Cognitive Science Program

Open data on industry payments to healthcare providers reveal potential hidden costs to the public (2019)
Jorge Mejia, Amanda Mejia, Franco Pestilli
Nature communications, 10 (1), 1-8

Abstract

Healthcare industry players make payments to medical providers for non-research expenses. While these payments may pose conflicts of interest, their relationship with overall healthcare costs remains largely unknown. In this study, we linked Open Payments data on providers’ industry payments with Medicare data on healthcare costs. We investigated 374,766 providers’ industry payments and healthcare costs. We demonstrate that providers receiving higher amounts of industry payments tend to bill higher drug and medical costs. Specifically, we find that a 10% increase in industry payments is associated with 1.3% higher medical and 1.8% higher drug costs. For a typical provider, for example, a 10% or $25 increase in annual industry payments would be associated with approximately $1,100 higher medical costs and $100 higher drug costs. Furthermore, the association between payments and healthcare costs …

Template Independent Component Analysis: Targeted and Reliable Estimation of Subject-level Brain Networks using Big Data Population Priors (2019)
Amanda F Mejia, Mary Beth Nebel, Yikai Wang, Brian S Caffo, Ying Guo
arXiv preprint arXiv:1906.07294, 1-58

Abstract

Large brain imaging databases contain a wealth of information on brain organization in the populations they target, and on individual variability. While such databases have been used to study group-level features of populations directly, they are currently underutilized as a resource to inform single-subject analysis. Here, we propose leveraging the information contained in large functional magnetic resonance imaging (fMRI) databases by establishing population priors to employ in an empirical Bayesian framework. We focus on estimation of brain networks as source signals in independent component analysis (ICA). We formulate a hierarchical" template" ICA model where source signals---including known population brain networks and subject-specific signals---are represented as latent variables. For estimation, we derive an expectation maximization (EM) algorithm having an explicit solution. However, as this solution is computationally intractable, we also consider an approximate subspace algorithm and a faster two-stage approach. Through extensive simulation studies, we assess performance of both methods and compare with dual regression, a popular but ad-hoc method. The two proposed algorithms have similar performance, and both dramatically outperform dual regression. We also conduct a reliability study utilizing the Human Connectome Project and find that template ICA achieves substantially better performance than dual regression, achieving 75-250% higher intra-subject reliability.

A Bayesian general linear modeling approach to cortical surface fMRI data analysis (2019)
Amanda F Mejia, Yu Yue, David Bolin, Finn Lindgren, Martin A Lindquist
Journal of the American Statistical Association, 1-26

Abstract

Cortical surface functional magnetic resonance imaging (cs-fMRI) has recently grown in popularity versus traditional volumetric fMRI. In addition to offering better whole-brain visualization, dimension reduction, removal of extraneous tissue types, and improved alignment of cortical areas across subjects, it is also more compatible with common assumptions of Bayesian spatial models. However, as no spatial Bayesian model has been proposed for cs-fMRI data, most analyses continue to employ the classical general linear model (GLM), a “massive univariate” approach. Here, we propose a spatial Bayesian GLM for cs-fMRI, which employs a class of sophisticated spatial processes to model latent activation fields. We make several advances compared with existing spatial Bayesian models for volumetric fMRI. First, we use integrated nested Laplacian approximations, a highly accurate and efficient Bayesian …

Improved estimation of subject-level functional connectivity using full and partial correlation with empirical Bayes shrinkage (2018)
Amanda F Mejia, Mary Beth Nebel, Anita D Barber, Ann S Choe, James J Pekar, Brian S Caffo, Martin A Lindquist
NeuroImage, 172 478-491

Abstract

Reliability of subject-level resting-state functional connectivity (FC) is determined in part by the statistical techniques employed in its estimation. Methods that pool information across subjects to inform estimation of subject-level effects (e.g., Bayesian approaches) have been shown to enhance reliability of subject-level FC. However, fully Bayesian approaches are computationally demanding, while empirical Bayesian approaches typically rely on using repeated measures to estimate the variance components in the model. Here, we avoid the need for repeated measures by proposing a novel measurement error model for FC describing the different sources of variance and error, which we use to perform empirical Bayes shrinkage of subject-level FC towards the group average. In addition, since the traditional intra-class correlation coefficient (ICC) is inappropriate for biased estimates, we propose a new reliability …

