activity
20172021
most citedQuantifying the strength of structural connectivity underlying functional brain networks

4 citations · 6 across the 5 of their papers we have counts for

collaborators

9 papers

stat.ME2021

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…

stat.ME2021

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.…

stat.ME2020

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…

stat.AP20192 cited

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…

stat.AP2018

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…

stat.ME2018

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…