@article {bai2016activity, title = {An activity index for raw accelerometry data and its comparison with other activity metrics}, journal = {PloS ONE}, volume = {11}, number = {8}, year = {2016}, pages = {e0160644}, publisher = {Public Library of Science}, author = {Bai, Jiawei and Di, Chongzhi and Xiao, Luo and Evenson, Kelly R and LaCroix, Andrea Z and Crainiceanu, Ciprian M and Buchner, David M} } @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 {794, title = {Estimating energy expenditure from heart rate in older adults: a case for calibration.}, journal = {PLoS One}, volume = {9}, year = {2014}, month = {2014}, pages = {e93520}, abstract = {

BACKGROUND: Accurate measurement of free-living energy expenditure is vital to understanding changes in energy metabolism with aging. The efficacy of heart rate as a surrogate for energy expenditure is rooted in the assumption of a linear function between heart rate and energy expenditure, but its validity and reliability in older adults remains unclear.

OBJECTIVE: To assess the validity and reliability of the linear function between heart rate and energy expenditure in older adults using different levels of calibration.

DESIGN: Heart rate and energy expenditure were assessed across five levels of exertion in 290 adults participating in the Baltimore Longitudinal Study of Aging. Correlation and random effects regression analyses assessed the linearity of the relationship between heart rate and energy expenditure and cross-validation models assessed predictive performance.

RESULTS: Heart rate and energy expenditure were highly correlated (r=0.98) and linear regardless of age or sex. Intra-person variability was low but inter-person variability was high, with substantial heterogeneity of the random intercept (s.d. =0.372) despite similar slopes. Cross-validation models indicated individual calibration data substantially improves accuracy predictions of energy expenditure from heart rate, reducing the potential for considerable measurement bias. Although using five calibration measures provided the greatest reduction in the standard deviation of prediction errors (1.08 kcals/min), substantial improvement was also noted with two (0.75 kcals/min).

CONCLUSION: These findings indicate standard regression equations may be used to make population-level inferences when estimating energy expenditure from heart rate in older adults but caution should be exercised when making inferences at the individual level without proper calibration.

}, issn = {1932-6203}, doi = {10.1371/journal.pone.0093520}, author = {Schrack, Jennifer A and Zipunnikov, Vadim and Goldsmith, Jeff and Bandeen-Roche, Karen and Crainiceanu, Ciprian M and Ferrucci, Luigi} } @article {790, title = {Health effects of lesion localization in multiple sclerosis: spatial registration and confounding adjustment.}, journal = {PLoS One}, volume = {9}, year = {2014}, month = {2014}, pages = {e107263}, abstract = {

Brain lesion localization in multiple sclerosis (MS) is thought to be associated with the type and severity of adverse health effects. However, several factors hinder statistical analyses of such associations using large MRI datasets: 1) spatial registration algorithms developed for healthy individuals may be less effective on diseased brains and lead to different spatial distributions of lesions; 2) interpretation of results requires the careful selection of confounders; and 3) most approaches have focused on voxel-wise regression approaches. In this paper, we evaluated the performance of five registration algorithms and observed that conclusions regarding lesion localization can vary substantially with the choice of registration algorithm. Methods for dealing with confounding factors due to differences in disease duration and local lesion volume are introduced. Voxel-wise regression is then extended by the introduction of a metric that measures the distance between a patient-specific lesion mask and the population prevalence map.

}, issn = {1932-6203}, doi = {10.1371/journal.pone.0107263}, author = {Eloyan, Ani and Shou, Haochang and Shinohara, Russell T and Sweeney, Elizabeth M and Nebel, Mary Beth and Cuzzocreo, Jennifer L and Calabresi, Peter A and Reich, Daniel S and Lindquist, Martin A and Crainiceanu, Ciprian M} } @article {793, title = {Multilevel sparse functional principal component analysis.}, journal = {Stat}, volume = {3}, year = {2014}, month = {2014 Jan 29}, pages = {126-143}, abstract = {

We consider analysis of sparsely sampled multilevel functional data, where the basic observational unit is a function and data have a natural hierarchy of basic units. An example is when functions are recorded at multiple visits for each subject. Multilevel functional principal component analysis (MFPCA; Di et al. 2009) was proposed for such data when functions are densely recorded. Here we consider the case when functions are sparsely sampled and may contain only a few observations per function. We exploit the multilevel structure of covariance operators and achieve data reduction by principal component decompositions at both between and within subject levels. We address inherent methodological differences in the sparse sampling context to: 1) estimate the covariance operators; 2) estimate the functional principal component scores; 3) predict the underlying curves. Through simulations the proposed method is able to discover dominating modes of variations and reconstruct underlying curves well even in sparse settings. Our approach is illustrated by two applications, the Sleep Heart Health Study and eBay auctions.