Big Data and Neuroimaging (2017)
Yenny Webb-Vargas, Shaojie Chen, Aaron Fisher, Amanda Mejia, Yuting Xu, Ciprian Crainiceanu, Brian Caffo, Martin A Lindquist
Statistics in biosciences, 9 (2), 543-558

Abstract

Big Data are of increasing importance in a variety of areas, especially in the biosciences. There is an emerging critical need for Big Data tools and methods, because of the potential impact of advancements in these areas. Importantly, statisticians and statistical thinking have a major role to play in creating meaningful progress in this arena. We would like to emphasize this point in this special issue, as it highlights both the dramatic need for statistical input for Big Data analysis and for a greater number of statisticians working on Big Data problems. We use the field of statistical neuroimaging to demonstrate these points. As such, this paper covers several applications and novel methodological developments of Big Data tools applied to neuroimaging data.

PCA leverage: outlier detection for high-dimensional functional magnetic resonance imaging data (2017)
Amanda F Mejia, Mary Beth Nebel, Ani Eloyan, Brian Caffo, Martin A Lindquist
Biostatistics, 18 (3), 521-536

Abstract

Outlier detection for high-dimensional (HD) data is a popular topic in modern statistical research. However, one source of HD data that has received relatively little attention is functional magnetic resonance images (fMRI), which consists of hundreds of thousands of measurements sampled at hundreds of time points. At a time when the availability of fMRI data is rapidly growing—primarily through large, publicly available grassroots datasets—automated quality control and outlier detection methods are greatly needed. We propose principal components analysis (PCA) leverage and demonstrate how it can be used to identify outlying time points in an fMRI run. Furthermore, PCA leverage is a measure of the influence of each observation on the estimation of principal components, which are often of interest in fMRI data. We also propose an alternative measure, PCA robust distance, which is less sensitive to …

Independent association of severity of muscle weakness with disability as measured by the health assessment questionnaire disability index in scleroderma (2016)
Julie J Paik, Fredrick M Wigley, Amanda F Mejia, Laura K Hummers
Arthritis care & research, 68 (11), 1695-1703

Abstract

Objective To determine whether the presence and degree of muscle weakness in scleroderma is associated with disability. Methods The study included a cohort of 1,718 scleroderma patients who had available data on muscle strength and disability. The primary independent variable was muscle weakness as defined by the maximum Medsger muscle severity score and the outcome was disability as measured by the last recorded Health Assessment Questionnaire disability index (HAQ DI) score. Univariate regression analyses were performed to assess the association of HAQ DI scores with the Medsger muscle severity score and other scleroderma characteristics. A multivariate regression analysis was performed to determine whether an association existed between the degree of muscle weakness and disability, while controlling for confounders. Results In 1,718 patients with scleroderma, 22.8% (392 of 1,718 …

Effects of Scan Length and Shrinkage on Reliability of Resting-State Functional Connectivity in the Human Connectome Project (2016)
Amanda F Mejia, Mary Beth Nebel, Anita D Barber, Ann S Choe, Martin A Lindquist
arXiv preprint arXiv:1606.06284, 1-26

