OBJECTIVES:Osteoarthritis (OA) is associated with inflammation, chronic pain, functional limitations, and psychosocial distress. High omega-3 (n-3) polyunsaturated fatty acids (PUFAs) are associated with lower levels of inflammatory mediators, anti-nociception, and adaptive cognitive/emotional functioning. High omega-6 (n-6) PUFAs are associated with inflammation, nociception, and psychological distress. While findings related to n-3 supplementation in knee OA are mixed, consideration of the n-6:n-3 ratio and additional outcome measures may provide improved understanding of the potential relevance of these fatty acids in OA. On the basis of recommended and typical ranges of the n-6:n-3 ratio, we hypothesized that in adults with knee pain, those with a high n-6:n-3 ratio would have greater pain/functional limitations, experimental pain sensitivity, and psychosocial distress compared with those with a low n-6:n-3 ratio.MATERIALS AND METHODS:A cross-sectional investigation of clinical and experimental pain and physical and psychosocial functioning was completed in 167 adults ages 45 to 85 meeting knee OA screening criteria. Blood samples were collected and the plasma n-6:n-3 PUFA ratio determined. Quartile splits were computed and low (n=42) and high (n=41) ratio groups were compared.RESULTS:The high ratio group reported greater pain and functional limitations, (all Ps<0.04), mechanical temporal summation (hand and knee, P<0.05), and perceived stress (P=0.008) but not depressive symptoms.DISCUSSION:In adults with knee pain, a high n-6:n-3 ratio is associated with greater clinical pain/functional limitations, experimental pain sensitivity, and psychosocial distress compared with a low ratio group. Findings support consideration of the n-6:n-3 PUFA ratio and additional clinical endpoints in future research efforts.

Denote γ̂0 as the initial estimator for γ from Step 1 in the two-stage approach, and let γ̂λn be the estimator after variable selection at Step 2. In this section, we show that for a sequence of λn satisfying the conditions to be given below, the proposed estimator for γ̂λn is √ nconsistent. Furthermore, β̂λn has an oracle property that the non-zero component of β0 can be identified with probability tending to one and it is asymptotically normally distributed. Let β = (β (1), β T (2)) T , where β(1) and β(2) are the first q and the remaining p−q components of β respectively. The corresponding true value and parameter estimators from our method are (β (1),0, β T (2),0) T and (β̂ (1),λn , β̂ T (2),λn ) . Without loss of generality, we assume that for the true value β0, βj0 6= 0, j = 1, ..., q and βj0 = 0 for j = q + 1, ..., p. We assume that the following conditions hold.

BACCINI, MICHELA, MATTEI, ALESSANDRA AND MEALLI, FABRIZIA. Bayesian inference for causal mechanisms with application to a randomized study for postoperative pain control..... 605 BELOT, AURELIEN SEE DANIELI, CORALINE BENJAMINI, YOAV AND COHEN, RAMI. Weighted false discovery rate controlling procedures for clinical trials 91 BERGQUIST, SAVANNAH L. SEE ROSE, SHERRIBETH NEBEL, MARY SEE MEJIA, AMANDA F. BOCHDANOVITS, ZOLTAN SEE PECANKA, JAKUB BORDO, NADIA SEE GORFINE, MALKA BOSSARD, NADINE SEE DANIELI, CORALINE BRAUN, DANIELLE, GORFINE, MALKA, PARMIGIANI, GIOVANNI, ARVOLD, NILS D., DOMINICI, FRANCESCA AND ZIGLER, CORWIN. Propensity scores with misclassified treatment assignment: a likelihoodbased adjustment..................... 695

