@article {795, title = {A comparison of supervised machine learning algorithms and feature vectors for MS lesion segmentation using multimodal structural MRI.}, journal = {PLoS One}, volume = {9}, year = {2014}, month = {2014}, pages = {e95753}, abstract = {

Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance.

}, issn = {1932-6203}, doi = {10.1371/journal.pone.0095753}, author = {Sweeney, Elizabeth M and Vogelstein, Joshua T and Cuzzocreo, Jennifer L and Calabresi, Peter A and Reich, Daniel S and Crainiceanu, Ciprian M and Shinohara, Russell T} } @article {833, title = {Cross-sectional and longitudinal association of body mass index and brain volume.}, journal = {Hum Brain Mapp}, volume = {35}, year = {2014}, month = {2014 Jan}, pages = {75-88}, abstract = {

Although a link between body mass index (BMI) and brain volume has been established in several cross-sectional studies, evidence of the association between change in BMI over time and changes in brain structure is limited. Using data from a cohort of 347 former lead workers and community controls with two magnetic resonance imaging scans over a period of ~5 years, we estimated cross-sectional and longitudinal associations of BMI and brain volume using both region of interest (ROI) and voxel-based morphometric (VBM) methods. We found that associations of BMI and brain volume were not significantly different in former lead workers when compared with community controls. In the cross-sectional analysis, higher BMIs were associated with smaller brain volumes in gray matter (GM) using both ROI and VBM approaches. No associations with white matter (WM) were observed. In the longitudinal analysis, higher baseline BMI was associated with greater decline in temporal and occipital GM ROI volumes. Change in BMI over the 5-year period was only associated with change in hippocampal volume and was not associated with change in any of the GM ROIs. Overall, higher BMI was associated with lower GM volume in several ROIs and with declines in volume in temporal and occipital GM over time. These results suggest that sustained high body mass may contribute to progressive temporal and occipital atrophy.

}, keywords = {Aged, Atrophy, Body Mass Index, Brain, Cross-Sectional Studies, Humans, Image Interpretation, Computer-Assisted, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged, Obesity}, issn = {1097-0193}, doi = {10.1002/hbm.22159}, author = {Bobb, Jennifer F and Schwartz, Brian S and Davatzikos, Christos and Caffo, Brian} } @article {843, title = {Cloak and DAG: a response to the comments on our comment.}, journal = {Neuroimage}, volume = {76}, year = {2013}, month = {2013 Aug 1}, pages = {446-9}, abstract = {

Our original comment (Lindquist and Sobel, 2011) made explicit the types of assumptions neuroimaging researchers are making when directed graphical models (DGMs), which include certain types of structural equation models (SEMs), are used to estimate causal effects. When these assumptions, which many researchers are not aware of, are not met, parameters of these models should not be interpreted as effects. Thus it is imperative that neuroimaging researchers interested in issues involving causation, for example, effective connectivity, consider the plausibility of these assumptions for their particular problem before using SEMs. In cases where these additional assumptions are not met, researchers may be able to use other methods and/or design experimental studies where the use of unrealistic assumptions can be avoided. Pearl does not disagree with anything we stated. However, he takes exception to our use of potential outcomes{\textquoteright} notation, which is the standard notation used in the statistical literature on causal inference, and his comment is devoted to promoting his alternative conventions. Glymour{\textquoteright}s comment is based on three claims that he inappropriately attributes to us. Glymour is also more optimistic than us about the potential of using directed graphical models (DGMs) to discover causal relations in neuroimaging research; we briefly address this issue toward the end of our rejoinder.

}, keywords = {Artifacts, Brain, Computer Simulation, Humans, Image Interpretation, Computer-Assisted, Meta-Analysis as Topic}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2011.11.027}, author = {Lindquist, Martin A and Sobel, Michael E} } @article {810, title = {Combining hidden Markov models for comparing the dynamics of multiple sleep electroencephalograms.}, journal = {Stat Med}, volume = {32}, year = {2013}, month = {2013 Aug 30}, pages = {3342-56}, abstract = {

In this manuscript, we consider methods for the analysis of populations of electroencephalogram signals during sleep for the study of sleep disorders using hidden Markov models (HMMs). Notably, we propose an easily implemented method for simultaneously modeling multiple time series that involve large amounts of data. We apply these methods to study sleep-disordered breathing (SDB) in the Sleep Heart Health Study (SHHS), a landmark study of SDB and cardiovascular consequences. We use the entire, longitudinally collected, SHHS cohort to develop HMM population parameters, which we then apply to obtain subject-specific Markovian predictions. From these predictions, we create several indices of interest, such as transition frequencies between latent states. Our HMM analysis of electroencephalogram signals uncovers interesting findings regarding differences in brain activity during sleep between those with and without SDB. These findings include stability of the percent time spent in HMM latent states across matched diseased and non-diseased groups and differences in the rate of transitioning.

