@article {797, title = {Biomarkers of vascular calcification and mortality in patients with ESRD.}, journal = {Clin J Am Soc Nephrol}, volume = {9}, year = {2014}, month = {2014 Apr}, pages = {745-55}, abstract = {

BACKGROUND: Vascular calcification is common among patients undergoing dialysis and is associated with mortality. Factors such as osteoprotegerin (OPG), osteopontin (OPN), bone morphogenic protein-7 (BMP-7), and fetuin-A are involved in vascular calcification.

DESIGN, SETTING, PARTICIPANTS, \& MEASUREMENTS: OPG, OPN, BMP-7, and fetuin-A were measured in blood samples from 602 incident dialysis patients recruited from United States dialysis centers between 1995 and 1998 as part of the Choices for Healthy Outcomes In Caring for ESRD Study. Their association with all-cause and cardiovascular mortality were assessed using Cox proportional hazards models adjusted for demographic characteristics, comorbidity, serum phosphate, and calcium. An interaction with diabetes was tested because of its known association with vascular calcification. Predictive accuracy of selected biomarkers was explored by C-statistics in nested models with training and validation subcohorts.

RESULTS: Higher OPG and lower fetuin-A levels were associated with higher mortality over up to 13 years of follow-up (median, 3.4 years). The adjusted hazard ratios (HR) for highest versus lowest tertile were 1.49 (95\% confidence interval [95\% CI], 1.08 to 2.06) for OPG and 0.69 (95\% CI, 0.52 to 0.92) for fetuin-A. In stratified models, the highest tertile of OPG was associated with higher mortality among patients without diabetes (HR, 2.42; 95\% CI, 1.35 to 4.34), but not patients with diabetes (HR, 1.26; 95\% CI, 0.82 to 1.93; P for interaction=0.001). In terms of cardiovascular mortality, higher fetuin-A was associated with lower risk (HR, 0.85 per 0.1 g/L: 95\% CI, 0.75 to 0.96). In patients without diabetes, higher OPG was associated with greater risk (HR for highest versus lowest tertile, 2.91; 95\% CI, 1.06 to 7.99), but not in patients with diabetes or overall. OPN and BMP-7 were not independently associated with outcomes overall. The addition of OPG and fetuin-A did not significantly improve predictive accuracy of mortality.

CONCLUSIONS: OPG and fetuin-A may be risk factors for all-cause and cardiovascular mortality in patients undergoing dialysis, but do not improve risk prediction.

}, issn = {1555-905X}, doi = {10.2215/CJN.05450513}, author = {Scialla, Julia J and Kao, W H Linda and Crainiceanu, Ciprian and Sozio, Stephen M and Oberai, Pooja C and Shafi, Tariq and Coresh, Josef and Powe, Neil R and Plantinga, Laura C and Jaar, Bernard G and Parekh, Rulan S} } @article {836, title = {Brain mediators of the effects of noxious heat on pain.}, journal = {Pain}, volume = {155}, year = {2014}, month = {2014 Aug}, pages = {1632-48}, abstract = {

Recent human neuroimaging studies have investigated the neural correlates of either noxious stimulus intensity or reported pain. Although useful, analyzing brain relationships with stimulus intensity and behavior separately does not address how sensation and pain are linked in the central nervous system. In this study, we used multi-level mediation analysis to identify brain mediators of pain--regions in which trial-by-trial responses to heat explained variability in the relationship between noxious stimulus intensity (across 4 levels) and pain. This approach has the potential to identify multiple circuits with complementary roles in pain genesis. Brain mediators of noxious heat effects on pain included targets of ascending nociceptive pathways (anterior cingulate, insula, SII, and medial thalamus) and also prefrontal and subcortical regions not associated with nociceptive pathways per se. Cluster analysis revealed that mediators were grouped into several distinct functional networks, including the following: somatosensory, paralimbic, and striatal-cerebellar networks that increased with stimulus intensity; and 2 networks co-localized with "default mode" regions in which stimulus intensity-related decreases mediated increased pain. We also identified "thermosensory" regions that responded to increasing noxious heat but did not predict pain reports. Finally, several regions did not respond to noxious input, but their activity predicted pain; these included ventromedial prefrontal cortex, dorsolateral prefrontal cortex, cerebellar regions, and supplementary motor cortices. These regions likely underlie both nociceptive and non-nociceptive processes that contribute to pain, such as attention and decision-making processes. Overall, these results elucidate how multiple distinct brain systems jointly contribute to the central generation of pain.