}, issn = {0038-9986}, doi = {10.1002/sta4.50}, author = {Di, Chongzhi and Crainiceanu, Ciprian M and Jank, Wolfgang S} } @article {804, title = {Normalization and extraction of interpretable metrics from raw accelerometry data.}, journal = {Biostatistics}, volume = {15}, year = {2014}, month = {2014 Jan}, pages = {102-16}, abstract = {

We introduce an explicit set of metrics for human activity based on high-density acceleration recordings from a hip-worn tri-axial accelerometer. These metrics are based on two concepts: (i) Time Active, a measure of the length of time when activity is distinguishable from rest and (ii) AI, a measure of the relative amplitude of activity relative to rest. All measurements are normalized (have the same interpretation across subjects and days), easy to explain and implement, and reproducible across platforms and software implementations. Metrics were validated by visual inspection of results and quantitative in-lab replication studies, and by an association study with health outcomes.

}, keywords = {Acceleration, Aged, Baltimore, Cohort Studies, Data Interpretation, Statistical, Female, Humans, Male, Motor Activity}, issn = {1468-4357}, doi = {10.1093/biostatistics/kxt029}, author = {Bai, Jiawei and He, Bing and Shou, Haochang and Zipunnikov, Vadim and Glass, Thomas A and Crainiceanu, Ciprian M} } @article {791, title = {Predicting human movement with multiple accelerometers using movelets.}, journal = {Med Sci Sports Exerc}, volume = {46}, year = {2014}, month = {2014 Sep}, pages = {1859-66}, abstract = {

PURPOSE: The study aims were 1) to develop transparent algorithms that use short segments of training data for predicting activity types and 2) to compare the prediction performance of the proposed algorithms using single accelerometers and multiple accelerometers.

METHODS: Sixteen participants (age, 80.6 yr (4.8 yr); body mass index, 26.1 kg{\textperiodcentered}m (2.5 kg{\textperiodcentered}m)) performed 15 lifestyle activities in the laboratory, each wearing three accelerometers at the right hip and left and right wrists. Triaxial accelerometry data were collected at 80 Hz using ActiGraph GT3X+. Prediction algorithms were developed, which, instead of extracting features, build activity-specific dictionaries composed of short signal segments called movelets. Three alternative approaches were proposed to integrate the information from the multiple accelerometers.

RESULTS: With at most several seconds of training data per activity, the prediction accuracy at the second-level temporal resolution was very high for lying, standing, normal/fast walking, and standing up from a chair (the median prediction accuracy ranged from 88.2\% to 99.9\% on the basis of the single-accelerometer movelet approach). For these activities, wrist-worn accelerometers performed almost as well as hip-worn accelerometers (the median difference in accuracy between wrist and hip ranged from -2.7\% to 5.8\%). Modest improvements in prediction accuracy were achieved by integrating information from multiple accelerometers.

DISCUSSION AND CONCLUSIONS: It is possible to achieve high prediction accuracy at the second-level temporal resolution with very limited training data. To increase prediction accuracy from the simultaneous use of multiple accelerometers, a careful selection of integrative approaches is required.

}, issn = {1530-0315}, doi = {10.1249/MSS.0000000000000285}, author = {He, Bing and Bai, Jiawei and Zipunnikov, Vadim V and Koster, Annemarie and Caserotti, Paolo and Lange-Maia, Brittney and Glynn, Nancy W and Harris, Tamara B and Crainiceanu, Ciprian M} } @article {787, title = {Quantifying the lifetime circadian rhythm of physical activity: a covariate-dependent functional approach.}, journal = {Biostatistics}, year = {2014}, month = {2014 Oct 30}, abstract = {

Objective measurement of physical activity using wearable devices such as accelerometers may provide tantalizing new insights into the association between activity and health outcomes. Accelerometers can record quasi-continuous activity information for many days and for hundreds of individuals. For example, in the Baltimore Longitudinal Study on Aging physical activity was recorded every minute for [Formula: see text] adults for an average of [Formula: see text] days per adult. An important scientific problem is to separate and quantify the systematic and random circadian patterns of physical activity as functions of time of day, age, and gender. To capture the systematic circadian pattern, we introduce a practical bivariate smoother and two crucial innovations: (i) estimating the smoothing parameter using leave-one-subject-out cross validation to account for within-subject correlation and (ii) introducing fast computational techniques that overcome problems both with the size of the data and with the cross-validation approach to smoothing. The age-dependent random patterns are analyzed by a new functional principal component analysis that incorporates both covariate dependence and multilevel structure. For the analysis, we propose a practical and very fast trivariate spline smoother to estimate covariate-dependent covariances and their spectra. Results reveal several interesting, previously unknown, circadian patterns associated with human aging and gender.

