Search

We propose a new fast generalized functional principal components analysis (fast-GFPCA) algorithm for dimension reduction of non-Gaussian functional data. The method consists of: (1) binning the data within the functional domain; (2) fitting local random intercept generalized linear mixed models in every bin to obtain the initial estimates of the person-specific functional linear predictors; (3) using fast functional principal component analysis to smooth the linear predictors and obtain their eigenfunctions; and (4) estimating the global model conditional on the eigenfunctions of the linear predictors. An extensive simulation study shows that fast-GFPCA performs as well or better than existing state-of-the-art approaches, it is orders of magnitude faster than existing general purpose GFPCA methods, and scales up well with both the number of observed curves and observations per curve. Methods were motivated by and applied to a study of active/inactive physical activity profiles obtained from wearable accelerometers in the NHANES 2011-2014 study. The method can be implemented by any user familiar with mixed model software, though the R package fastGFPCA is provided for convenience.

Supplemental material, MSJ900942_Supplementary_table_2 for Association of body mass index with longitudinal rates of retinal atrophy in multiple sclerosis by Angeliki G Filippatou, Jeffrey Lambe, Elias S Sotirchos, Kathryn C Fitzgerald, Andrew Aston, Olwen C Murphy, Nicole Pellegrini, Nicholas Fioravante, Hunter Risher, Esther Ogbuokiri, Ohemaa Kwakyi, Brandon Toliver, Simidele Davis, Nicholas Luciano, Ciprian Crainiceanu, Jerry L Prince, Ellen M Mowry, Peter A Calabresi and Shiv Saidha in Multiple Sclerosis Journal

Supplemental material, MSJ900942_Supplementary_table_3 for Association of body mass index with longitudinal rates of retinal atrophy in multiple sclerosis by Angeliki G Filippatou, Jeffrey Lambe, Elias S Sotirchos, Kathryn C Fitzgerald, Andrew Aston, Olwen C Murphy, Nicole Pellegrini, Nicholas Fioravante, Hunter Risher, Esther Ogbuokiri, Ohemaa Kwakyi, Brandon Toliver, Simidele Davis, Nicholas Luciano, Ciprian Crainiceanu, Jerry L Prince, Ellen M Mowry, Peter A Calabresi and Shiv Saidha in Multiple Sclerosis Journal

Supplemental material, MSJ900942_Supplementary_table_1 for Association of body mass index with longitudinal rates of retinal atrophy in multiple sclerosis by Angeliki G Filippatou, Jeffrey Lambe, Elias S Sotirchos, Kathryn C Fitzgerald, Andrew Aston, Olwen C Murphy, Nicole Pellegrini, Nicholas Fioravante, Hunter Risher, Esther Ogbuokiri, Ohemaa Kwakyi, Brandon Toliver, Simidele Davis, Nicholas Luciano, Ciprian Crainiceanu, Jerry L Prince, Ellen M Mowry, Peter A Calabresi and Shiv Saidha in Multiple Sclerosis Journal

ObjectiveTo summarize current and emerging imaging techniques that can be used to assess neuroprotection and repair in multiple sclerosis (MS), and to provide a consensus opinion on the potential utility of each technique in clinical trial settings.MethodsClinicians and scientists with expertise in the use of MRI in MS convened in Toronto, Canada, in November 2016 at a North American Imaging in Multiple Sclerosis (NAIMS) Cooperative workshop meeting. The discussion was compiled into a manuscript and circulated to all NAIMS members in attendance. Edits and feedback were incorporated until all authors were in agreement.ResultsA wide spectrum of imaging techniques and analysis methods in the context of specific study designs were discussed, with a focus on the utility and limitations of applying each technique to assess neuroprotection and repair. Techniques were discussed under specific themes, and included conventional imaging, magnetization transfer ratio, diffusion tensor imaging, susceptibility-weighted imaging, imaging cortical lesions, magnetic resonance spectroscopy, PET, advanced diffusion imaging, sodium imaging, multimodal techniques, imaging of special regions, statistical considerations, and study design.ConclusionsImaging biomarkers of neuroprotection and repair are an unmet need in MS. There are a number of promising techniques with different strengths and limitations, and selection of a specific technique will depend on a number of factors, notably the question the trial seeks to answer. Ongoing collaborative efforts will enable further refinement and improved methods to image the effect of novel therapeutic agents that exert benefit in MS predominately through neuroprotective and reparative mechanisms.