Introduction: Individuals with osteoarthritis (OA) show increased morbidity and mortality. Telomere length, a measure of cellular aging, predicts increased morbidity and mortality. Telomeres shorten with persisting biological and psychosocial stress. Living with chronic OA pain is stressful. Previous research exploring telomere length in people with OA has produced inconsistent results. Considering pain severity may clarify the relationship between OA and telomeres.Objectives: We hypothesized that individuals with high OA chronic pain severity would have shorter telomeres than those with no or low chronic pain severity.Methods: One hundred thirty-six adults, ages 45 to 85 years old, with and without symptomatic knee OA were included in the analysis. Peripheral blood leukocyte telomere length was measured, and demographic, clinical, and functional data were collected. Participants were categorized into 5 pain severity groups based on an additive index of frequency, intensity, time or duration, and total number of pain sites (FITT). Covariates included age, sex, race or ethnicity, study site, and knee pain status.Results: The no or low chronic pain severity group had significantly longer telomeres compared with the high pain severity group, P = 0.025. A significant chronic pain severity dose response emerged for telomere length, P = 0.034. The FITT chronic pain severity index was highly correlated with the clinical and functional OA pain measures. However, individual clinical and functional measures were not associated with telomere length.Conclusion: Results demonstrate accelerated cellular aging with high knee OA chronic pain severity and provide evidence for the potential utility of the FITT chronic pain severity index in capturing the biological burden of chronic pain.

Precise modeling of disease progression in neurodegenerative disorders may enable early intervention before clinical manifestation of a disease, which is crucial since early intervention at the premanifest stage is expected to be more effective. Neuroimaging biomarkers are indicative of the underlying disease pathology and may be used to predict future disease occurrence at the premanifest stage. As observed in many pivotal studies, longitudinal measurements of clinical outcomes, such as motor or cognitive symptoms, often present nonlinear sigmoid shapes over time, where the inflection points of the trajectories mark a meaningful time in disease progression. Therefore, to identify neuroimaging biomarkers predicting disease progression, we propose a nonlinear mixed effects model based on a sigmoid function to predict longitudinal clinical outcomes, and associate a linear combination of neuroimaging biomarkers with subject-specific inflection points. Based on an expectation-maximization (EM) algorithm, we propose a method that can fit a nonlinear model with many potentially correlated biomarkers for random inflection points while achieving computational stability. Variable selection is introduced in the algorithm in order to identify important biomarkers of disease progression and to reduce prediction variability. We apply the proposed method to the data from the Predictors of Huntington's Disease study to select brain subcortical regional volumes predictive of the inflection points of the motor and cognitive function trajectories. Our results reveal that brain atrophy in the striatum and expansion of the ventricular system are highly predictive of the inflection points. Furthermore, these inflection points may precede clinically defined disease onset by as early as a decade and thus may be useful biomarkers as early signs of Huntington's Disease onset.

Univariate semiparametric methods are often used in modeling nonlinear age trajectories for imaging data, which may result in efficiency loss and lower power for identifying important age-related effects that exist in the data. As observed in multiple neuroimaging studies, age trajectories show similar nonlinear patterns for the left and right corresponding regions and for the different parts of a big organ such as the corpus callosum. To incorporate the spatial similarity information without assuming spatial smoothness, we propose a multivariate semiparametric regression model with a spatial similarity penalty, which constrains the variation of the age trajectories among similar regions. The proposed method is applicable to both cross-sectional and longitudinal region-level imaging data. We show the asymptotic rates for the bias and covariance functions of the proposed estimator and its asymptotic normality. Our simulation studies demonstrate that by borrowing information from similar regions, the proposed spatial similarity method improves the efficiency remarkably. We apply the proposed method to two neuroimaging data examples. The results reveal that accounting for the spatial similarity leads to more accurate estimators and better functional clustering results for visualizing brain atrophy pattern.Functional clustering; Longitudinal magnetic resonance imaging (MRI); Penalized B-splines; Region of interest (ROI); Spatial penalty.

Introduction: As individuals age, there is an increased focus on social relationships (Carstensen, 2006). However, age-related changes in the brain can interfere with social functioning (Ebner, et ...