}, issn = {1468-4357}, doi = {10.1093/biostatistics/kxu045}, author = {Xiao, Luo and Huang, Lei and Schrack, Jennifer A and Ferrucci, Luigi and Zipunnikov, Vadim and Crainiceanu, Ciprian M} } @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 {788, title = {A unifying framework for marginalized random intercept models of correlated binary outcomes.}, journal = {Int Stat Rev}, volume = {82}, year = {2014}, month = {2014 Aug}, pages = {275-295}, abstract = {

We demonstrate that many current approaches for marginal modeling of correlated binary outcomes produce likelihoods that are equivalent to the copula-based models herein. These general copula models of underlying latent threshold random variables yield likelihood-based models for marginal fixed effects estimation and interpretation in the analysis of correlated binary data with exchangeable correlation structures. Moreover, we propose a nomenclature and set of model relationships that substantially elucidates the complex area of marginalized random intercept models for binary data. A diverse collection of didactic mathematical and numerical examples are given to illustrate concepts.

}, issn = {0306-7734}, doi = {10.1111/insr.12035}, author = {Swihart, Bruce J and Caffo, Brian S and Crainiceanu, Ciprian M} } @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 {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 {809, title = {In vivo identification of morphologic retinal abnormalities in neuromyelitis optica.}, journal = {Neurology}, volume = {80}, year = {2013}, month = {2013 Apr 9}, pages = {1406-14}, abstract = {

OBJECTIVE: To assess eyes with neuromyelitis optica (NMO) for morphologic retinal abnormalities utilizing high-definition optical coherence tomography (OCT) imaging.

METHODS: In this cross-sectional study, 39 patients with NMO spectrum disorders and 39 age- and sex-matched healthy controls underwent spectral-domain OCT and visual function testing.

RESULTS: Microcystic macular edema (MME) of the inner nuclear layer (INL) was identified in 10 of 39 patients (26\%) and was exclusively found in eyes with a history of optic neuritis (ON). MME eyes had lower high- and low-contrast letter-acuity scores (100\%: p = 0.002; 2.5\%: p = 0.002; 1.25\%: p = 0.004), lower peripapillary retinal nerve fiber layer (RNFL) thickness (p = 0.04), lower macular RNFL thickness (p = 0.004), lower ganglion cell layer + inner plexiform layer (GCIP) thickness (p = 0.007), higher INL thickness (p < 0.001), and a greater number of ON episodes (p = 0.008) relative to non-MME eyes with a history of ON. After adjusting for history of multiple ON episodes, these findings remained significant for macular-RNFL thickness (p = 0.03), INL thickness (p < 0.001), and 100\% and 2.5\% contrast letter-acuity scores (p = 0.008 and p = 0.03, respectively). NMO spectrum eyes without ON history had lower macular RNFL thickness (p = 0.003), GCIP thickness (p = 0.002), outer nuclear layer thickness (p = 0.02), and low-contrast letter-acuity scores (2.5\%: p = 0.03; 1.25\%: p = 0.002) compared to healthy controls.

CONCLUSIONS: We have identified a pattern of retinal morphologic abnormalities in NMO that is associated with severe retinal axonal and neuronal loss and corresponding visual disability. MME may contribute to poor visual outcomes following NMO-associated ON or alternatively represent a marker of ON severity. Additionally, our results support that subclinical involvement of the anterior visual pathway may occur in NMO spectrum disorders.