}, issn = {1872-6623}, doi = {10.1016/j.pain.2014.05.015}, author = {Atlas, Lauren Y and Lindquist, Martin A and Bolger, Niall and Wager, Tor D} } @article {807, title = {Bayesian scalar-on-image regression with application to association between intracranial DTI and cognitive outcomes.}, journal = {Neuroimage}, volume = {83}, year = {2013}, month = {2013 Dec}, pages = {210-23}, abstract = {

Diffusion tensor imaging (DTI) measures water diffusion within white matter, allowing for in vivo quantification of brain pathways. These pathways often subserve specific functions, and impairment of those functions is often associated with imaging abnormalities. As a method for predicting clinical disability from DTI images, we propose a hierarchical Bayesian "scalar-on-image" regression procedure. Our procedure introduces a latent binary map that estimates the locations of predictive voxels and penalizes the magnitude of effect sizes in these voxels, thereby resolving the ill-posed nature of the problem. By inducing a spatial prior structure, the procedure yields a sparse association map that also maintains spatial continuity of predictive regions. The method is demonstrated on a simulation study and on a study of association between fractional anisotropy and cognitive disability in a cross-sectional sample of 135 multiple sclerosis patients.

}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2013.06.020}, author = {Huang, Lei and Goldsmith, Jeff and Reiss, Philip T and Reich, Daniel S and Crainiceanu, Ciprian M} } @article {799, title = {Body composition and arsenic metabolism: a cross-sectional analysis in the Strong Heart Study.}, journal = {Environ Health}, volume = {12}, year = {2013}, month = {2013}, pages = {107}, abstract = {

OBJECTIVE: The objective of this study was to evaluate the association between measures of body composition and patterns of urine arsenic metabolites in the 1989-1991 baseline visit of the Strong Heart Study, a cardiovascular disease cohort of adults recruited from rural communities in Arizona, Oklahoma, North Dakota and South Dakota.

METHODS: We evaluated 3,663 Strong Heart Study participants with urine arsenic species above the limit of detection and no missing data on body mass index, \% body fat and fat free mass measured by bioelectrical impedance, waist circumference and other variables. We summarized urine arsenic species patterns as the relative contribution of inorganic (iAs), methylarsonate (MMA) and dimethylarsinate (DMA) species to their sum. We modeled the associations of \% arsenic species biomarkers with body mass index, \% body fat, fat free mass, and waist circumference categories in unadjusted regression models and in models including all measures of body composition. We also considered adjustment for arsenic exposure and demographics.

RESULTS: Increasing body mass index was associated with higher mean \% DMA and lower mean \% MMA before and after adjustment for sociodemographic variables, arsenic exposure, and for other measures of body composition. In unadjusted linear regression models, \% DMA was 2.4 (2.1, 2.6) \% higher per increase in body mass index category (< 25, >=25 \& <30, >=30 \& <35, >=35 kg/m2), and \% MMA was 1.6 (1.4, 1.7) \% lower. Similar patterns were observed for \% body fat, fat free mass, and waist circumference measures in unadjusted models and in models adjusted for potential confounders, but the associations were largely attenuated or disappeared when adjusted for body mass index.

CONCLUSION: Measures of body size, especially body mass index, are associated with arsenic metabolism biomarkers. The association may be related to adiposity, fat free mass or body size. Future epidemiologic studies of arsenic should consider body mass index as a potential modifier for arsenic-related health effects.

}, issn = {1476-069X}, doi = {10.1186/1476-069X-12-107}, author = {Gribble, Matthew O and Crainiceanu, Ciprian M and Howard, Barbara V and Umans, Jason G and Francesconi, Kevin A and Goessler, Walter and Zhang, Ying and Silbergeld, Ellen K and Guallar, Eliseo and Navas-Acien, Ana} } @article {landmanb, title = {Biological parametric mapping accounting for random regressors with regression calibration and model II regression}, journal = {Accepted in Neuroimage}, year = {2012}, author = {Yang, X and Lauzon, C and Crainiceanu, C and Caffo, B and Resnick, S and Landman, B} } @article {679, title = {Bootstrap-based inference on the difference in the means of two correlated functional processes.}, journal = {Stat Med}, year = {2012}, month = {2012 Aug 1}, abstract = {

We propose nonparametric inference methods on the mean difference between two correlated functional processes. We compare methods that (1) incorporate different levels of smoothing of the mean and covariance; (2) preserve the sampling design; and (3) use parametric and nonparametric estimation of the mean functions. We apply our method to estimating the mean difference between average normalized δ power of sleep electroencephalograms for 51 subjects with severe sleep apnea and 51 matched controls in the first 4\ ;h after sleep onset. We obtain data from the Sleep Heart Health Study, the largest community cohort study of sleep. Although methods are applied to a single case study, they can be applied to a large number of studies that have correlated functional data. Copyright {\textcopyright} 2012 John Wiley \& Sons, Ltd.

