Search Results for author: Guorong Wu

Found 7 papers, 0 papers with code

Uncovering the system vulnerability and criticality of human brain under evolving neuropathological events in Alzheimer's Disease

no code implementations22 Jan 2022 Jingwen Zhang, Qing Liu, Haorui Zhang, Michelle Dai, Qianqian Song, Defu Yang, Guorong Wu, Minghan Chen

Despite the striking efforts in investigating neurobiological factors behind the acquisition of beta-amyloid (A), protein tau (T), and neurodegeneration ([N]) biomarkers, the mechanistic pathways of how AT[N] biomarkers spread throughout the brain remain elusive.

Analyzing the Spatiotemporal Interaction and Propagation of ATN Biomarkers in Alzheimer's Disease using Longitudinal Neuroimaging Data

no code implementations7 Mar 2021 Qing Liu, Defu Yang, Jingwen Zhang, Ziming Wei, Guorong Wu, Minghan Chen

Three major biomarkers: beta-amyloid (A), pathologic tau (T), and neurodegeneration (N), are recognized as valid proxies for neuropathologic changes of Alzheimer's disease.

A Network-Guided Reaction-Diffusion Model of AT[N] Biomarkers in Alzheimer's Disease

no code implementations10 Sep 2020 Jingwen Zhang, Defu Yang, wei he, Guorong Wu, Minghan Chen

Currently, many studies of Alzheimer's disease (AD) are investigating the neurobiological factors behind the acquisition of beta-amyloid (A), pathologic tau (T), and neurodegeneration ([N]) biomarkers from neuroimages.

Learning Common Harmonic Waves on Stiefel Manifold -- A New Mathematical Approach for Brain Network Analyses

no code implementations1 Jul 2020 Jiazhou Chen, Guoqiang Han, Hongmin Cai, Defu Yang, Paul J. Laurienti, Martin Styner, Guorong Wu, Alzheimer's Disease Neuroimaging Initiative ADNI

To that end, we propose a novel connectome harmonic analysis framework to provide enhanced mathematical insights by detecting frequency-based alterations relevant to brain disorders.

Multi-resolution Graph Neural Network for Identifying Disease-specific Variations in Brain Connectivity

no code implementations3 Dec 2019 Xin Ma, Guorong Wu, Won Hwa Kim

As there is significant interest in understanding the altered interactions between different brain regions that lead to neuro-disorders, it is important to develop data-driven methods that work with a population of graph data for traditional prediction tasks.

Graph Learning

Groupwise Registration via Graph Shrinkage on the Image Manifold

no code implementations CVPR 2013 Shihui Ying, Guorong Wu, Qian Wang, Dinggang Shen

Specifically, we first use a graph to model the distribution of all image data sitting on the image manifold, with each node representing an image and each edge representing the geodesic pathway between two nodes (or images).

Image Registration

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