Palliative medicine is an interdisciplinary specialty focusing on improving quality of life (QOL) for patients with serious illness and their families. Palliative care programs are available or under development at over 80% of large US hospitals (300+ beds). Palliative care clinical trials present unique analytic challenges relative to evaluating the palliative care treatment efficacy which is to improve patients' diminishing QOL as disease progresses towards end of life (EOL). A unique feature of palliative care clinical trials is that patients will experience decreasing QOL during the trial despite potentially beneficial treatment. Often longitudinal QOL and survival data are highly correlated which, in the face of censoring, makes it challenging to properly analyze and interpret terminal QOL trend. To address these issues, we propose a novel semiparametric statistical approach to jointly model the terminal trend of QOL and survival data. There are two sub-models in our approach: a semiparametric mixed effects model for longitudinal QOL and a Cox model for survival. We use regression splines method to estimate the nonparametric curves and AIC to select knots. We assess the model performance through simulation to establish a novel modeling approach that could be used in future palliative care research trials. Application of our approach in a recently completed palliative care clinical trial is also presented.

BACKGROUND: Gamma-aminobutyric acid (GABA), the brain's principal inhibitory neurotransmitter, has been associated with perceptual and attentional functioning. Recent application of magnetic resonance spectroscopy (MRS) provides in vivo evidence for decreasing GABA concentrations during adulthood. It is unclear, however, how age-related decrements in cerebral GABA concentrations contribute to cognitive decline, or whether previously reported declines in cerebral GABA concentrations persist during healthy aging. We hypothesized that participants with higher GABA concentrations in the frontal cortex would exhibit superior cognitive function and that previously reported age-related decreases in cortical GABA concentrations continue into old age.METHODS: We measured GABA concentrations in frontal and posterior midline cerebral regions using a Mescher-Garwood point-resolved spectroscopy (MEGA-PRESS) H-1-MRS approach in 94 older adults without history or clinical evidence of mild cognitive impairment or dementia (mean age, 73 years). We administered the Montreal Cognitive Assessment to assess cognitive functioning.RESULTS: Greater frontal GABA concentrations were associated with superior cognitive performance. This relation remained significant after controlling for age, years of education, and brain atrophy. GABA concentrations in both frontal and posterior regions decreased as a function of age.CONCLUSIONS: These novel findings from a large, healthy, older population indicate that cognitive function is sensitive to cerebral GABA concentrations in the frontal cortex, and GABA concentration in frontal and posterior regions continue to decline in later age. These effects suggest that proton MRS may provide a clinically useful method for the assessment of normal and abnormal age-related cognitive changes and the associated physiological contributors.

We extend the notion of an influence or hat matrix to regression with functional responses and scalar predictors. For responses depending linearly on a set of predictors, our definition is shown to reduce to the conventional influence matrix for linear models. The pointwise degrees of freedom, the trace of the pointwise influence matrix, are shown to have an adaptivity property that motivates a two-step bivariate smoother for modeling nonlinear dependence on a single predictor. This procedure adapts to varying complexity of the nonlinear model at different locations along the function, and thereby achieves better performance than competing tensor product smoothers in an analysis of the development of white matter microstructure in the brain.

Cancer and its treatment are frequently associated with cancer-related cognitive impairment (CRCI). While CRCI has been associated with linked to chemotherapy, there is increasing evidence that the condition may start prior to treatment and for some, remain unresolved after active treatment and into survivorship. Although the pathophysiology of the condition is complex, alterations in systemic cytokines, signaling molecules activated in response to infection or injury that trigger inflammation, are a possible mechanism linked to cognitive dysfunction in breast cancer and other conditions. Given the conflicting results in the literature, the lack of focus on domain specific cognitive testing, and the need for a longer time period given the multiple modalities of standard treatments for early-stage breast cancer, this longitudinal study was conducted to address these gaps. Methods: We assessed 75 women with early-stage breast cancer at five points over two years, starting prior to the initial chemotherapy through 24 months after chemotherapy initiation. Measures included a validated computerized evaluation of domain-specific cognitive functioning and a 17-plex panel of plasma cytokines. Linear mixed-effects models were applied to test the relationships of clinical variables and cytokine concentrations to each cognitive domain.Results: Levels and patterns of cytokine concentrations varied over time: six of the 17 cytokines IL-12,IL-17, G-CSF, M1PS-1 beta, and MCP-1) had the most variability. Some cytokine levels (e.g., IL-6) increased during chemotherapy but then decreased subsequently, while others (e.g., IL-17) consistently declined from baseline over time. There were multiple relationships among cytokines and cognition, which varied over time. At baseline, elevated concentrations of G-CSF and reduced concentrations of IL-17 were associated with faster psychomotor speed. At the second time-point (prior to the mid-chemotherapy), multiple cytokines had significant associations with psychomotor speed, complex attention, executive function, verbal memory, cognitive flexibility, composite memory and visual memory. Six months after chemotherapy initiation and at the one-year point, there were multiple, significant relationships among cytokines and multiple cognitive. At two years, fewer significant relationships were noted; however, lower concentrations of IL-7, a hematopoietic cytokine, were associated with better psychomotor speed, complex attention, and memory (composite, verbal and visual). MCP-1 was inversely associated with psychomotor speed and complex attention and higher levels of MIP-1 beta were related to better complex attention.Conclusion: Levels and patterns of cytokines changed over time and demonstrated associations with domain-specific cognitive functioning that varied over time. The observed associations between cytokines and cognitive performance provides evidence that not only prototypical cytokines (i.e., IL-6, TNF-alpha, and IL1-beta but also cytokines from multiple classes may contribute to the inflammatory environment that is associated with cognitive dysfunction. Future studies to better delineate the cytokine changes, both individually and in networks, are needed to precisely assess a mechanistic link between cytokines and cognitive function in women receiving treatments for breast cancer. (C) 2016 Elsevier B.V. All rights reserved.

