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- False discovery rate (1)
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- Generalized approximate cross-validation (1)
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Articles 1 - 21 of 21
Full-Text Articles in Medical Neurobiology
Mclena-1: A Phase Ii Clinical Trial For The Assessment Of Safety, Tolerability, And Efficacy Of Lenalidomide In Patients With Mild Cognitive Impairment Due To Alzheimer's Disease; Trial Design And Rationale, Boris Decourt
Annual Research Symposium
No abstract provided.
Mindfulness And Pain Regulation: The Role Of Acceptance And Commitment Therapy For Individuals With Chronic Pain, Ariana C. White
Mindfulness And Pain Regulation: The Role Of Acceptance And Commitment Therapy For Individuals With Chronic Pain, Ariana C. White
Honors Theses and Capstones
Chronic pain is a significant and widely prevalent health condition which requires comprehensive care to address the many facets contributing to symptomatology. In 2016, 20% of American adults (about 50 million) reported experiencing chronic pain, of which 7.4% indicated that chronic pain frequently limited their life and participation in activities within the past 3 months (CDC, 2018). As a result, many individuals with chronic pain turn to opioid-based medication for pain relief, but long-term use of opioids actually increases pain sensation (Tobin, 2019). Moreover, opioid medication is unable to target underlying mental health components which emerge as part of chronic …
Massively Parallel Nonparametric Regression, With An Application To Developmental Brain Mapping, Philip T. Reiss, Lei Huang, Yin-Hsiu Chen, Lan Huo, Thaddeus Tarpey, Maarten Mennes
Massively Parallel Nonparametric Regression, With An Application To Developmental Brain Mapping, Philip T. Reiss, Lei Huang, Yin-Hsiu Chen, Lan Huo, Thaddeus Tarpey, Maarten Mennes
Lei Huang
We propose a penalized spline approach to performing large numbers of parallel nonparametric analyses of either of two types: restricted likelihood ratio tests of a parametric regression model versus a general smooth alternative, and nonparametric regression. Compared with naively performing each analysis in turn, our techniques reduce computation time dramatically. Viewing the large collection of scatterplot smooths produced by our methods as functional data, we develop a clustering approach to summarize and visualize these results. Our approach is applicable to ultra-high-dimensional data, particularly data acquired by neuroimaging; we illustrate it with an analysis of developmental trajectories of functional connectivity at …
Varying-Smoother Models For Functional Responses, Philip T. Reiss, Lei Huang, Huaihou Chen, Stan Colcombe
Varying-Smoother Models For Functional Responses, Philip T. Reiss, Lei Huang, Huaihou Chen, Stan Colcombe
Philip T. Reiss
This paper studies estimation of a smooth function f(x,v) when we are given functional responses of the form f(x, ·) + error, but scientific interest centers on the collection of functions f(·,v) for different v. The motivation comes from studies of human brain development, in which x denotes age whereas v refers to brain locations. Analogously to varying-coefficient models, in which the mean response is linear in x, the “varying-smoother” models that we consider exhibit nonlinear dependence on x that varies smoothly with v. We discuss three approaches to estimating varying-smoother models: (a) methods that employ a tensor product penalty; …
Paradoxical Results Of Adaptive False Discovery Rate Procedures In Neuroimaging Studies, Philip T. Reiss, Armin Schwartzman, Feihan Lu, Lei Huang, Erika Proal
Paradoxical Results Of Adaptive False Discovery Rate Procedures In Neuroimaging Studies, Philip T. Reiss, Armin Schwartzman, Feihan Lu, Lei Huang, Erika Proal
Philip T. Reiss
Adaptive false discovery rate (FDR) procedures, which offer greater power than the original FDR procedure of Benjamini and Hochberg, are often applied to statistical maps of the brain. When a large proportion of the null hypotheses are false, as in the case of widespread effects such as cortical thinning throughout much of the brain, adaptive FDR methods can surprisingly reject more null hypotheses than not accounting for multiple testing at all—i.e., using uncorrected p-values. A straightforward mathematical argument is presented to explain why this can occur with the q-value method of Storey and colleagues, and a simulation study shows that …
Function-On-Scalar Regression With The Refund Package, Philip T. Reiss
Function-On-Scalar Regression With The Refund Package, Philip T. Reiss
Philip T. Reiss
No abstract provided.
Smoothness Selection For Penalized Quantile Regression Splines, Philip T. Reiss, Lei Huang
Smoothness Selection For Penalized Quantile Regression Splines, Philip T. Reiss, Lei Huang
Philip T. Reiss
Modern data-rich analyses may call for fitting a large number of nonparametric quantile regressions. For example, growth charts may be constructed for each of a collection of variables, to identify those for which individuals with a disorder tend to fall in the tails of their age-specific distribution; such variables might serve as developmental biomarkers. When such analyses are carried out by penalized spline smoothing, reliable automatic selection of the smoothing parameter is particularly important. We show that two popular methods for smoothness selection may tend to overfit when estimating extreme quantiles as a smooth function of a predictor such as …
Semiparametric Methods For Mapping Brain Development, Philip T. Reiss, Yin-Hsiu Chen, Lan Huo
Semiparametric Methods For Mapping Brain Development, Philip T. Reiss, Yin-Hsiu Chen, Lan Huo
Philip T. Reiss
No abstract provided.