Supplemental material, MSJ826364_supplementary_statistical_methods for Effect of disease-modifying therapies on subcortical gray matter atrophy in multiple sclerosis by Elias S Sotirchos, Natalia Gonzalez-Caldito, Blake E Dewey, Kathryn C Fitzgerald, Jeffrey Glaister, Angeliki Filippatou, Esther Ogbuokiri, Sydney Feldman, Ohemaa Kwakyi, Hunter Risher, Ciprian Crainiceanu, Dzung L Pham, Peter C Van Zijl, Ellen M Mowry, Daniel S Reich, Jerry L Prince, Peter A Calabresi and Shiv Saidha in Multiple Sclerosis Journal

Supplemental material, MSJ826364_supplementary_results for Effect of disease-modifying therapies on subcortical gray matter atrophy in multiple sclerosis by Elias S Sotirchos, Natalia Gonzalez-Caldito, Blake E Dewey, Kathryn C Fitzgerald, Jeffrey Glaister, Angeliki Filippatou, Esther Ogbuokiri, Sydney Feldman, Ohemaa Kwakyi, Hunter Risher, Ciprian Crainiceanu, Dzung L Pham, Peter C Van Zijl, Ellen M Mowry, Daniel S Reich, Jerry L Prince, Peter A Calabresi and Shiv Saidha in Multiple Sclerosis Journal

The North American Imaging in Multiple Sclerosis (NAIMS) Cooperative represents a network of 27 academic centers focused on accelerating the pace of magnetic resonance imaging (MRI) research in multiple sclerosis (MS) through idea exchange and collaboration. Recently, NAIMS completed its first project evaluating the feasibility of implementation and reproducibility of quantitative MRI measures derived from scanning a single MS patient using a high-resolution 3T protocol at seven sites. The results showed the feasibility of utilizing advanced quantitative MRI measures in multicenter studies and demonstrated the importance of careful standardization of scanning protocols, central image processing, and strategies to account for inter-site variability.

Background: We propose a method for estimating the timing of in-bed intervals using objective data in a large representative U.S. sample, and quantify the association between these intervals and age, sex, and day of the week. Methods: The study included 11,951 participants six years and older from the National Health and Nutrition Examination Survey (NHANES) 2003-2006, who wore accelerometers to measure physical activity for seven consecutive days. Participants were instructed to remove the device just before the nighttime sleep period and put it back on immediately after. This nighttime period of non-wear was defined in this paper as the objective bedtime (OBT), an objectively estimated record of the in-bed-interval. For each night of the week, we estimated two measures: the duration of the OBT (OBT-D) and, as a measure of the chronotype, the midpoint of the OBT (OBT-M). We estimated day-of-the-week-specific OBT-D and OBT-M using gender-specific population percentile curves. Differences in OBT-M (chronotype) and OBT-D (the amount of time spent in bed) by age and sex were estimated using regression models. Results: The estimates of OBT-M and their differences among age groups were consistent with the estimates of chronotype obtained via self-report in European populations. The average OBT-M varied significantly by age, while OBT-D was less variable with age. The most pronounced differences were observed between OBT-M of weekday and weekend nights. Conclusions: The proposed measures, OBT-D and OBT-M, provide useful information of time in bed and chronotype in NHANES 2003-2006. They identify within-week patterns of bedtime and can be used to study associations between the bedtime and the large number of health outcomes collected in NHANES 2003-2006.

Additional file 1. Examples of statistical code in R.

Smoothing of noisy sample covariances is an important component in functional data analysis. We propose a novel covariance smoothing method based on penalized splines and associated software. The proposed method is a bivariate spline smoother that is designed for covariance smoothing and can be used for sparse functional or longitudinal data. We propose a fast algorithm for covariance smoothing using leave-one-subject-out cross validation. Our simulations show that the proposed method compares favorably against several commonly used methods. The method is applied to a study of child growth led by one of coauthors and to a public dataset of longitudinal CD4 counts., Comment: 29 pages, 8 figures

Structural magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple sclerosis (MS) patients. The formation of these lesions is a complex process involving inflammation, tissue damage, and tissue repair, all of which are visible on MRI. Here we characterize the lesion formation process on longitudinal, multi-sequence structural MRI from 34 MS patients and relate the longitudinal changes we observe within lesions to therapeutic interventions. In this article, we first outline a pipeline to extract voxel level, multi-sequence longitudinal profiles from four MRI sequences within lesion tissue. We then propose two models to relate clinical covariates to the longitudinal profiles. The first model is a principal component analysis (PCA) regression model, which collapses the information from all four profiles into a scalar value. We find that the score on the first PC identifies areas of slow, long-term intensity changes within the lesion at a voxel level, as validated by two experienced clinicians, a neuroradiologist and a neurologist. On a quality scale of 1 to 4 (4 being the highest) the neuroradiologist gave the score on the first PC a median rating of 4 (95% CI: [4,4]), and the neurologist gave it a median rating of 3 (95% CI: [3,3]). In the PCA regression model, we find that treatment with disease modifying therapies (p-value < 0.01), steroids (p-value < 0.01), and being closer to the boundary of abnormal signal intensity (p-value < 0.01) are associated with a return of a voxel to intensity values closer to that of normal-appearing tissue. The second model is a function-on-scalar regression, which allows for assessment of the individual time points at which the covariates are associated with the profiles. In the function-on-scalar regression both age and distance to the boundary were found to have a statistically significant association with the profiles.