The neuropeptide oxytocin plays a role in social cognition and affective processing. The neural processes underlying these effects are not well understood. Modulation of connectivity strength between subcortical and cortical regions has been suggested as one possible mechanism. The current study investigated effects of intranasal oxytocin administration on resting-state functional connectivity between amygdala and medial prefrontal cortex (mPFC), as two regions involved in social-cognitive and affective processing. Going beyond previous work that largely examined young male participants, our study comprised young and older men and women to identify age and sex variations in oxytocin's central processes. This approach was based on known hormonal differences among these groups and emerging evidence of sex differences in oxytocin's effects on amygdala reactivity and age-by-sex-modulated effects of oxytocin in affective processing. In a double-blind design, 79 participants were randomly assigned to self-administer either intranasal oxytocin or placebo before undergoing resting-state functional magnetic resonance imaging. Using a targeted region-to-region approach, resting-state functional connectivity strength between bilateral amygdala and mPFC was examined. Participants in the oxytocin compared to the placebo group and men compared to women had overall greater amygdala-mPFC connectivity strength at rest. These main effects were qualified by a significant three-way interaction: while oxytocin compared to placebo administration increased resting-state amygdala-mPFC connectivity for young women, oxytocin did not significantly influence connectivity in the other age-by-sex subgroups. This study provides novel evidence of age-by-sex differences in how oxytocin modulates resting-state brain connectivity, furthering our understanding of how oxytocin affects brain networks at rest. (C) 2016 Elsevier Ltd. All rights reserved.

Both HIV disease and advanced age have been associated with alterations to cerebral white matter, as measured with white matter hyperintensities (WMH) on fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI), and more recently with diffusion tensor imaging (DTI). This study investigates the combined effects of age and HIV serostatus on WMH and DTI measures, as well as the relationships between these white matter measures, in 88 HIV seropositive (HIV+) and 49 seronegative (HIV−) individuals aged 23–79 years. A whole-brain volumetric measure of WMH was quantified from FLAIR images using a semi-automated process, while fractional anisotropy (FA) was calculated for 15 regions of a whole-brain white matter skeleton generated using tract-based spatial statistics (TBSS). An age by HIV interaction was found indicating a significant association between WMH and older age in HIV+ participants only. Similarly, significant age by HIV interactions were found indicating stronger associations between older age and decreased FA in the posterior limbs of the internal capsules, cerebral peduncles, and anterior corona radiata in HIV+ vs. HIV− participants. The interactive effects of HIV and age were stronger with respect to whole-brain WMH than for any of the FA measures. Among HIV+ participants, greater WMH and lower anterior corona radiata FA were associated with active hepatitis C virus infection, a history of AIDS, and higher current CD4 cell count. Results indicate that age exacerbates HIV-associated abnormalities of whole-brain WMH and fronto-subcortical white matter integrity.

