12 citations · 33 across the 13 of their papers we have counts for
13 papers
Simultaneous Cluster Structure Learning and Estimation of Heterogeneous Graphs for Matrix-variate fMRI Data
Dong Liu, Changwei Zhao, Yong He +3
Graphical models play an important role in neuroscience studies, particularly in brain connectivity analysis. Typically, observations/samples are from several heterogenous groups a…
Joint Learning of Multiple Differential Networks with fMRI data for Brain Connectivity Alteration Detection
Hao Chen, Ying Guo, Yong He +3
In this study we focus on the problem of joint learning of multiple differential networks with function Magnetic Resonance Imaging (fMRI) data sets from multiple research centers.…
LOCUS: A Novel Decomposition Method for Brain Network Connectivity Matrices using Low-rank Structure with Uniform Sparsity
Yikai Wang, Ying Guo
Network-oriented research has been increasingly popular in many scientific areas. In neuroscience research, imaging-based network connectivity measures have become the key for unde…
Simultaneous Differential Network Analysis and Classification for High-dimensional Matrix-variate Data, with application to Brain Connectivity Alteration Detection and fMRI-guided Medical Diagnoses of Alzheimer's Disease
Chen Hao, Guo Ying, He Yong +6
Alzheimer's disease (AD) is the most common form of dementia, which causes problems with memory, thinking and behavior. Growing evidence has shown that the brain connectivity netwo…
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 Differential Degree Test for Comparing Brain Networks
Ixavier A Higgins, Ying Guo, Suprateek Kundu +2
Recently, graph theory has become a popular method for characterizing brain functional organization. One important goal in graph theoretical analysis of brain networks is to identi…
A hierarchical independent component analysis model for longitudinal Neuroimaging studies
Yikai Wang, Ying Guo
In recent years, longitudinal neuroimaging study has become increasingly popular in neuroscience research to investigate disease-related changes in brain functions. In current neur…
HINT: A Hierarchical Independent Component Analysis Toolbox for Investigating Brain Functional Networks using Neuroimaging Data
Joshua Lukemire, Yikai Wang, Amit Verma +1
Independent component analysis (ICA) is a popular tool for investigating brain organization in neuroscience research. In fMRI studies, an important goal is to study how brain netwo…
Integrative Bayesian Analysis of Brain Functional Networks Incorporating Anatomical Knowledge
Ixavier A. Higgins, Suprateek Kundu, Ying Guo
Recently, there has been increased interest in fusing multimodal imaging to better understand brain organization. Specifically, accounting for knowledge of anatomical pathways conn…
Statistical methods for characterizing transfusion-related changes in regional oxygenation using Near-infrared spectroscopy (NIRS) in preterm infants
Ying Guo, Yikai Wang, Terri Marin +3
Near infrared spectroscopy (NIRS) is an imaging-based diagnostic tool that provides non-invasive and continuous evaluation of regional tissue oxygenation in real-time. In recent ye…
Bayesian Joint Modeling of Multiple Brain Functional Networks
Joshua Lukemire, Suprateek Kundu, Giuseppe Pagnoni +1
Brain function is organized in coordinated modes of spatio-temporal activity (functional networks) exhibiting an intrinsic baseline structure with variations under different experi…
Quantifying the strength of structural connectivity underlying functional brain networks
Phebe Brenne Kemmer, F. DuBois Bowman, Helen Mayberg +1
In recent years, there has been strong interest in neuroscience studies to investigate brain organization through networks of brain regions that demonstrate strong functional conne…
Modeling Covariate Effects in Group Independent Component Analysis with Applications to Functional Magnetic Resonance Imaging
Ran Shi, Ying Guo
Independent component analysis (ICA) is a powerful computational tool for separating independent source signals from their linear mixtures. ICA has been widely applied in neuroimag…