Abstract

In this paper, we use data from the Human Connectome Project (N= 461) to investigate the effect of scan length on reliability of resting-state functional connectivity (rsFC) estimates produced from resting-state functional magnetic resonance imaging (rsfMRI). Additionally, we study the benefits of empirical Bayes shrinkage, in which subject-level estimates borrow strength from the population average by trading a small increase in bias for a greater reduction in variance. For each subject, we compute raw and shrinkage estimates of rsFC between 300 regions identified through independent components analysis (ICA) based on rsfMRI scans varying from 3 to 30 minutes in length. The time course for each region is determined using dual regression, and rsFC is estimated as the Pearson correlation between each pair of time courses. Shrinkage estimates for each subject are computed as a weighted average between the raw subject-level estimate and the population average estimate, where the weight is determined for each connection by the relationship of within-subject variance to between-subject variance. We find that shrinkage estimates exhibit greater reliability than raw estimates for most connections, with 30-40% improvement using scans less than 10 minutes in length and 10-20% improvement using scans of 20-30 minutes. We also observe significant spatial variability in reliability of both raw and shrinkage estimates, with connections within the default mode and motor networks exhibiting the greatest reliability and between-network connections exhibiting the poorest reliability. We conclude that the scan length required for reliable estimation …

Statistical estimation of T1 relaxation times using conventional magnetic resonance imaging (2016)
Amanda F Mejia, Elizabeth M Sweeney, Blake Dewey, Govind Nair, Pascal Sati, Colin Shea, Daniel S Reich, Russell T Shinohara
NeuroImage, 133 176-188

Abstract

Quantitative T1 maps estimate T1 relaxation times and can be used to assess diffuse tissue abnormalities within normal-appearing tissue. T1 maps are popular for studying the progression and treatment of multiple sclerosis (MS). However, their inclusion in standard imaging protocols remains limited due to the additional scanning time and expert calibration required and susceptibility to bias and noise. Here, we propose a new method of estimating T1 maps using four conventional MR images, which are intensity-normalized using cerebellar gray matter as a reference tissue and related to T1 using a smooth regression model. Using cross-validation, we generate statistical T1 maps for 61 subjects with MS. The statistical maps are less noisy than the acquired maps and show similar reproducibility. Tests of group differences in normal-appearing white matter across MS subtypes give similar results using both methods.

Statistical Methods for Functional Magnetic Resonance Imaging Data (2016)
Amanda Mejia
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Abstract

Understanding how the brain functions is one of the most important goals in science and medicine today. Functional magnetic resonance imaging (fMRI) is a noninvasive, widely used technology for studying brain function in humans. While fMRI has great potential to shed light on cognitive development, decline and disorders, it also presents statistical and computational challenges due to a myriad of sources of noise and the large size of the data. In this thesis, I propose several methods to improve the analysis of resting-state fMRI, which is used to understand connectivity between different regions of the brain. Specifically, this thesis addresses two primary themes. First, I propose shrinkage estimators for functional connectivity, which improve reliability of subject-level estimates by "borrowing strength" across subjects. Second, I propose a method of identifying artifacts in fMRI data through a novel high-dimensional outlier detection method. The proposed methods can be used together and have the potential to significantly improve our understanding of brain connectivity at the subject level using resting-state fMRI.

Statistical estimation of white matter microstructure from conventional MRI (2016)
Leah H Suttner, Amanda Mejia, Blake Dewey, Pascal Sati, Daniel S Reich, Russell T Shinohara
NeuroImage: Clinical, 12 (), 615-623

Abstract

Diffusion tensor imaging (DTI) has become the predominant modality for studying white matter integrity in multiple sclerosis (MS) and other neurological disorders. Unfortunately, the use of DTI-based biomarkers in large multi-center studies is hindered by systematic biases that confound the study of disease-related changes. Furthermore, the site-to-site variability in multi-center studies is significantly higher for DTI than that for conventional MRI-based markers. In our study, we apply the Quantitative MR Estimation Employing Normalization (QuEEN) model to estimate the four DTI measures: MD, FA, RD, and AD. QuEEN uses a voxel-wise generalized additive regression model to relate the normalized intensities of one or more conventional MRI modalities to a quantitative modality, such as DTI. We assess the accuracy of the models by comparing the prediction error of estimated DTI images to the scan-rescan error in …

