@article {reiss2012quantile, title = {Smoothness Selection for Penalized Quantile Regression Splines}, journal = {International Journal of Biostatistics} year={2012}, year = {Submitted}, note = {To appear}, keywords = {keyword-test1}, author = {Philip T. Reiss and Lei Huang} } @article {834, title = {Separate neural representations for physical pain and social rejection.}, journal = {Nat Commun}, volume = {5}, year = {2014}, month = {2014}, pages = {5380}, abstract = {

Current theories suggest that physical pain and social rejection share common neural mechanisms, largely by virtue of overlapping functional magnetic resonance imaging (fMRI) activity. Here we challenge this notion by identifying distinct multivariate fMRI patterns unique to pain and rejection. Sixty participants experience painful heat and warmth and view photos of ex-partners and friends on separate trials. FMRI pattern classifiers discriminate pain and rejection from their respective control conditions in out-of-sample individuals with 92\% and 80\% accuracy. The rejection classifier performs at chance on pain, and vice versa. Pain- and rejection-related representations are uncorrelated within regions thought to encode pain affect (for example, dorsal anterior cingulate) and show distinct functional connectivity with other regions in a separate resting-state data set (N=91). These findings demonstrate that separate representations underlie pain and rejection despite common fMRI activity at the gross anatomical level. Rather than co-opting pain circuitry, rejection involves distinct affective representations in humans.

}, issn = {2041-1723}, doi = {10.1038/ncomms6380}, author = {Woo, Choong-Wan and Koban, Leonie and Kross, Ethan and Lindquist, Martin A and Banich, Marie T and Ruzic, Luka and Andrews-Hanna, Jessica R and Wager, Tor D} } @article {792, title = {Shrinkage prediction of seed-voxel brain connectivity using resting state fMRI.}, journal = {Neuroimage}, volume = {102 Pt 2}, year = {2014}, month = {2014 Nov 15}, pages = {938-44}, abstract = {

Resting-state functional magnetic resonance imaging (rs-fMRI) is used to investigate synchronous activations in spatially distinct regions of the brain, which are thought to reflect functional systems supporting cognitive processes. Analyses are often performed using seed-based correlation analysis, allowing researchers to explore functional connectivity between data in a seed region and the rest of the brain. Using scan-rescan rs-fMRI data, we investigate how well the subject-specific seed-based correlation map from the second replication of the study can be predicted using data from the first replication. We show that one can dramatically improve prediction of subject-specific connectivity by borrowing strength from the group correlation map computed using all other subjects in the study. Even more surprisingly, we found that the group correlation map provided a better prediction of a subject{\textquoteright}s connectivity than the individual{\textquoteright}s own data. While further discussion and experimentation are required to understand how this can be used in practice, results indicate that shrinkage-based methods that borrow strength from the population mean should play a role in rs-fMRI data analysis.

}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2014.05.043}, author = {Shou, Haochang and Eloyan, Ani and Nebel, Mary Beth and Mejia, Amanda and Pekar, James J and Mostofsky, Stewart and Caffo, Brian and Lindquist, Martin A and Crainiceanu, Ciprian M} } @article {796, title = {Smooth Scalar-on-Image Regression via Spatial Bayesian Variable Selection.}, journal = {J Comput Graph Stat}, volume = {23}, year = {2014}, month = {2014 Jan 1}, pages = {46-64}, abstract = {

We develop scalar-on-image regression models when images are registered multidimensional manifolds. We propose a fast and scalable Bayes inferential procedure to estimate the image coefficient. The central idea is the combination of an Ising prior distribution, which controls a latent binary indicator map, and an intrinsic Gaussian Markov random field, which controls the smoothness of the nonzero coefficients. The model is fit using a single-site Gibbs sampler, which allows fitting within minutes for hundreds of subjects with predictor images containing thousands of locations. The code is simple and is provided in less than one page in the Appendix. We apply this method to a neuroimaging study where cognitive outcomes are regressed on measures of white matter microstructure at every voxel of the corpus callosum for hundreds of subjects.

}, issn = {1061-8600}, doi = {10.1080/10618600.2012.743437}, author = {Goldsmith, Jeff and Huang, Lei and Crainiceanu, Ciprian M} } @article {786, title = {Statistical normalization techniques for magnetic resonance imaging.}, journal = {Neuroimage Clin}, volume = {6}, year = {2014}, month = {2014}, pages = {9-19}, abstract = {

While computed tomography and other imaging techniques are measured in absolute units with physical meaning, magnetic resonance images are expressed in arbitrary units that are difficult to interpret and differ between study visits and subjects. Much work in the image processing literature on intensity normalization has focused on histogram matching and other histogram mapping techniques, with little emphasis on normalizing images to have biologically interpretable units. Furthermore, there are no formalized principles or goals for the crucial comparability of image intensities within and across subjects. To address this, we propose a set of criteria necessary for the normalization of images. We further propose simple and robust biologically motivated normalization techniques for multisequence brain imaging that have the same interpretation across acquisitions and satisfy the proposed criteria. We compare the performance of different normalization methods in thousands of images of patients with Alzheimer{\textquoteright}s disease, hundreds of patients with multiple sclerosis, and hundreds of healthy subjects obtained in several different studies at dozens of imaging centers.

