39 citations · 141 across the 34 of their papers we have counts for
36 papers
Bayesian Estimation of Continuous Brain Connectivity with Log-Gaussian Cox Processes
Jaehoan Kim, William Consagra, Debdeep Pati +1
Continuous structural connectivity describes the connection between any two points of the cortical surface by an intensity function. Based on the endpoints of tractography streamli…
Verifier-Guided Synthetic Augmentation for 3D Human Shape Generation
Yuexuan Wu, Yang Xiang, Hamid Laga +3
Limited training data diversity constrains generative modeling of 3D human bodies: conservative models remain close to observed examples, whereas exploratory models often violate b…
ConSEAL: Connectivity-Informed Streamline Endpoint Alignment for Diffeomorphic Cortical Registration
Yang Xiang, Martin Cole, Zhengwu Zhang
Cortical surface registration is often driven by local geometric descriptors (e.g., sulcal depth and curvature). While this approach achieves geometric correspondence, it neglects…
Attention-Based Variational Framework for Joint and Individual Components Learning with Applications in Brain Network Analysis
Yifei Zhang, Meimei Liu, Zhengwu Zhang
Brain organization is increasingly characterized through multiple imaging modalities, most notably structural connectivity (SC) and functional connectivity (FC). Integrating these…
Joint Semi-Symmetric Tensor PCA for Integrating Multi-modal Populations of Networks
Jiaming Liu, Lili Zheng, Zhengwu Zhang +1
Multi-modal populations of networks arise in many scenarios including in large-scale multi-modal neuroimaging studies that capture both functional and structural neuroimaging data…
Continuous and Atlas-free Analysis of Brain Structural Connectivity
William Consagra, Martin Cole, Xing Qiu +1
Brain structural networks are often represented as discrete adjacency matrices with elements summarizing the connectivity between pairs of regions of interest (ROIs). These ROIs ar…
High-dimensional Response Growth Curve Modeling for Longitudinal Neuroimaging Analysis
Lu Wang, Xiang Lyu, Zhengwu Zhang +1
There is increasing interest in modeling high-dimensional longitudinal outcomes in applications such as developmental neuroimaging research. Growth curve model offers a useful tool…
Nonparametric Modeling of Diffusion MRI Signal in Q-space
Arkaprava Roy, Zhou Lan, Zhengwu Zhang
This paper describes a novel nonparametric model for modeling diffusion MRI signals in q-space. In q-space, diffusion MRI signal is measured for a sequence of magnetic strengths (b…
Genetic underpinnings of brain structural connectome for young adults
Yize Zhao, Changgee Chang, Jingwen Zhang +1
With distinct advantages in power over behavioral phenotypes, brain imaging traits have become emerging endophenotypes to dissect molecular contributions to behaviors and neuropsyc…
Motion-Invariant Variational Auto-Encoding of Brain Structural Connectomes
Yizi Zhang, Meimei Liu, Zhengwu Zhang +1
Mapping of human brain structural connectomes via diffusion MRI offers a unique opportunity to understand brain structural connectivity and relate it to various human traits, such…
Tree Representations of Brain Structural Connectivity via Persistent Homology
Didong Li, Phuc Nguyen, Zhengwu Zhang +1
The brain structural connectome is generated by a collection of white matter fiber bundles constructed from diffusion weighted MRI (dMRI), acting as highways for neural activity. T…
Interpretable AI for relating brain structural and functional connectomes
Haoming Yang, Steven Winter, Zhengwu Zhang +1
One of the central problems in neuroscience is understanding how brain structure relates to function. Naively one can relate the direct connections of white matter fiber tracts bet…
Analyzing Brain Structural Connectivity as Continuous Random Functions
William Consagra, Martin Cole, Zhengwu Zhang
This work considers a continuous framework to characterize the population-level variability of structural connectivity. Our framework assumes the observed white matter fiber tract…
Outlier Detection for Multi-Network Data
Pritam Dey, Zhengwu Zhang, David B. Dunson
It has become routine in neuroscience studies to measure brain networks for different individuals using neuroimaging. These networks are typically expressed as adjacency matrices,…
Low Tree-Rank Bayesian Vector Autoregression Model
Leo L. Duan, Zeyu Yuwen, George Michailidis +1
Vector autoregression has been widely used for modeling and analysis of multivariate time series data. In high-dimensional settings, model parameter regularization schemes inducing…
Learning to Model the Relationship Between Brain Structural and Functional Connectomes
Yang Li, Gonzalo Mateos, Zhengwu Zhang
Recent advances in neuroimaging along with algorithmic innovations in statistical learning from network data offer a unique pathway to integrate brain structure and function, and t…
Amplitude Mean of Functional Data on
Zhengwu Zhang, Bayan Saparbayeva
Manifold-valued functional data analysis (FDA) recently becomes an active area of research motivated by the raising availability of trajectories or longitudinal data observed on no…
Optimized Diffusion Imaging for Brain Structural Connectome Analysis
William Consagra, Arun Venkataraman, Zhengwu Zhang
High angular resolution diffusion imaging (HARDI) is a type of diffusion magnetic resonance imaging (dMRI) that measures diffusion signals on a sphere in q-space. It has been widel…
High-Dimensional Spatial Quantile Function-on-Scalar Regression
Zhengwu Zhang, Xiao Wang, Linglong Kong +1
This paper develops a novel spatial quantile function-on-scalar regression model, which studies the conditional spatial distribution of a high-dimensional functional response given…
Multivariate functional responses low rank regression with an application to brain imaging data
Xiucai Ding, Dengdeng Yu, Zhengwu Zhang +1
We propose a multivariate functional responses low rank regression model with possible high dimensional functional responses and scalar covariates. By expanding the slope functions…
Multi-scale graph principal component analysis for connectomics
Steven Winter, Zhengwu Zhang, David Dunson
In brain connectomics, the cortical surface is parcellated into different regions of interest (ROIs) prior to statistical analysis. The brain connectome for each individual can the…
Auto-encoding brain networks with applications to analyzing large-scale brain imaging datasets
Meimei Liu, Zhengwu Zhang, David B. Dunson
There has been huge interest in studying human brain connectomes inferred from different imaging modalities and exploring their relationship with human traits, such as cognition. B…
Soft Tensor Regression
Georgia Papadogeorgou, Zhengwu Zhang, David B. Dunson
Statistical methods relating tensor predictors to scalar outcomes in a regression model generally vectorize the tensor predictor and estimate the coefficients of its entries employ…
Learning Signal Subgraphs from Longitudinal Brain Networks with Symmetric Bilinear Logistic Regression
Lu Wang, Zhengwu Zhang
Modern neuroimaging technologies, combined with state-of-the-art data processing pipelines, have made it possible to collect longitudinal observations of an individual's brain conn…