}, keywords = {Data Interpretation, Statistical, Electroencephalography, Female, Humans, Longitudinal Studies, Male, Markov Chains, Middle Aged, Models, Statistical, Sleep Apnea Syndromes}, issn = {1097-0258}, doi = {10.1002/sim.5747}, author = {Langrock, Roland and Swihart, Bruce J and Caffo, Brian S and Punjabi, Naresh M and Crainiceanu, Ciprian M} } @article {823, title = {Comparison of total hospital-acquired bloodstream infections to central line-associated bloodstream infections and implications for outcome measures in infection control.}, journal = {Infect Control Hosp Epidemiol}, volume = {34}, year = {2013}, month = {2013 Sep}, pages = {984-6}, abstract = {

The validity of the central line-associated bloodstream infection (CLABSI) measure is compromised by subjectivity. We observed significant decreases in both CLABSIs and total hospital-acquired bloodstream infections (BSIs) following a CLABSI prevention intervention in adult intensive care units. Total hospital-acquired BSIs could be explored as an adjunct, objective CLABSI measure.

}, keywords = {Adult, Catheter-Related Infections, Catheterization, Central Venous, Cross Infection, Humans, Intensive Care Units, Longitudinal Studies, Tertiary Care Centers}, issn = {1559-6834}, doi = {10.1086/671730}, author = {Leekha, Surbhi and Li, Shanshan and Thom, Kerri A and Preas, Michael Anne and Caffo, Brian S and Morgan, Daniel J and Harris, Anthony D} } @article {684, title = {Corrected confidence bands for functional data using principal components.}, journal = {Biometrics}, volume = {69}, year = {2013}, month = {2013 Mar}, pages = {41-51}, abstract = {

Functional principal components (FPC) analysis is widely used to decompose and express functional observations. Curve estimates implicitly condition on basis functions and other quantities derived from FPC decompositions; however these objects are unknown in practice. In this article, we propose a method for obtaining correct curve estimates by accounting for uncertainty in FPC decompositions. Additionally, pointwise and simultaneous confidence intervals that account for both model- and decomposition-based variability are constructed. Standard mixed model representations of functional expansions are used to construct curve estimates and variances conditional on a specific decomposition. Iterated expectation and variance formulas combine model-based conditional estimates across the distribution of decompositions. A bootstrap procedure is implemented to understand the uncertainty in principal component decomposition quantities. Our method compares favorably to competing approaches in simulation studies that include both densely and sparsely observed functions. We apply our method to sparse observations of CD4 cell counts and to dense white-matter tract profiles. Code for the analyses and simulations is publicly available, and our method is implemented in the R package refund on CRAN.

