10 citations · 14 across the 12 of their papers we have counts for
13 papers
Robust Community Detection for Noisy Networks with Covariates: Application to Functional Brain Networks
Zeyu Hu, Frederick H. Xu, Suprateek Kundu +4
Community detection is fundamental to understanding the modular organization in functional brain networks, yet noise in neuroimaging-derived networks and auxiliary node-level covar…
iLENS: Interpretable LLM-Guided Mixture-of-Experts for Neuroimaging Survival Analysis
Farica Zhuang, Seong Woo Han, Zixuan Wen +3
Alzheimer's Disease (AD) is a complex neurodegenerative disorder that continues to impact millions of people worldwide. Predicting AD conversion during the prodromal stage remains…
Disentangling Latent Risk Pathways via Bayesian Hypergraph Inference
Shengxian Ding, Haonan Gao, Pangpang Liu +2
Electronic health records (EHR) pose large-scale multi-disease modeling problems in which many outcomes are rare and strongly influenced by shared risk factors. While modern approa…
Volume-optimal persistence homological scaffolds of hemodynamic networks covary with MEG theta-alpha aperiodic dynamics
Nghi Nguyen, Tao Hou, Enrico Amico +9
Higher-order properties of functional magnetic resonance imaging (fMRI) induced connectivity have been shown to unravel many exclusive topological and dynamical insights beyond pai…
A principled framework to assess the information-theoretic fitness of brain functional sub-circuits
Duy Duong-Tran, Nghi Nguyen, Shizhuo Mu +10
In systems and network neuroscience, many common practices in brain connectomic analysis are often not properly scrutinized. One such practice is mapping a predetermined set of sub…
Learnable Community-Aware Transformer for Brain Connectome Analysis with Token Clustering
Yanting Yang, Beidi Zhao, Zhuohao Ni +2
Neuroscientific research has revealed that the complex brain network can be organized into distinct functional communities, each characterized by a cohesive group of regions of int…
Learning High-Order Relationships of Brain Regions
Weikang Qiu, Huangrui Chu, Selena Wang +4
Discovering reliable and informative relationships among brain regions from functional magnetic resonance imaging (fMRI) signals is essential in phenotypic predictions. Most of the…
Bayesian mixed model inference for genetic association under related samples with brain network phenotype
Xinyuan Tian, Yiting Wang, Selena Wang +2
Genetic association studies for brain connectivity phenotypes have gained prominence due to advances in non-invasive imaging techniques and quantitative genetics. Brain connectivit…
Establishing group-level brain structural connectivity incorporating anatomical knowledge under latent space modeling
Selena Wang, Yiting Wang, Frederick H. Xu +2
Brain structural connectivity, capturing the white matter fiber tracts among brain regions inferred by diffusion MRI (dMRI), provides a unique characterization of brain anatomical…
Bayesian subtyping for multi-state brain functional connectome with application on adolescent brain cognition
Tianqi Chen, Chichun Tan, Hongyu Zhao +3
Converging evidence indicates that the heterogeneity of cognitive profiles may arise through detectable alternations in brain functions. Particularly, brain functional connectivity…
Covariance-on-Covariance Regression
Yi Zhao, Yize Zhao
A Covariance-on-Covariance regression model is introduced in this manuscript. It is assumed that there exists (at least) a pair of linear projections on outcome covariance matrices…
Bayesian semi-parametric inference for clustered recurrent events with zero-inflation and a terminal event/4163305
Xinyuan Tian, Maria Ciarleglio, Jiachen Cai +4
Recurrent event data are common in clinical studies when participants are followed longitudinally, and are often subject to a terminal event. With the increasing popularity of larg…
A Bayesian nonparametric mixture model for selecting genes and gene subnetworks
Yize Zhao, Jian Kang, Tianwei Yu
It is very challenging to select informative features from tens of thousands of measured features in high-throughput data analysis. Recently, several parametric/regression models h…