A unique feature of end-of-life (EOL) research studies, such as hospice/palliative care studies, is the short life expectancy of participants which makes it important to study the terminal trajectory of longitudinal data in these studies. Survival data are also commonly seen in such studies and strongly correlated with longitudinal data. Without appropriate handling of the correlation, estimate for longitudinal trajectory will be inefficient with distorted clinical interpretation. Unfortunately, censoring of the survival times occurs frequently and makes it challenging to account for the correlation. To address these issues, we propose a novel semiparametric statistical approach for jointly modeling longitudinal and survival data in EOL studies. There are two sub-models in our joint modeling approach: a semiparametric mixed effects model for the longitudinal data and a Cox model for the survival data. Regression splines method with natural cubic B-splines are used to estimate the nonparametric curves and AIC is employed to select knots. Inference for quality-adjusted life years is provided as well using this approach. Performance of the model is assessed through simulation and we also apply the model to a recently completed randomized clinical trial in palliative care research to establish a standard modeling approach in the field of EOL research.

Cognitive dysfunction in women with breast cancer continues to be an area of intense research interest. The prevalence, severity, timing, and cognitive domains that are most affected, as well as the contribution of cancer and its treatments to cognition, remain unresolved. Thus, longitudinal studies are needed that examine cognitive function during different stages of breast cancer treatment and survivorship. This longitudinal trial followed women with early-stage breast cancer, prior to chemotherapy through 2 years survivorship.

Neuroimaging studies of cognitive and brain aging often yield massive datasets that create many analytic and statistical challenges. In this paper, we discuss and address several limitations in the existing work. (1) Linear models are often used to model the age effects on neuroimaging markers, which may be inadequate in capturing the potential nonlinear age effects. (2) Marginal correlations are often used in brain network analysis, which are not efficient in characterizing a complex brain network. (3) Due to the challenge of high-dimensionality, only a small subset of the regional neuroimaging markers is considered in a prediction model, which could miss important regional markers. To overcome those obstacles, we introduce several advanced statistical methods for analyzing data from cognitive and brain aging studies. Specifically, we introduce semiparametric models for modeling age effects, graphical models for brain network analysis, and penalized regression methods for selecting the most important markers in predicting cognitive outcomes. We illustrate these methods using the healthy aging data from the Active Brain Study.

Resting‐state functional magnetic resonance image is a useful technique for investigating brain functional connectivity at rest. In this work, we develop flexible regression models and methods for determining differences in resting‐state functional connectivity as a function of age, gender, drug intervention, or neuropsychiatric disorders. We propose two complementary methods for identifying changes of edges and subgraphs. (i) For detecting changes of edges, we select the optimal model at each edge and then conduct contrast tests to identify the effects of the important variables while controlling the familywise error rate. (ii) We adopt the network‐based statistics method to improve power by incorporating the graph topological structure. Both methods have wide applications for low signal‐to‐noise ratio data. We propose stability criteria for the choice of threshold in the network‐based statistics procedure and utilize efficient massive parallel procedure to speed up the estimation and inference procedure. Results from our simulation studies show that the thresholds chosen by the proposed stability criteria outperform the Bonferroni threshold. To demonstrate applicability, we use both methods in the context of the Oxytocin and Aging Study to determine effects of age, gender, and drug treatment on resting‐state functional connectivity, as well as in the context of the Autism Brain Imaging Data Exchange Study to determine effects of autism spectrum disorder on functional connectivity at rest. Copyright © 2016 John Wiley & Sons, Ltd.

Background . Chronic pain is associated with increased morbidity and mortality, predominated by cardiovascular disease and cancer. Investigating related risk factor measures may elucidate the biological burden of chronic pain. Objectives . We hypothesized that chronic pain severity would be positively associated with the risk factor composite. Methods . Data from 12,982 participants in the 6th Tromsø study were analyzed. Questionnaires included demographics, health behaviors, medical comorbidities, and chronic pain symptoms. The risk factor composite was comprised of body mass index, fibrinogen, C-reactive protein, and triglycerides. Chronic pain severity was characterized by frequency, intensity, time/duration, and total number of pain sites. Results . Individuals with chronic pain had a greater risk factor composite than individuals without chronic pain controlling for covariates and after excluding inflammation-related health conditions (p<0.001). A significant “dose-response” relationship was demonstrated with pain severity (p<0.001). In individuals with chronic pain, the risk factor composite varied by health behavior, exercise, lower levels and smoking, and higher levels. Discussion . The risk factor composite was higher in individuals with chronic pain, greater with increasing pain severity, and influenced by health behaviors. Conclusions . Identification of a biological composite sensitive to pain severity and adaptive/maladaptive behaviors would have significant clinical and research utility.