}, keywords = {Brain, CD4 Lymphocyte Count, Computer Simulation, Confidence Intervals, HIV, HIV Infections, Humans, Magnetic Resonance Imaging, Models, Statistical, Multiple Sclerosis, Principal Component Analysis}, issn = {1541-0420}, doi = {10.1111/j.1541-0420.2012.01808.x}, author = {Goldsmith, J and Greven, S and Crainiceanu, C} } @article {pmid22472185, title = {Cadmium Exposure and All Cause and Cardiovascular Mortality in the US General Population}, journal = {Environ Health Perspect}, year = {2012}, month = {Apr}, author = {Tellez-Plaza, M. and Navas-Acien, A. and Menke, A. and Crainiceanu, C and Pastor-Barriuso, R. and Guallar, E.} } @article {LS11b, title = {Cloak and DAG: A response to the comments on our comment}, journal = {NeuroImage, to appear}, year = {2012}, issn = {1053-8119}, doi = {10.1016/j.neuroimage.2011.11.027}, url = {http://www.sciencedirect.com/science/article/pii/S1053811911013085}, author = {Lindquist, Martin A and Sobel, M.E.} } @article {thom, title = {Comparison of swab versus sponge methodology for the identification of acinetobacter baumannii from the hospital environment}, journal = {Accepted in the Journal of Clinical Microbiology}, year = {2012}, author = {Thom, K and Howard, T and Sembajwe, S and Harris, A and Strassle, P and Caffo, B and Caroll, K and Johnson, J} } @article {ellignton, title = {Computerized lung sound analysis to improve the specificity of pediatric pneumonia in resource-poor settings}, journal = {Accepted in the British Medical Journal Open}, year = {2012}, author = {Ellington, L and Gilman, R and Tielsch, J and Steinhoff, M and Figueroa, D and Rodriguez, S and Caffo, B and Tracey, B and West, J and Checkley, W} } @article {PACE:PACE2905, title = {Clinical predictors of conduction disease progression in type I myotonic muscular dystrophy}, journal = {Pacing and Clinical Electrophysiology, PMCID: PMC3035751}, volume = {34}, number = {2}, year = {2011}, pages = {171{\textendash}176}, publisher = {Blackwell Publishing Inc}, keywords = {cardiomyopathy, conduction disease, electrocardiography, myotonic muscular dystrophy, sudden death}, issn = {1540-8159}, doi = {10.1111/j.1540-8159.2010.02905.x}, url = {http://dx.doi.org/10.1111/j.1540-8159.2010.02905.x}, author = {Nazarian, S and Wagner, K and Caffo, B and Tomaselli, G} } @article {li3, title = {Comparison of proportions for composite endpoints with missing components}, journal = {Journal of Biopharmaceutical Statistics, NIHMSID: 316386}, volume = {21}, year = {2011}, pages = {271{\textendash}281}, author = {Li, X and Caffo, B} } @article {aurora2011correlating, title = {Correlating subjective and objective sleepiness: revisiting the association using survival analysis.}, journal = {Sleep}, volume = {34}, number = {12}, year = {2011}, pages = {1707}, author = {Aurora, RN and Caffo, B and Crainiceanu, C and Punjabi, N} } @article {pmid20693268, title = {Cadmium and peripheral arterial disease: gender differences in the 1999-2004 US National Health and Nutrition Examination Survey}, journal = {Am. J. Epidemiol.}, volume = {172}, number = {6}, year = {2010}, month = {Sep}, pages = {671{\textendash}681}, author = {Tellez-Plaza, M. and Navas-Acien, A. and Crainiceanu, C and Sharrett, A. R. and Guallar, E.} } @article {hedlin2009covariate, title = {Covariate-adjusted nonparametric analysis of magnetic resonance images using Markov chain Monte Carlo}, journal = {Statistics and its Interface, NIHMSID: 210823}, volume = {3}, number = {1}, year = {2010}, pages = {113-123}, author = {Hedlin, H and Caffo, B and Mahfoud, Z and Bassett, S} } @article {Crai:comm:2009, title = {Comment on {\textquoteright}{\textquoteright}Bayesian Generalized Method of Moments{\textquoteright}{\textquoteright} (Pkg: P191-222)}, journal = {Bayesian Analysis}, volume = {4}, number = {2}, year = {2009}, pages = {213{\textendash}216}, author = {Crainiceanu, C} } @article {lindquist2009correlations, title = {Correlations and multiple comparisons in functional imaging: a statistical perspective (Commentary on Vul et al., 2009)}, journal = {Perspectives on Psychological Science}, volume = {4}, number = {3}, year = {2009}, pages = {310{\textendash}313}, publisher = {SAGE Publications}, author = {Lindquist, Martin A and Gelman, Andrew} } @article {Chen:Crai:cox:2009, title = {Cox Models With Smooth Functional Effect of Covariates Measured With Error}, journal = {Journal of the American Statistical Association}, volume = {104}, number = {487}, year = {2009}, pages = {1144{\textendash}1154}, keywords = {Measurement error, Smoothing, Survival analysis}, author = {Cheng, Yu-Jen and Crainiceanu, C} } @article {pmid18197299, title = {Cadmium exposure and hypertension in the 1999-2004 National Health and Nutrition Examination Survey (NHANES)}, journal = {Environ. Health Perspect.}, volume = {116}, number = {1}, year = {2008}, month = {Jan}, pages = {51{\textendash}56}, author = {Tellez-Plaza, M. and Navas-Acien, A. and Crainiceanu, C and Guallar, E.} } @article {Caff:Crai:Deng:Hend:case:2008, title = {A Case Study in Pharmacologic Colon Imaging Using Principal Curves in Single-Photon Emission Computed Tomography}, journal = {Journal of the American Statistical Association}, volume = {103}, number = {484}, year = {2008}, pages = {1470{\textendash}1480}, keywords = {Curve fitting, medical imaging, Pharmacology, Smoothing, SPECT}, author = {Caffo, B and Crainiceanu, C and Deng, Lijuan and Hendrix, C} } @article {swihart2008quantitative, title = {Characterizing Sleep Structure Using the Hypnogram}, journal = {Journal of Clinical Sleep Medicine, PMCID: PMC2542492}, volume = {4}, number = {4}, year = {2008}, pages = {349{\textendash}355}, author = {Swihart, B and Caffo, B and Bandeen-Roche, K and Punjabi, N} } @article {zhang2006cigarette, title = {Cigarette smoking and nocturnal sleep architecture}, journal = {American Journal of Epidemiology}, volume = {164}, number = {6}, year = {2006}, pages = {529}, publisher = {Oxford Univ Press}, author = {Zhang, L and Samet, J and Caffo, B and Punjabi, N} }