}, issn = {2213-1582}, doi = {10.1016/j.nicl.2014.08.008}, author = {Shinohara, Russell T and Sweeney, Elizabeth M and Goldsmith, Jeff and Shiee, Navid and Mateen, Farrah J and Calabresi, Peter A and Jarso, Samson and Pham, Dzung L and Reich, Daniel S and Crainiceanu, Ciprian M} } @article {789, title = {Structured functional principal component analysis.}, journal = {Biometrics}, year = {2014}, month = {2014 Oct 18}, abstract = {

Motivated by modern observational studies, we introduce a class of functional models that expand nested and crossed designs. These models account for the natural inheritance of the correlation structures from sampling designs in studies where the fundamental unit is a function or image. Inference is based on functional quadratics and their relationship with the underlying covariance structure of the latent processes. A computationally fast and scalable estimation procedure is developed for high-dimensional data. Methods are used in applications including high-frequency accelerometer data for daily activity, pitch linguistic data for phonetic analysis, and EEG data for studying electrical brain activity during sleep.

}, issn = {1541-0420}, doi = {10.1111/biom.12236}, author = {Shou, Haochang and Zipunnikov, Vadim and Crainiceanu, Ciprian M and Greven, Sonja} } @article {820, title = {Successful implementation of a unit-based quality nurse to reduce central line-associated bloodstream infections.}, journal = {Am J Infect Control}, volume = {42}, year = {2014}, month = {2014 Feb}, pages = {139-43}, abstract = {

BACKGROUND: Central line (CL)-associated bloodstream infections (CLABSI) are an important cause of patient morbidity and mortality. Novel strategies to prevent CLABSI are needed.

METHODS: We described a quasiexperimental study to examine the effect of the presence of a unit-based quality nurse (UQN) dedicated to perform patient safety and infection control activities with a focus on CLABSI prevention in a surgical intensive care unit (SICU).

RESULTS: From July 2008 to March 2012, there were 3,257 SICU admissions; CL utilization ratio was 0.74 (18,193 CL-days/24,576 patient-days). The UQN program began in July 2010; the nurse was present for 30\% (193/518) of the days of the intervention period of July 2010 to March 2012. The average CLABSI rate was 5.0 per 1,000 CL-days before the intervention and 1.5 after the intervention and decreased by 5.1\% (P = .005) for each additional 1\% of days of the month that the UQN was present, even after adjusting for CLABSI rates in other adult intensive care units, time, severity of illness, and Comprehensive Unit-based Safety Program participation (5.1\%, P = .004). Approximately 11.4 CLABSIs were prevented.

CONCLUSION: The presence of a UQN dedicated to perform infection control activities may be an effective strategy for CLABSI reduction.

}, keywords = {Adult, Catheter-Related Infections, Catheterization, Central Venous, Humans, Infection Control, Intensive Care Units, Nurses, Quality Control, Quality of Health Care}, issn = {1527-3296}, doi = {10.1016/j.ajic.2013.08.006}, author = {Thom, Kerri A and Li, Shanshan and Custer, Melissa and Preas, Michael Anne and Rew, Cindy D and Cafeo, Christina and Leekha, Surbhi and Caffo, Brian S and Scalea, Thomas M and Lissauer, Matthew E} } @article {nareshCaffeine, title = {Sleep-Disordered breathing and caffeine consumption: results of a community-based study}, journal = {Accepted in Chest}, year = {2012}, author = {Aurora, RN and Crainiceanu, C and Caffo, B and Punjabi, N} } @article {VP11_unlabeled, title = {Shuffled Graph Classification: Theory and Connectome Applications}, journal = {under review}, year = {2011}, author = {Vogelstein, Joshua T and Priebe, Carey E} } @article {frfrst2010, title = {The second order ancillary}, journal = {Bernoulli}, volume = {16}, year = {2010}, pages = {1208{\textendash}1223}, author = {Fraser, A.M. and Fraser, D.A.S. and Staicu, A. M.} } @article {punjabi, title = {Sleep-disordered Breathing and Mortality: a Prospective Cohort Study}, journal = {PLOS Medicine, PMCID: PMC2722083}, volume = {6}, year = {2009}, author = {Punjabi, N and Caffo, B and Goodwin, J and Gottlieb, D and Newman, A and O{\textquoteright}Connor, G and Rapoport, D and Redline, S and Resnick, H and Robbins, J and Samet, J and Shahar, E and Unruh, M} } @article {VogelsteinPaninski09, title = {Spike inference from calcium imaging using sequential Monte Carlo methods.}, journal = {Biophys J}, volume = {97}, number = {2}, year = {2009}, pages = {636{\textendash}655}, publisher = {Department of Neuroscience, The Johns Hopkins School of Medicine, Baltimore, Maryland, USA. joshuav@jhu.edu}, abstract = {