We propose a novel method for neurodevelopmental brain mapping that displays how an individual's values for a quantity of interest compare with age-specific norms. By estimating smoothly age-varying distributions at a set of brain regions of interest, we derive age-dependent region-wise quantile ranks for a given individual, which can be presented in the form of a brain map. Such quantile rank maps could potentially be used for clinical screening. Bootstrap-based confidence intervals are proposed for the quantile rank estimates. We also propose a recalibrated Kolmogorov-Smirnov test for detecting group differences in the age-varying distribution. This test is shown to be more robust to model misspecification than a linear regression-based test. The proposed methods are applied to brain imaging data from the Nathan Kline Institute Rockland Sample and from the Autism Brain Imaging Data Exchange (ABIDE) sample.

BACKGROUND:Antagonists of N-methyl-D-aspartate-type glutamate receptors (NMDAR) induce symptoms that closely resemble those of schizophrenia, including negative symptoms. D-serine is a naturally occurring NMDAR modulator that reverses the effects of NMDAR antagonists in animal models of schizophrenia. D-serine effects have been assessed previously for treatment of established schizophrenia, but not in the early stages of the disorder. We aimed to assess effects of D-serine on negative symptoms in at risk individuals. METHODS:We did a double-blind, placebo-controlled, parallel-group randomised clinical trial at four academic US centres. Individuals were eligible for inclusion in the study if they were at clinical high risk of schizophrenia, aged between 13-35 years, had a total score of more than 20 on the Scale of Prodromal Symptoms (SOPS), and had an interest in participation in the clinical trial. Exclusion criteria included a history of suprathreshold psychosis symptoms (ie, no longer qualifying as prodromal) or clinical judgment that the reported symptoms from the SOPS were accounted for better by another disorder (eg, depression). Randomisation was done using a generated list with block sizes of four. Participants were stratified by site, with participants, investigators, and assessors all masked through use of identical looking placebos and centralised drug dispensation to study assignment. D-serine (60 mg/kg) was given orally in divided daily doses for 16 weeks. The primary endpoint was for negative SOPS, measured weekly for the first 6 weeks, then every 2 weeks. Participants who received at least one post-baseline assessment were included in analysis. Serum cytokine concentrations were collected at baseline, midpoint, and endpoint to assess the mechanism of action. Safety outcomes including laboratory assessments were obtained for all individuals. This trial is registered with ClinicalTrials.gov, number NCT0082620. FINDINGS:We enrolled participants between April 2, 2009, and July 23, 2012. 44 participants were randomly assigned to receive either D-serine (n=20) or placebo (n=24); 35 had assessable data (15 D-serine, 20 placebo). D-serine induced a 35·7% (SD 17·8) improvement in negative symptoms, which was significant compared with placebo (mean final SOPS negative score 7·6 [SEM 1·4] for D-serine group vs 11·3 [1·2] for placebo group; d=0·68, p=0·03). Five participants who received D-serine and nine participants who received placebo discontinued the study early because of withdrawn consent or loss to follow-up (n=8), conversion to psychosis (n=2), laboratory-confirmed adverse events (n=2), or protocol deviations (n=2). INTERPRETATION:This study supports use of NMDAR-based interventions, such as D-serine, for treatment of prodromal symptoms of schizophrenia. On the basis of observed effect sizes, future studies with sample sizes of about 40 per treatment group would be needed for confirmation of beneficial effects on symptoms and NMDAR-related inflammatory changes. Long-term studies are needed to assess effects on psychosis conversion in individuals at clinical high risk of schizophrenia. FUNDING:National Institutes of Health.