41 citations · 51 across the 15 of their papers we have counts for
16 papers
A Pipeline for ADNI Resting-State Functional MRI Processing and Quality Control
Saige Rutherford, Zeshawn Zahid, Robert C. Welsh +3
The Alzheimer's Disease Neuroimaging Initiative (ADNI) provides a comprehensive multimodal neuroimaging resource for studying aging and Alzheimer's disease (AD). Since its second w…
Bayesian brain mapping: a population-informed framework for personalized functional network topography and connectivity
Diego Derman, Nohelia Da Silva Sanchez, Damon D. Pham +3
The spatial topography of functional brain organization is increasingly recognized to play an important role in cognition and disease. Accounting for individual differences in func…
A robust multivariate, non-parametric outlier identification method for scrubbing in fMRI
Fatma Parlak, Damon D. Pham, Zeshawn Zahid +1
Functional magnetic resonance imaging (fMRI) data contain high levels of noise and artifacts. To avoid contamination of downstream analyses, fMRI-based studies must identify and re…
Fast Bayesian estimation of brain activation with cortical surface fMRI data using EM
Daniel A. Spencer, David Bolin, Amanda F. Mejia
Task functional magnetic resonance imaging (fMRI) is a type of neuroimaging data used to identify areas of the brain that activate during specific tasks or stimuli. These data are…
Sources of residual autocorrelation in multiband task fMRI and strategies for effective mitigation
Fatma Parlak, Damon D. Pham, Daniel A. Spencer +2
In task fMRI analysis, OLS is typically used to estimate task-induced activation in the brain. Since task fMRI residuals often exhibit temporal autocorrelation, it is common practi…
Fast Bayesian estimation of brain activation with cortical surface and subcortical fMRI data using EM
Daniel Spencer, David Bolin, Mary Beth Nebel +1
Analysis of brain imaging scans is critical to understanding the way the human brain functions, which can be leveraged to treat injuries and conditions that affect the quality of l…
Longitudinal surface-based spatial Bayesian GLM reveals complex trajectories of motor neurodegeneration in ALS
Amanda F. Mejia, Vincent Koppelmans, Laura Jelsone-Swain +2
Longitudinal fMRI datasets hold great promise for the study of neurodegenerative diseases, but realizing their potential depends on extracting accurate fMRI-based brain measures in…
Less is more: balancing noise reduction and data retention in fMRI with data-driven scrubbing
Damon Pham, Daniel McDonald, Lei Ding +2
Artifacts in functional MRI (fMRI) data cause deviations from common distributional assumptions, introduce spatial and temporal outliers, and reduce the signal-to-noise ratio of th…
ciftiTools: A package for reading, writing, visualizing and manipulating CIFTI files in R
Damon Pham, John Muschelli, Amanda Mejia
There is significant interest in adopting surface- and grayordinate-based analysis of MR data for a number of reasons, including improved whole-cortex visualization, the ability to…
Spatial Bayesian GLM on the cortical surface produces reliable task activations in individuals and groups
Daniel Spencer, Yu, Yue +3
The general linear model (GLM) is a widely popular and convenient tool for estimating the functional brain response and identifying areas of significant activation during a task or…
A spatial template independent component analysis model for subject-level brain network estimation and inference
Amanda F. Mejia, David Bolin, Yu Ryan Yue +3
Independent component analysis is commonly applied to functional magnetic resonance imaging (fMRI) data to extract independent components (ICs) representing functional brain networ…
Template Independent Component Analysis: Targeted and Reliable Estimation of Subject-level Brain Networks using Big Data Population Priors
Amanda F. Mejia, Mary Beth Nebel, Yikai Wang +2
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 us…
A Bayesian General Linear Modeling Approach to Cortical Surface fMRI Data Analysis
Amanda Mejia, Yu Ryan Yue, David Bolin +2
Cortical surface fMRI (cs-fMRI) has recently grown in popularity versus traditional volumetric fMRI, as it allows for more meaningful spatial smoothing and is more compatible with…
Effects of Scan Length and Shrinkage on Reliability of Resting-State Functional Connectivity in the Human Connectome Project
Amanda F. Mejia, Mary Beth Nebel, Anita D. Barber +2
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) estimat…
PCA leverage: outlier detection for high-dimensional functional magnetic resonance imaging data
Amanda F. Mejia, Mary Beth Nebel, Ani Eloyan +2
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…
Improving Reliability of Subject-Level Resting-State fMRI Parcellation with Shrinkage Estimators
Amanda F. Mejia, Mary Beth Nebel, Haochang Shou +5
A recent interest in resting state functional magnetic resonance imaging (rsfMRI) lies in subdividing the human brain into anatomically and functionally distinct regions of interes…