@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 {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 {Bai2014Metrics, title = {Normalization and extraction of interpretable metrics from raw accelerometry data}, journal = {Biostatistics}, volume = {15}, number = {1}, year = {2014}, pages = {102{\textendash}116}, 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 = {Activity intensity, Movelets, Movement, Signal processing, Time active, Tri-axial accelerometer}, issn = {14654644}, author = {Bai, Jiawei and He, Bing and Shou, Haochang and Zipunnikov, Vadim and Glass, Thomas A. 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 {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} }