@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 {835,
	title = {Evaluating dynamic bivariate correlations in resting-state fMRI: a comparison study and a new approach.},
	journal = {Neuroimage},
	volume = {101},
	year = {2014},
	month = {2014 Nov 1},
	pages = {531-46},
	abstract = {<p>To date, most functional Magnetic Resonance Imaging (fMRI) studies have assumed that the functional connectivity (FC) between time series from distinct brain regions is constant across time. However, recently, there has been an increased interest in quantifying possible dynamic changes in FC during fMRI experiments, as it is thought that this may provide insight into the fundamental workings of brain networks. In this work we focus on the specific problem of estimating the dynamic behavior of pair-wise correlations between time courses extracted from two different regions of the brain. We critique the commonly used sliding-window technique, and discuss some alternative methods used to model volatility in the finance literature that could also prove to be useful in the neuroimaging setting. In particular, we focus on the Dynamic Conditional Correlation (DCC) model, which provides a model-based approach towards estimating dynamic correlations. We investigate the properties of several techniques in a series of simulation studies and find that DCC achieves the best overall balance between sensitivity and specificity in detecting dynamic changes in correlations. We also investigate its scalability beyond the bivariate case to demonstrate its utility for studying dynamic correlations between more than two brain regions. Finally, we illustrate its performance in an application to test-retest resting state fMRI data.</p>},
	issn = {1095-9572},
	doi = {10.1016/j.neuroimage.2014.06.052},
	author = {Lindquist, Martin A and Xu, Yuting and Nebel, Mary Beth and Caffo, Brain S}
}
@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 = {<p>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.</p>},
	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}
}