A lag functional linear model for prediction of magnetization transfer ratio in multiple sclerosis lesions (2016)
Gina-Maria Pomann, Ana-Maria Staicu, Edgar J Lobaton, Amanda F Mejia, Blake E Dewey, Daniel S Reich, Elizabeth M Sweeney, Russell T Shinohara
The Annals of Applied Statistics, 10 (4), 2325-2348

Abstract

We propose a lag functional linear model to predict a response using multiple functional predictors observed at discrete grids with noise. Two procedures are proposed to estimate the regression parameter functions:(1) an approach that ensures smoothness for each value of time using generalized cross-validation; and (2) a global smoothing approach using a restricted maximum likelihood framework. Numerical studies are presented to analyze predictive accuracy in many realistic scenarios. The methods are employed to estimate a magnetic resonance imaging (MRI)-based measure of tissue damage (the magnetization transfer ratio, or MTR) in multiple sclerosis (MS) lesions, a disease that causes damage to the myelin sheaths around axons in the central nervous system. Our method of estimation of MTR within lesions is useful retrospectively in research applications where MTR was not acquired, as well as in …

Improving reliability of subject-level resting-state fMRI parcellation with shrinkage estimators (2015)
Amanda F Mejia, Mary Beth Nebel, Haochang Shou, Ciprian M Crainiceanu, James J Pekar, Stewart Mostofsky, Brian Caffo, Martin A Lindquist
NeuroImage, 112 (), 14-29

Abstract

A recent interest in resting state functional magnetic resonance imaging (rsfMRI) lies in subdividing the human brain into anatomically and functionally distinct regions of interest. For example, brain parcellation is often a necessary step for defining the network nodes used in connectivity studies. While inference has traditionally been performed on group-level data, there is a growing interest in parcellating single subject data. However, this is difficult due to the inherent low signal-to-noise ratio of rsfMRI data, combined with typically short scan lengths. A large number of brain parcellation approaches employ clustering, which begins with a measure of similarity or distance between voxels. The goal of this work is to improve the reproducibility of single-subject parcellation using shrinkage-based estimators of such measures, allowing the noisy subject-specific estimator to “borrow strength” in a principled manner from a …

Evidence for specificity of motor impairments in catching and balance in children with autism (2015)
Katarina Ament, Amanda Mejia, Rebecca Buhlman, Shannon Erklin, Brian Caffo, Stewart Mostofsky, Ericka Wodka
Journal of autism and developmental disorders, 45 (3), 742-751

Abstract

To evaluate evidence for motor impairment specificity in autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD). Children completed performance-based assessment of motor functioning (Movement Assessment Battery for Children: MABC-2). Logistic regression models were used to predict group membership. In the models comparing typically developing and developmental disability (DD), all three MABC subscale scores were significantly negatively associated with having a DD. In the models comparing ADHD and ASD, catching and static balance items were associated with ASD group membership, with a 1 point decrease in performance increasing odds of ASD by 36 and 39 %, respectively. Impairments in motor skills requiring the coupling of visual and temporal feedback to guide and adjust movement appear specifically deficient in ASD.

Left‐hemispheric microstructural abnormalities in children with high‐functioning autism spectrum disorder (2015)
Daniel Peterson, Rajneesh Mahajan, Deana Crocetti, Amanda Mejia, Stewart Mostofsky
Autism Research , 8 (1), 61-72

Abstract

Current theories of the neurobiological basis of autism spectrum disorder (ASD) posit an altered pattern of connectivity in large‐scale brain networks. Here we used diffusion tensor imaging to investigate the microstructural properties of the white matter (WM) that mediates interregional connectivity in 36 high‐functioning children with ASD (HF‐ASD) as compared with 37 controls. By employing an atlas‐based analysis using large deformation diffeometric morphic mapping registration, a widespread but left‐lateralized pattern of abnormalities was revealed. The mean diffusivity (MD) of water in the WM of HF‐ASD children was significantly elevated throughout the left hemisphere, particularly in the outer‐zone cortical WM. Across diagnostic groups, there was a significant effect of age on left‐hemisphere MD, with a similar reduction in MD during childhood in both typically developing and HF‐ASD children. The …