As recent advances in calcium sensing technologies facilitate simultaneously imaging action potentials in neuronal populations, complementary analytical tools must also be developed to maximize the utility of this experimental paradigm. Although the observations here are fluorescence movies, the signals of interest{\textendash}spike trains and/or time varying intracellular calcium concentrations{\textendash}are hidden. Inferring these hidden signals is often problematic due to noise, nonlinearities, slow imaging rate, and unknown biophysical parameters. We overcome these difficulties by developing sequential Monte Carlo methods (particle filters) based on biophysical models of spiking, calcium dynamics, and fluorescence. We show that even in simple cases, the particle filters outperform the optimal linear (i.e., Wiener) filter, both by obtaining better estimates and by providing error bars. We then relax a number of our model assumptions to incorporate nonlinear saturation of the fluorescence signal, as well external stimulus and spike history dependence (e.g., refractoriness) of the spike trains. Using both simulations and in vitro fluorescence observations, we demonstrate temporal superresolution by inferring when within a frame each spike occurs. Furthermore, the model parameters may be estimated using expectation maximization with only a very limited amount of data (e.g., approximately 5-10 s or 5-40 spikes), without the requirement of any simultaneous electrophysiology or imaging experiments.

}, keywords = {Animals, Biological, Calcium, cytology/metabolism, Fluorescence, Inbred C57BL, Intracellular Space, metabolism, Mice, models, Monte Carlo Method, Neurons, Probability, Time Factors}, doi = {10.1016/j.bpj.2008.08.005}, url = {http://dx.doi.org/10.1016/j.bpj.2008.08.005}, author = {Vogelstein, Joshua T and Watson, Brendon O and Packer, Adam M and Yuste, Rafael and Jedynak, Bruno and Paninski, Liam} } @article {lindquist08, title = {The Statistical Analysis of fMRI Data}, journal = {Statistical Science}, volume = {23}, year = {2008}, pages = {439{\textendash}464}, author = {Lindquist, Martin A} } @article {choi2008survey, title = {A survey of the likelihood approach to bioequivalence trials}, journal = {Statistics in Medicine, PMCID: PMC2778077}, volume = {27}, number = {24}, year = {2008}, pages = {4874{\textendash}4894}, publisher = {John Wiley \& Sons, Ltd Chichester, UK}, author = {Choi, L and Caffo, B and Rohde, C} } @article {pmid17646610, title = {Short-term variability in measures of glycemia and implications for the classification of diabetes}, journal = {Arch. Intern. Med.}, volume = {167}, number = {14}, year = {2007}, month = {Jul}, pages = {1545{\textendash}1551}, author = {Selvin, E. and Crainiceanu, C and Brancati, F. L. and Coresh, J.} } @article {Crai:Rupp:Carr:Josh:Good:spat:2007, title = {Spatially Adaptive Bayesian Penalized Splines with Heteroscedastic Errors}, journal = {Journal of Computational and Graphical Statistics}, volume = {16}, number = {2}, year = {2007}, pages = {265{\textendash}288}, keywords = {Heteroscedasticity, MCMC, Multivariate smoothing, Regression splines, spatially adaptive penalty, thin-plate splines, Variance functions}, author = {Crainiceanu, C and Ruppert, D. and Carroll, Raymond J. and Joshi, Adarsh and Goodner, Billy} } @article {Gime:Crai:Barb:Jeno:Morg:semi:2006, title = {Semiparametric Regression in Capture\textndashrecapture Modeling}, journal = {Biometrics}, volume = {62}, number = {3}, year = {2006}, pages = {691{\textendash}698}, keywords = {auxiliary variables, Bayesian inference, Demographic rates, Environmental covariates, penalized splines, Winbugs}, author = {Gimenez, O. and Crainiceanu, C and Barbraud, C. and Jenouvrier, S. and Morgan, B. J. T.} } @article {yacoub2003spin, title = {Spin-echo fMRI in humans using high spatial resolutions and high magnetic fields}, journal = {Magnetic resonance in medicine}, volume = {49}, number = {4}, year = {2003}, pages = {655{\textendash}664}, publisher = {Wiley Online Library}, author = {Yacoub, Essa and Duong, Timothy Q and De Moortele, Van and Lindquist, Martin A and Adriany, Gregor and Kim, Seong-Gi and U{\u g}urbil, K{\^a}mil and Hu, Xiaoping and others} } @article {agresti2000simple, title = {Simple and effective confidence intervals for proportions and differences of proportions result from adding two successes and two failures}, journal = {The American Statistician}, volume = {54}, number = {4}, year = {2000}, pages = {280{\textendash}288}, publisher = {American Statistical Association}, author = {Agresti, A and Caffo, B} }