}, keywords = {Adolescent, Adult, Aged, Aquaporin 4, Autoantibodies, Case-Control Studies, Chi-Square Distribution, Female, Humans, Macular Edema, Male, Middle Aged, Nerve Fibers, Neuromyelitis Optica, Retina, Tomography, Optical Coherence, Vision Tests, Visual Acuity, Young Adult}, issn = {1526-632X}, doi = {10.1212/WNL.0b013e31828c2f7a}, author = {Sotirchos, Elias S and Saidha, Shiv and Byraiah, Gita and Mealy, Maureen A and Ibrahim, Mohamed A and Sepah, Yasir Jamal and Newsome, Scott D and Ratchford, John N and Frohman, Elliot M and Balcer, Laura J and Crainiceanu, Ciprian M and Nguyen, Quan Dong and Levy, Michael and Calabresi, Peter A} } @article {812, title = {Likelihood-based population independent component analysis.}, journal = {Biostatistics}, volume = {14}, year = {2013}, month = {2013 Jul}, pages = {514-27}, abstract = {

Independent component analysis (ICA) is a widely used technique for blind source separation, used heavily in several scientific research areas including acoustics, electrophysiology, and functional neuroimaging. We propose a scalable two-stage iterative true group ICA methodology for analyzing population level functional magnetic resonance imaging (fMRI) data where the number of subjects is very large. The method is based on likelihood estimators of the underlying source densities and the mixing matrix. As opposed to many commonly used group ICA algorithms, the proposed method does not require significant data reduction by a 2-fold singular value decomposition. In addition, the method can be applied to a large group of subjects since the memory requirements are not restrictive. The performance of our approach is compared with a commonly used group ICA algorithm via simulation studies. Furthermore, the proposed method is applied to a large collection of resting state fMRI datasets. The results show that established brain networks are well recovered by the proposed algorithm.

}, keywords = {Adult, Algorithms, Attention Deficit Disorder with Hyperactivity, Biostatistics, Brain, Child, Computer Simulation, Databases, Factual, Humans, Imaging, Three-Dimensional, Likelihood Functions, Magnetic Resonance Imaging, Principal Component Analysis, Statistics, Nonparametric}, issn = {1468-4357}, doi = {10.1093/biostatistics/kxs055}, author = {Eloyan, Ani and Crainiceanu, Ciprian M and Caffo, Brian S} } @article {802, title = {OASIS is Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI.}, journal = {Neuroimage Clin}, volume = {2}, year = {2013}, month = {2013}, pages = {402-13}, abstract = {

Magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple sclerosis (MS) patients and is essential for diagnosing the disease and monitoring its progression. In practice, lesion load is often quantified by either manual or semi-automated segmentation of MRI, which is time-consuming, costly, and associated with large inter- and intra-observer variability. We propose OASIS is Automated Statistical Inference for Segmentation (OASIS), an automated statistical method for segmenting MS lesions in MRI studies. We use logistic regression models incorporating multiple MRI modalities to estimate voxel-level probabilities of lesion presence. Intensity-normalized T1-weighted, T2-weighted, fluid-attenuated inversion recovery and proton density volumes from 131 MRI studies (98 MS subjects, 33 healthy subjects) with manual lesion segmentations were used to train and validate our model. Within this set, OASIS detected lesions with a partial area under the receiver operating characteristic curve for clinically relevant false positive rates of 1\% and below of 0.59\% (95\% CI; [0.50\%, 0.67\%]) at the voxel level. An experienced MS neuroradiologist compared these segmentations to those produced by LesionTOADS, an image segmentation software that provides segmentation of both lesions and normal brain structures. For lesions, OASIS out-performed LesionTOADS in 74\% (95\% CI: [65\%, 82\%]) of cases for the 98 MS subjects. To further validate the method, we applied OASIS to 169 MRI studies acquired at a separate center. The neuroradiologist again compared the OASIS segmentations to those from LesionTOADS. For lesions, OASIS ranked higher than LesionTOADS in 77\% (95\% CI: [71\%, 83\%]) of cases. For a randomly selected subset of 50 of these studies, one additional radiologist and one neurologist also scored the images. Within this set, the neuroradiologist ranked OASIS higher than LesionTOADS in 76\% (95\% CI: [64\%, 88\%]) of cases, the neurologist 66\% (95\% CI: [52\%, 78\%]) and the radiologist 52\% (95\% CI: [38\%, 66\%]). OASIS obtains the estimated probability for each voxel to be part of a lesion by weighting each imaging modality with coefficient weights. These coefficients are explicit, obtained using standard model fitting techniques, and can be reused in other imaging studies. This fully automated method allows sensitive and specific detection of lesion presence and may be rapidly applied to large collections of images.

