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Full-Text Articles in Medical Biomathematics and Biometrics
Extracting Information From Functional Connectivity Maps Via Function-On-Scalar Regression, Philip T. Reiss, Maarten Mennes, Eva Petkova, Lei Huang, Matthew J. Hoptman, Bharat B. Biswal, Stanley J. Colcombe, Xi-Nian Zuo, Michael P. Milham
Extracting Information From Functional Connectivity Maps Via Function-On-Scalar Regression, Philip T. Reiss, Maarten Mennes, Eva Petkova, Lei Huang, Matthew J. Hoptman, Bharat B. Biswal, Stanley J. Colcombe, Xi-Nian Zuo, Michael P. Milham
Lei Huang
Functional connectivity of an individual human brain is often studied by acquiring a resting state functional magnetic resonance imaging scan, and mapping the correlation of each voxel's BOLD time series with that of a seed region. As large collections of such maps become available, including multisite data sets, there is an increasing need for ways to distill the information in these maps in a readily visualized form. Here we propose a two-step analytic strategy. First, we construct connectivity-distance profiles, which summarize the connectivity of each voxel in the brain as a function of distance from the seed, a functional relationship …
Extracting Information From Functional Connectivity Maps Via Function-On-Scalar Regression, Philip T. Reiss, Maarten Mennes, Eva Petkova, Lei Huang, Matthew J. Hoptman, Bharat B. Biswal, Stanley J. Colcombe, Xi-Nian Zuo, Michael P. Milham
Extracting Information From Functional Connectivity Maps Via Function-On-Scalar Regression, Philip T. Reiss, Maarten Mennes, Eva Petkova, Lei Huang, Matthew J. Hoptman, Bharat B. Biswal, Stanley J. Colcombe, Xi-Nian Zuo, Michael P. Milham
Philip T. Reiss
Functional connectivity of an individual human brain is often studied by acquiring a resting state functional magnetic resonance imaging scan, and mapping the correlation of each voxel's BOLD time series with that of a seed region. As large collections of such maps become available, including multisite data sets, there is an increasing need for ways to distill the information in these maps in a readily visualized form. Here we propose a two-step analytic strategy. First, we construct connectivity-distance profiles, which summarize the connectivity of each voxel in the brain as a function of distance from the seed, a functional relationship …
Wavelet-Based Functional Mixed Models To Characterize Population Heterogeneity In Accelerometer Profiles: A Case Study. , Jeffrey S. Morris, Cassandra Arroyo, Brent A. Coull, Louise M. Ryan, Steven L. Gortmaker
Wavelet-Based Functional Mixed Models To Characterize Population Heterogeneity In Accelerometer Profiles: A Case Study. , Jeffrey S. Morris, Cassandra Arroyo, Brent A. Coull, Louise M. Ryan, Steven L. Gortmaker
Jeffrey S. Morris
We present a case study illustrating the challenges of analyzing accelerometer data taken from a sample of children participating in an intervention study designed to increase physical activity. An accelerometer is a small device worn on the hip that records the minute-by-minute activity levels of the child throughout the day for each day it is worn. The resulting data are irregular functions characterized by many peaks representing short bursts of intense activity. We model these data using the wavelet-based functional mixed model. This approach incorporates multiple fixed effects and random effect functions of arbitrary form, the estimates of which are …
Wavelet-Based Nonparametric Modeling Of Hierarchical Functions In Colon Carcinogenesis., Jeffrey S. Morris, Marina Vannucci, Philip J. Brown, Raymond J. Carroll
Wavelet-Based Nonparametric Modeling Of Hierarchical Functions In Colon Carcinogenesis., Jeffrey S. Morris, Marina Vannucci, Philip J. Brown, Raymond J. Carroll
Jeffrey S. Morris
In this article we develop new methods for analyzing the data from an experiment using rodent models to investigate the effect of type of dietary fat on O6-methylguanine-DNA-methyltransferase (MGMT), an important biomarker in early colon carcinogenesis. The data consist of observed profiles over a spatial variable contained within a two-stage hierarchy, a structure that we dub hierarchical functional data. We present a new method providing a unified framework for modeling these data, simultaneously yielding estimates and posterior samples for mean, individual, and subsample-level profiles, as well as covariance parameters at the various hierarchical levels. Our method is nonparametric in that …