60 citations · 78 across the 13 of their papers we have counts for
13 papers
Biophysics-informed deep operator learning for inverse problems with application to electrophysiological source reconstruction
Eardi Lila, Erica R. Peterson, Alexis N. Bosseler +2
Electrophysiological brain signals are typically acquired through indirect and noisy measurements, providing transformed representations of the underlying neural activity. Source r…
Learned Hemodynamic Coupling Inference in Resting-State Functional MRI
William Consagra, Eardi Lila
Functional magnetic resonance imaging (fMRI) provides an indirect measurement of neuronal activity via hemodynamic responses that vary across brain regions and individuals. Ignorin…
Dimension-reduced outcome-weighted learning for estimating individualized treatment regimes in observational studies
Sungtaek Son, Eardi Lila, Kwun Chuen Gary Chan
Individualized treatment regimes (ITRs) aim to improve clinical outcomes by assigning treatment based on patient-specific characteristics. However, existing methods often struggle…
Why Text Prevails: Vision May Undermine Multimodal Medical Decision Making
Siyuan Dai, Lunxiao Li, Kun Zhao +6
With the rapid progress of large language models (LLMs), advanced multimodal large language models (MLLMs) have demonstrated impressive zero-shot capabilities on vision-language ta…
Genetic Regression Analysis of Human Brain Connectivity Using an Efficient Estimator of Genetic Covariance
Keshav Motwani, Ali Shojaie, Ariel Rokem +1
Non-invasive measurements of the human brain using magnetic resonance imaging (MRI) have significantly improved our understanding the brain's network organization by enabling measu…
Asymmetric canonical correlation analysis of Riemannian and high-dimensional data
James Buenfil, Eardi Lila
In this paper, we introduce a novel statistical model for the integrative analysis of Riemannian-valued functional data and high-dimensional data. We apply this model to explore th…
Inference for Heterogeneous Graphical Models using Doubly High-Dimensional Linear-Mixed Models
Kun Yue, Eardi Lila, Ali Shojaie
Inferring the structure of population-level functional connectivity networks from multi-subject functional magnetic resonance imaging data is essential for understanding the connec…
Eigen-Adjusted Functional Principal Component Analysis
Ci-Ren Jiang, Eardi Lila, John AD Aston +1
Functional Principal Component Analysis (FPCA) has become a widely-used dimension reduction tool for functional data analysis. When additional covariates are available, existing FP…
Interpretable discriminant analysis for functional data supported on random nonlinear domains with an application to Alzheimer's disease
Eardi Lila, Wenbo Zhang, Swati Rane Levendovszky
We introduce a novel framework for the classification of functional data supported on nonlinear, and possibly random, manifold domains. The motivating application is the identifica…
Functional random effects modeling of brain shape and connectivity
Eardi Lila, John A. D. Aston
We present a statistical framework that jointly models brain shape and functional connectivity, which are two complex aspects of the brain that have been classically studied indepe…
Representation and reconstruction of covariance operators in linear inverse problems
Eardi Lila, Simon Arridge, John A. D. Aston
We introduce a framework for the reconstruction and representation of functions in a setting where these objects cannot be directly observed, but only indirect and noisy measuremen…
Statistical Analysis of Functions on Surfaces, with an application to Medical Imaging
Eardi Lila, John A. D. Aston
In Functional Data Analysis, data are commonly assumed to be smooth functions on a fixed interval of the real line. In this work, we introduce a comprehensive framework for the ana…
Smooth Principal Component Analysis over two-dimensional manifolds with an application to Neuroimaging
Eardi Lila, John A. D. Aston, Laura M. Sangalli
Motivated by the analysis of high-dimensional neuroimaging signals located over the cortical surface, we introduce a novel Principal Component Analysis technique that can handle fu…