}, issn = {2213-1582}, doi = {10.1016/j.nicl.2013.03.002}, author = {Sweeney, Elizabeth M and Shinohara, Russell T and Shiee, Navid and Mateen, Farrah J and Chudgar, Avni A and Cuzzocreo, Jennifer L and Calabresi, Peter A and Pham, Dzung L and Reich, Daniel S and Crainiceanu, Ciprian M} } @article {805, title = {Practical recommendations for population PK studies with sampling time errors.}, journal = {Eur J Clin Pharmacol}, volume = {69}, year = {2013}, month = {2013 Dec}, pages = {2055-64}, abstract = {

PURPOSE: Population pharmacokinetic (PK) data collected from routine clinical practice offers a rich source of valuable information. However, in observational population PK data, accurate time information for blood samples is often missing, resulting in measurement errors (ME) in the sampling time variable. The goal of this study was to investigate the effects on model parameters when a scheduled time is used instead of the actual blood sampling time, and to propose ME correction methods.

METHODS: Simulation studies were conducted based on two major factors: the curvature in PK profiles and the size of ME. As ME correction methods, transform both sides (TBS) models were developed with application of Box-Cox power transformation and Taylor expansion. The TBS models were compared to a conventional population PK model using simulations.

RESULTS: The most important determinant of bias due to time ME was the degree of curvature (nonlinearity) in PK profiles; the smaller the curvature around sampling times, the smaller the associated bias. The second important determinant was the magnitude of ME; the larger the ME, the larger the bias. The proposed TBS models performed better than a conventional population PK modeling when curvature and ME were substantial.

CONCLUSIONS: Time ME in sampling time can lead to bias on the parameter estimators. The following practical recommendations are provided: 1) when the curvature of PK profiles is small, conventional population PK modeling is robust to even large ME; and 2) when the curvature is moderate or large, the proposed methodology reduces bias in parameter estimates.

}, keywords = {Humans, Models, Biological, Pharmacokinetics, Research Design, Time Factors}, issn = {1432-1041}, doi = {10.1007/s00228-013-1576-7}, author = {Choi, Leena and Crainiceanu, Ciprian M and Caffo, Brian S} } @article {801, title = {Quantification of multiple-sclerosis-related brain atrophy in two heterogeneous MRI datasets using mixed-effects modeling.}, journal = {Neuroimage Clin}, volume = {3}, year = {2013}, month = {2013}, pages = {171-9}, abstract = {

Brain atrophy, measured by MRI, has been proposed as a useful surrogate marker for disease progression in multiple sclerosis (MS). However, it is conventionally assumed that the accurate quantification of brain atrophy is made difficult, if not impossible, by changes in the parameters of the MRI acquisition, which are almost inevitable over the course of a longitudinal study since MRI technology changes rapidly. This state of affairs can negatively affect clinical trial design and limit the use of historical data. Here, we investigate whether we can coherently estimate brain atrophy rates in a heterogeneous MS sample via linear mixed-effects multivariable regression, incorporating three critical assumptions: (1) using age at time of scanning, rather than time since baseline, as the regressor of interest; (2) scanning individuals with a variety of techniques; and (3) introducing a simple additive correction for major differences in MRI protocol. We fit the model to several measures of brain volume as the outcome in two MS populations: 1123 scans from 195 cases acquired for over approximately 7~years in two natural history protocols (Cohort 1), and 1331 scans from 69 cases seen for over 11~years who were primarily treated with two specific MS disease-modifying therapies (Cohort 2). We compared the mixed-effects model with additive correction for MRI acquisition parameters to a model fit without this correction and performed sample-size calculations to provide an estimate of the number of participants in an MS clinical trial that might be required to see a therapeutic effect of treatment using the approach described here. The results show that without the additive correction for T1-weighted protocol parameters, atrophy was underestimated and subject-specific estimates were more narrowly distributed about the population mean. Ventricular CSF is the most consistently estimated brain volume, with a mean of 2.8\%/year increase in Cohort 1 and 4.4\%/year increase in Cohort 2. An interesting observation was that gray matter volume decreased and white matter volume remained essentially unchanged in both cohorts, suggesting that changes in ventricular CSF volume are a surrogate for changes in gray matter volume. In conclusion, the mixed-effects modeling framework presented here allows effective use of heterogeneously acquired and historical data in the study of brain atrophy in MS, potentially simplifying the design of future single- and multi-site clinical trials and natural history studies.

}, issn = {2213-1582}, doi = {10.1016/j.nicl.2013.08.001}, author = {Jones, Blake C and Nair, Govind and Shea, Colin D and Crainiceanu, Ciprian M and Cortese, Irene C M and Reich, Daniel S} } @article {811, title = {Relationships between retinal axonal and neuronal measures and global central nervous system pathology in multiple sclerosis.}, journal = {JAMA Neurol}, volume = {70}, year = {2013}, month = {2013 Jan}, pages = {34-43}, abstract = {

OBJECTIVE: To determine the relationships between conventional and segmentation-derived optical coherence tomography (OCT) retinal layer thickness measures with intracranial volume (a surrogate of head size) and brain substructure volumes in multiple sclerosis (MS).