}, issn = {1097-0258}, doi = {10.1002/sim.5439}, author = {Crainiceanu, C and Staicu, A. M. and Ray, Shubankar and Punjabi, Naresh} } @article {springerlink:10.1007/s10928-011-9211-7, title = {A Bayesian hierarchical nonlinear mixture model in the presence of artifactual outliers in a population pharmacokinetic study}, journal = {Journal of Pharmacokinetics and Pharmacodynamics}, volume = {38}, year = {2011}, note = {10.1007/s10928-011-9211-7}, pages = {613-636}, publisher = {Springer Netherlands}, issn = {1567-567X}, url = {http://dx.doi.org/10.1007/s10928-011-9211-7}, author = {Choi, L and Caffo, B and Kohli, U and Pandharipande, P and Kurnik, D and Ely, E and Stein, C} } @article {pmid21743798, title = {Bayesian Functional Data Analysis Using WinBUGS}, journal = {J Stat Softw}, volume = {32}, number = {11}, year = {2010}, month = {Jan}, author = {Crainiceanu, C and Goldsmith, J} } @article {atlas, title = {Brain mediators of predictive cue effects on perceived pain}, journal = {J Neurosci}, volume = {30}, year = {2010}, pages = {12964-77}, author = {Atlas, L.Y. and Bolger, N. and Lindquist, Martin A and Wager, T.D.} } @article {846, title = {Brain mediators of predictive cue effects on perceived pain.}, journal = {J Neurosci}, volume = {30}, year = {2010}, month = {2010 Sep 29}, pages = {12964-77}, abstract = {

Information about upcoming pain strongly influences pain experience in experimental and clinical settings, but little is known about the brain mechanisms that link expectation and experience. To identify the pathways by which informational cues influence perception, analyses must jointly consider both the effects of cues on brain responses and the relationship between brain responses and changes in reported experience. Our task and analysis strategy were designed to test these relationships. Auditory cues elicited expectations for barely painful or highly painful thermal stimulation, and we assessed how cues influenced human subjects{\textquoteright} pain reports and brain responses to matched levels of noxious heat using functional magnetic resonance imaging. We used multilevel mediation analysis to identify brain regions that (1) are modulated by predictive cues, (2) predict trial-to-trial variations in pain reports, and (3) formally mediate the relationship between cues and reported pain. Cues influenced heat-evoked responses in most canonical pain-processing regions, including both medial and lateral pain pathways. Effects on several regions correlated with pretask expectations, suggesting that expectancy plays a prominent role. A subset of pain-processing regions, including anterior cingulate cortex, anterior insula, and thalamus, formally mediated cue effects on pain. Effects on these regions were in turn mediated by cue-evoked anticipatory activity in the medial orbitofrontal cortex (OFC) and ventral striatum, areas not previously directly implicated in nociception. These results suggest that activity in pain-processing regions reflects a combination of nociceptive input and top-down information related to expectations, and that anticipatory processes in OFC and striatum may play a key role in modulating pain processing.

}, keywords = {Adult, Brain, Cognition, Cues, Female, Hot Temperature, Humans, Male, Pain, Pain Threshold, Perception, Physical Stimulation}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.0057-10.2010}, author = {Atlas, Lauren Y and Bolger, Niall and Lindquist, Martin A and Wager, Tor D} } @article {MishchenkoPaninski09, title = {A Bayesian approach for inferring neuronal connectivity from calcium fluorescent imaging data}, journal = {Annals of Applied Statistics}, volume = {in press}, year = {2009}, author = {Mishchenko, Yuriy and Vogelstein, Joshua T and Paninski, Liam} } @article {Luo:Crai:Loui:Chat:baye:2009, title = {Bayesian Inference for Smoking Cessation with a Latent Cure State}, journal = {Biometrics}, volume = {65}, number = {3}, year = {2009}, pages = {970{\textendash}978}, keywords = {cure model, MCMC, Mixed-effects model, prediction, Recurrent events, Smoking cessation}, author = {Luo, Sheng and Crainiceanu, C and Louis, Thomas A. and Chatterjee, Nilanjan} } @article {Wager09a, title = {Brain mediators of cardiovascular responses to social threat, Part I: Reciprocal dorsal and ventral sub-regions of the medial prefrontal cortex and heart-rate reactivity}, journal = {NeuroImage}, volume = {47}, year = {2009}, pages = {821-835}, author = {Wager, T.D. and Waugh, C.E. and Lindquist, Martin A and Noll, D.C. and Fredrickson, B.L. and Taylor, S.F.} } @article {Wager09b, title = {Brain mediators of cardiovascular responses to social threat, Part II: Prefrontal subcortical pathways and relationship with anxiety}, journal = {NeuroImage}, volume = {47}, year = {2009}, pages = {836-851}, author = {Wager, T.D. and van Ast, V. and Davidson, M.L. and Lindquist, Martin A and Ochsner, K.N.} } @article {bowman2008bayesian, title = {A Bayesian hierarchical framework for spatial modeling of fMRI data}, journal = {NeuroImage, PMCID: PMC2134321}, volume = {39}, number = {1}, year = {2008}, pages = {146{\textendash}156}, publisher = {Elsevier}, author = {Bowman, F and Caffo, B and Bassett, S and Kilts, C} } @article {Crai:Digg:Rowl:biva:2008, title = {Bivariate Binomial Spatial Modeling of Loa Loa Prevalence in Tropical Africa}, journal = {Journal of the American Statistical Association}, volume = {103}, number = {481}, year = {2008}, pages = {21{\textendash}37}, keywords = {Geostatistics, low rank, thin-plate splines}, author = {Crainiceanu, C and Diggle, Peter J. and Rowlingson, Barry} }