal. Probabilities of dying were also similar for men and women, increased for climbers about 60 and older (especially for the few that had summited), and were independent of experience. Since 2005, many more climbers (3,620) have attempted Everest. Here our primary goal is to quantify recent patterns of success and death and to evaluate changes over time. Also, we investigate whether patterns relate to key socio-demographic covariates (age, sex, host country, prior experience). Recent climbers were more diverse both in gender (women = 14.6% vs. 9.1% for 1990-2005) and in age (climbers ≥ 40 = 54.1% vs. 38.7%). Strikingly, recent climbers of both sexes were almost twice as likely to summit-and slightly less likely to die-than were comparable climbers in the previous survey. Temporal shifts may reflect improved weather forecasting, installation of fixed ropes on much of the route, and accumulative logistic equipment and experience. We add two new analyses. The probability of dying from illness or non-traumas (e.g., high-altitude illness, hypothermia), relative to dying from falling or from 'objective hazards' (avalanche, rock or ice fall), increased marginally with age. Recent crowding during summit bids was four-fold greater than in the prior sample, but surprisingly crowding has no evident effect on success or death during summit bids. Our results inform prospective climbers as to their current odds of success and of death, as well as inform governments of Nepal and China of the safety consequences and economic impacts of periodically debated restrictions based on climber age and experience.