DESIGN: Cross-sectional study.

SETTING: Johns Hopkins University, Baltimore, Maryland.

PARTICIPANTS: A total of 84 patients with MS and 24 healthy control subjects.

MAIN OUTCOME MEASURES: High-definition spectral-domain OCT conventional and automated segmentation-derived discrete retinal layer thicknesses and 3-T magnetic resonance imaging brain substructure volumes.

RESULTS: Peripapillary retinal nerve fiber layer as well as composite ganglion cell layer+inner plexiform layer thicknesses in the eyes of patients with MS without a history of optic neuritis were associated with cortical gray matter (P=.01 and P=.04, respectively) and caudate (P=.04 and P=.03, respectively) volumes. Inner nuclear layer thickness, also in eyes without a history of optic neuritis, was associated with fluid-attenuated inversion recovery lesion volume (P=.007) and inversely associated with normal-appearing white matter volume (P=.005) in relapsing-remitting MS. As intracranial volume was found to be related with several of the OCT measures in patients with MS and healthy control subjects and is already known to be associated with brain substructure volumes, all OCT-brain substructure relationships were adjusted for intracranial volume. CONCLUSIONS Retinal measures reflect global central nervous system pathology in multiple sclerosis, with thicknesses of discrete retinal layers each appearing to be associated with distinct central nervous system processes. Moreover, OCT measures appear to correlate with intracranial volume in patients with MS and healthy control subjects, an important unexpected factor unaccounted for in prior studies examining the relationships between peripapillary retinal nerve fiber layer thickness and brain substructure volumes.

}, keywords = {Adult, Axons, Caudate Nucleus, Central Nervous System, Cerebral Cortex, Cross-Sectional Studies, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Multiple Sclerosis, Retina, Retinal Neurons, Tomography, Optical Coherence}, issn = {2168-6157}, doi = {10.1001/jamaneurol.2013.573}, author = {Saidha, Shiv and Sotirchos, Elias S and Oh, Jiwon and Syc, Stephanie B and Seigo, Michaela A and Shiee, Navid and Eckstein, Chistopher and Durbin, Mary K and Oakley, Jonathan D and Meyer, Scott A and Frohman, Teresa C and Newsome, Scott and Ratchford, John N and Balcer, Laura J and Pham, Dzung L and Crainiceanu, Ciprian M and Frohman, Elliot M and Reich, Daniel S and Calabresi, Peter A} } @article {708, title = {Arsenic exposure, diabetes prevalence, and diabetes control in the Strong Heart Study.}, journal = {Am J Epidemiol}, volume = {176}, year = {2012}, month = {2012 Nov 15}, pages = {865-74}, abstract = {

This study evaluated the association of arsenic exposure, as measured in urine, with diabetes prevalence, glycated hemoglobin, and insulin resistance in American Indian adults from Arizona, Oklahoma, and North and South Dakota (1989-1991). We studied 3,925 men and women 45-74 years of age with available urine arsenic measures. Diabetes was defined as a fasting glucose level of 126 mg/dL or higher, a 2-hour glucose level of 200 mg/dL or higher, a hemoglobin A1c (HbA1c) of 6.5\% or higher, or diabetes treatment. Median urine arsenic concentration was 14.1 {\textmu}g/L (interquartile range, 7.9-24.2). Diabetes prevalence was 49.4\%. After adjustment for sociodemographic factors, diabetes risk factors, and urine creatinine, the prevalence ratio of diabetes comparing the 75th versus 25th percentiles of total arsenic concentrations was 1.14 (95\% confidence interval: 1.08, 1.21). The association between arsenic and diabetes was restricted to participants with poor diabetes control (HbA1c >=8\%). Arsenic was positively associated with HbA1c levels in participants with diabetes. Arsenic was not associated with HbA1c or with insulin resistance (assessed by homeostatic model assessment to quantify insulin resistance) in participants without diabetes. Urine arsenic was associated with diabetes control in a population from rural communities in the United States with a high burden of diabetes. Prospective studies that evaluate the direction of the relation between poor diabetes control and arsenic exposure are needed.

