6 citations · 15 across the 13 of their papers we have counts for
15 papers
Causal Inference for Unobservable Multivariate Outcomes, with Applications to Brain Effective Connectivity
Haiyue Song, Ani Eloyan, Youjin Lee
Evaluating the causal effect of an intervention on multivariate outcomes is challenging when the outcomes are interdependent and derived rather than directly observed. Effective co…
TRAECR: A Tool for Preprocessing Positron Emission Tomography Imaging for Statistical Modeling
Akhil Ambekar, Robert Zielinski, Ani Eloyan
Positron emission tomography (PET) imaging is widely used in a number of clinical applications, including cancer and Alzheimer's disease (AD) diagnosis, monitoring of disease devel…
Geometric Modeling of Hippocampal Tau Deposition: A Surface-Based Framework for Covariate Analysis and Off-Target Contamination Detection
Liangkang Wang, Akhil Ambekar, Ani Eloyan
We introduce a framework combining geometric modeling with disease progression analysis to investigate tau deposition in Alzheimer's disease (AD) using positron emission tomography…
Longitudinal Principal Manifold Estimation
Robert Zielinski, Kun Meng, Ani Eloyan
Longitudinal magnetic resonance imaging data is used to model trajectories of change in brain regions of interest to identify areas susceptible to atrophy in those with neurodegene…
Leveraging population information in brain connectivity via Bayesian ICA with a novel informative prior for correlation matrices
Amanda Mejia, David Bolin, Daniel Spencer +1
Brain functional connectivity (FC), the temporal synchrony between brain networks, is essential to understand the functional organization of the brain and to identify changes due t…
Statistical Inference on Grayscale Images via the Euler-Radon Transform
Kun Meng, Mattie Ji, Jinyu Wang +4
Tools from topological data analysis have been widely used to represent binary images in many scientific applications. Methods that aim to represent grayscale images (i.e., where p…
Distributed model building and recursive integration for big spatial data modeling
Emily C. Hector, Brian J. Reich, Ani Eloyan
Motivated by the need for computationally tractable spatial methods in neuroimaging studies, we develop a distributed and integrated framework for estimation and inference of Gauss…
Randomness of Shapes and Statistical Inference on Shapes via the Smooth Euler Characteristic Transform
Kun Meng, Jinyu Wang, Lorin Crawford +1
In this article, we establish the mathematical foundations for modeling the randomness of shapes and conducting statistical inference on shapes using the smooth Euler characteristi…
Brain Functional Connectivity Estimation Utilizing Diffusion Kernels on a Structural Connectivity Graph
Nathan Tung, Jerome Sanes, Eli Upfal +1
Functional connectivity (FC) refers to the investigation of interactions between brain regions to understand integration of neural activity in several regions. FC is often estimate…
Considerations Across Three Cultures: Parametric Regressions, Interpretable Algorithms, and Complex Algorithms
Ani Eloyan, Sherri Rose
We consider an extension of Leo Breiman's thesis from "Statistical Modeling: The Two Cultures" to include a bifurcation of algorithmic modeling, focusing on parametric regressions,…
Population-level Task-evoked Functional Connectivity via Fourier Analysis
Kun Meng, Ani Eloyan
Functional magnetic resonance imaging (fMRI) is a non-invasive and in-vivo imaging technique essential for measuring brain activity. Functional connectivity is used to study associ…
Principal manifold estimation via model complexity selection
Kun Meng, Ani Eloyan
We propose a framework of principal manifolds to model high-dimensional data. This framework is based on Sobolev spaces and designed to model data of any intrinsic dimension. It in…
Relating multi-sequence longitudinal intensity profiles and clinical covariates in new multiple sclerosis lesions
Elizabeth M. Sweeney, Russell T. Shinohara, Blake E. Dewey +5
Structural magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple sclerosis (MS) patients. The formation of these lesions is a complex process inv…
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…
Homotopic Group ICA for Multi-Subject Brain Imaging Data
Juemin Yang, Ani Eloyan, Anita Barber +5
Independent Component Analysis (ICA) is a computational technique for revealing latent factors that underlie sets of measurements or signals. It has become a standard technique in…