Introducing Functional Data Analysis To Neuroimaging, And Vice Versa, Philip T. Reiss
Introducing Functional Data Analysis To Neuroimaging, And Vice Versa, Philip T. Reiss
Philip T. Reiss
No abstract provided.
Massively Parallel Nonparametrics [Hds 2011 Slides], Philip T. Reiss, Lei Huang
Massively Parallel Nonparametrics [Hds 2011 Slides], Philip T. Reiss, Lei Huang
Philip T. Reiss
No abstract provided.
Flexible Dependence Of Functional Responses On Scalar Predictors, Philip T. Reiss, Lei Huang
Flexible Dependence Of Functional Responses On Scalar Predictors, Philip T. Reiss, Lei Huang
Philip T. Reiss
No abstract provided.
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 …
Functional Generalized Linear Models With Images As Predictors, Philip T. Reiss, R. Todd Ogden
Functional Generalized Linear Models With Images As Predictors, Philip T. Reiss, R. Todd Ogden
Philip T. Reiss
Functional principal component regression (FPCR) is a promising new method for regressing scalar outcomes on functional predictors. In this paper we present a theoretical justification for the use of principal components in functional regression. FPCR is then extended in two directions: from linear to the generalized linear modeling, and from univariate signal predictors to high-resolution image predictors. We show how to implement the method efficiently by adapting generalized additive model technology to the functional regression context. A technique is proposed for estimating simultaneous confidence bands for the coefficient function; in the neuroimaging setting, this yields a novel means to identify …
Regression When The Predictors Are Images, Philip T. Reiss
Regression When The Predictors Are Images, Philip T. Reiss
Philip T. Reiss
No abstract provided.
Simultaneous Confidence Bands For The Coefficient Function In Functional Regression, Philip T. Reiss
Simultaneous Confidence Bands For The Coefficient Function In Functional Regression, Philip T. Reiss
Philip T. Reiss
No abstract provided.
Inferring Group Differences In Brain Connectivity From Functional Magnetic Resonance Images, Philip T. Reiss
Inferring Group Differences In Brain Connectivity From Functional Magnetic Resonance Images, Philip T. Reiss
Philip T. Reiss
No abstract provided.
Reliability Of Functional Connectivity Networks: How Can We Assess It?, Philip T. Reiss
Reliability Of Functional Connectivity Networks: How Can We Assess It?, Philip T. Reiss
Philip T. Reiss
No abstract provided.
Functional Generalized Linear Models With Applications To Neuroimaging, Philip T. Reiss, R. Todd Ogden
Functional Generalized Linear Models With Applications To Neuroimaging, Philip T. Reiss, R. Todd Ogden
Philip T. Reiss
No abstract provided.
Laser Capture Sampling And Analytical Issues In Proteomics, Howard Gutstein, Jeffrey S. Morris
Laser Capture Sampling And Analytical Issues In Proteomics, Howard Gutstein, Jeffrey S. Morris
Jeffrey S. Morris
Proteomics holds the promise of evaluating global changes in protein expression and post-translational modificaiton in response to environmental stimuli. However, difficulties in achieving cellular anatomic resolution and extracting specific types of proteins from cells have limited the efficacy of these techniques. Laser capture microdissection has provided a solution to the problem of anatomical resolution in tissues. New extraction methodologies have expanded the range of proteins identified in subsequent analyses. This review will examine the application of laser capture microdissection to proteomic tissue sampling, and subsequent extraction of these samples for differential expression analysis. Statistical and other quantitative issues important for …
Cross-Modal Enhancement Of Perceived Brightness: Sensory Interaction Versus Response Bias, Yoav Arieh, Lawrence E. Marks, Eric C. Odgaard
Cross-Modal Enhancement Of Perceived Brightness: Sensory Interaction Versus Response Bias, Yoav Arieh, Lawrence E. Marks, Eric C. Odgaard
Department of Psychology Faculty Scholarship and Creative Works
Stein, London, Wilkinson, and Price (1996) reported the presence of cross-modal enhancement of perceived visual intensity: Participants tended to rate weak lights as brighter when accompanied by a concurrent pulse of white noise than when presented alone. In the present study, two methods were used to determine whether the enhancement reflects an early-stage sensory process or a later-stage decisional process, such as a response bias. First, the enhancement was eliminated when the noise accompanied the light on only 25% versus 50% of the trials. Second, the enhancement was absent when tested with a paired-comparison method. These findings are consistent with …