}, keywords = {Aged, Arizona, Arsenicals, Blood Glucose, Creatinine, Diabetes Mellitus, Environmental Exposure, Female, Hemoglobin A, Glycosylated, Humans, Indians, North American, Male, Middle Aged, North Dakota, Oklahoma, Poisson Distribution, Prevalence, Regression Analysis, Risk Factors, South Dakota}, issn = {1476-6256}, doi = {10.1093/aje/kws153}, author = {Gribble, Matthew O and Howard, Barbara V and Umans, Jason G and Shara, Nawar M and Francesconi, Kevin A and Goessler, Walter and Crainiceanu, Ciprian M and Silbergeld, Ellen K and Guallar, Eliseo and Navas-Acien, Ana} } @article {709, title = {Microcystic macular oedema, thickness of the inner nuclear layer of the retina, and disease characteristics in multiple sclerosis: a retrospective study.}, journal = {Lancet Neurol}, volume = {11}, year = {2012}, month = {2012 Nov}, pages = {963-72}, abstract = {

BACKGROUND: Microcystic macular oedema (MMO) of the retinal inner nuclear layer (INL) has been identified in patients with multiple sclerosis (MS) by use of optical coherence tomography (OCT). We aimed to determine whether MMO of the INL, and increased thickness of the INL are associated with disease activity or disability progression.

METHODS: This retrospective study was done at the Johns Hopkins Hospital (Baltimore, MD, USA), between September, 2008, and March, 2012. Patients with MS and healthy controls underwent serial OCT scans and clinical assessments including visual function. OCT scanning, including automated intraretinal layer segmentation, yielded thicknesses of the retinal nerve fibre layer, the ganglion cell layer plus inner plexiform layer, the INL plus outer plexiform layer (the combined thickness of these layers was used as a surrogate measure of INL thickness), and the outer nuclear layer. Patients with MS also underwent annual brain MRI scans. Disability scores were compared with the Wilcoxon rank-sum test. Mixed-effects linear regression was used to compare OCT measures and letter-acuity scores. Logistic regression was used to examine the relations of baseline OCT thicknesses with clinical and radiological parameters.

FINDINGS: 164 patients with MS and 60 healthy controls were assessed. Mean follow-up was 25{\textperiodcentered}8 months (SD 9{\textperiodcentered}1) for patients with MS and 22{\textperiodcentered}4 months (11{\textperiodcentered}4) for healthy controls. Ten (6\%) patients with MS had MMO during at least one study visit; MMO was visible at baseline in four of these patients. Healthy controls did not have MMO. Patients with MS and MMO had higher baseline MS severity scores (median 5{\textperiodcentered}93 [range 2{\textperiodcentered}44-8{\textperiodcentered}91]) than those who did not have MMO at any time during the study (151 patients; 3{\textperiodcentered}81 [0{\textperiodcentered}13-9{\textperiodcentered}47]; p=0{\textperiodcentered}032), although expanded disability status scale (EDSS) scores were not significantly different (5{\textperiodcentered}2 [1{\textperiodcentered}0-6{\textperiodcentered}5] for patients with MS and MMO vs 2{\textperiodcentered}5 [0{\textperiodcentered}0-8{\textperiodcentered}0] for those without MMO; p=0{\textperiodcentered}097). The eyes of patients with MS and MMO (12 eyes) versus those without MMO (302 eyes) had lower letter-acuity scores (100\% contrast, p=0{\textperiodcentered}017; 2{\textperiodcentered}5\% contrast, p=0{\textperiodcentered}031; 1{\textperiodcentered}25\% contrast, p=0{\textperiodcentered}014), and increased INL thicknesses (p=0{\textperiodcentered}003) at baseline. Increased baseline INL thickness in patients with MS was associated with the development of contrast-enhancing lesions (p=0{\textperiodcentered}007), new T2 lesions (p=0{\textperiodcentered}015), EDSS progression (p=0{\textperiodcentered}034), and relapses in patients with relapsing-remitting MS (p=0{\textperiodcentered}008) during the study. MMO was not associated with disease activity during follow-up.

INTERPRETATION: Increased INL thickness on OCT is associated with disease activity in MS. If this finding is confirmed, INL thickness could be a useful predictor of disease progression in patients with MS.

FUNDING: National Multiple Sclerosis Society, National Eye Institute, Braxton Debbie Angela Dillon and Skip Donor Advisor Fund.

}, keywords = {Adult, Cohort Studies, Female, Follow-Up Studies, Humans, Macular Edema, Male, Middle Aged, Multiple Sclerosis, Retina, Retinal Ganglion Cells, Retrospective Studies, Tomography, Optical Coherence}, issn = {1474-4465}, doi = {10.1016/S1474-4422(12)70213-2}, author = {Saidha, Shiv and Sotirchos, Elias S and Ibrahim, Mohamed A and Crainiceanu, Ciprian M and Gelfand, Jeffrey M and Sepah, Yasir J and Ratchford, John N and Oh, Jiwon and Seigo, Michaela A and Newsome, Scott D and Balcer, Laura J and Frohman, Elliot M and Green, Ari J and Nguyen, Quan D and Calabresi, Peter A} } @article {813, title = {Movelets: A dictionary of movement.}, journal = {Electron J Stat}, volume = {6}, year = {2012}, month = {2012}, pages = {559-578}, abstract = {

Recent technological advances provide researchers with a way of gathering real-time information on an individual{\textquoteright}s movement through the use of wearable devices that record acceleration. In this paper, we propose a method for identifying activity types, like walking, standing, and resting, from acceleration data. Our approach decomposes movements into short components called "movelets", and builds a reference for each activity type. Unknown activities are predicted by matching new movelets to the reference. We apply our method to data collected from a single, three-axis accelerometer and focus on activities of interest in studying physical function in elderly populations. An important technical advantage of our methods is that they allow identification of short activities, such as taking two or three steps and then stopping, as well as low frequency rare(compared with the whole time series) activities, such as sitting on a chair. Based on our results we provide simple and actionable recommendations for the design and implementation of large epidemiological studies that could collect accelerometry data for the purpose of predicting the time series of activities and connecting it to health outcomes.

}, issn = {1935-7524}, doi = {10.1214/12-EJS684}, author = {Bai, Jiawei and Goldsmith, Jeff and Caffo, Brian and Glass, Thomas A and Crainiceanu, Ciprian M} } @article {710, title = {Relationships Between Retinal Axonal and Neuronal Measures and Global Central Nervous System Pathology in Multiple Sclerosis.}, journal = {Arch Neurol}, year = {2012}, month = {2012 Oct 1}, pages = {1-10}, abstract = {

OBJECTIVE To determine the relationships between conventional and segmentation-derived optical coherence tomography (OCT) retinal layer thickness measures with intracranial volume (a surrogate of head size) and brain substructure volumes in multiple sclerosis (MS). DESIGN Cross-sectional study. SETTING Johns Hopkins University, Baltimore, Maryland. PARTICIPANTS A total of 84 patients with MS and 24 healthy control subjects. MAIN OUTCOME MEASURES High-definition spectral-domain OCT conventional and automated segmentation-derived discrete retinal layer thicknesses and 3-T magnetic resonance imaging brain substructure volumes. RESULTS Peripapillary retinal nerve fiber layer as well as composite ganglion cell layer\ +\ inner plexiform layer thicknesses in the eyes of patients with MS without a history of optic neuritis were associated with cortical gray matter (P\ =\ .01 and P\ =\ .04, respectively) and caudate (P\ =\ .04 and P\ =\ .03, respectively) volumes. Inner nuclear layer thickness, also in eyes without a history of optic neuritis, was associated with fluid-attenuated inversion recovery lesion volume (P\ =\ .007) and inversely associated with normal-appearing white matter volume (P\ =\ .005) in relapsing-remitting MS. As intracranial volume was found to be related with several of the OCT measures in patients with MS and healthy control subjects and is already known to be associated with brain substructure volumes, all OCT-brain substructure relationships were adjusted for intracranial volume. CONCLUSIONS Retinal measures reflect global central nervous system pathology in multiple sclerosis, with thicknesses of discrete retinal layers each appearing to be associated with distinct central nervous system processes. Moreover, OCT measures appear to correlate with intracranial volume in patients with MS and healthy control subjects, an important unexpected factor unaccounted for in prior studies examining the relationships between peripapillary retinal nerve fiber layer thickness and brain substructure volumes.

}, issn = {1538-3687}, doi = {10.1001/archneurol.2013.573}, author = {Saidha, Shiv and Sotirchos, Elias S and Oh, Jiwon and Syc, Stephanie B and Seigo, Michaela A and Shiee, Navid and Eckstein, Chistopher and Durbin, Mary K and Oakley, Jonathan D and Meyer, Scott A and Frohman, Teresa C and Newsome, Scott and Ratchford, John N and Balcer, Laura J and Pham, Dzung L and Crainiceanu, Ciprian M and Frohman, Elliot M and Reich, Daniel S and Calabresi, Peter A} }