86 citations · 145 across the 14 of their papers we have counts for
14 papers
Learning Covariance-Based Multi-Scale Representation of Neuroimaging Measures for Alzheimer Classification
Seunghun Baek, Injun Choi, Mustafa Dere +3
Stacking excessive layers in DNN results in highly underdetermined system when training samples are limited, which is very common in medical applications. In this regard, we presen…
Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals
Tingting Dan, Jiaqi Ding, Ziquan Wei +4
Graph neural networks (GNNs) are widely used in domains like social networks and biological systems. However, the locality assumption of GNNs, which limits information exchange to…
DeepGraphDMD: Interpretable Spatio-Temporal Decomposition of Non-linear Functional Brain Network Dynamics
Md Asadullah Turja, Martin Styner, Guorong Wu
Functional brain dynamics is supported by parallel and overlapping functional network modes that are associated with specific neural circuits. Decomposing these network modes from…
Pathology Steered Stratification Network for Subtype Identification in Alzheimer's Disease
Enze Xu, Jingwen Zhang, Jiadi Li +4
Alzheimer's disease (AD) is a heterogeneous, multifactorial neurodegenerative disorder characterized by beta-amyloid, pathologic tau, and neurodegeneration. There are no effective…
Uncovering the System Vulnerability and Criticality of Human Brain under Dynamical Neuropathological Events in Alzheimer's Disease
Jingwen Zhang, Qing Liu, Haorui Zhang +5
Background: Despite the striking efforts in investigating neurobiological factors behind the acquisition of amyloid-\b{eta} (A), protein tau (T), and neurodegeneration ([N]) biomar…
Analyzing the Spatiotemporal Interaction and Propagation of ATN Biomarkers in Alzheimer's Disease using Longitudinal Neuroimaging Data
Qing Liu, Defu Yang, Jingwen Zhang +3
Three major biomarkers: beta-amyloid (A), pathologic tau (T), and neurodegeneration (N), are recognized as valid proxies for neuropathologic changes of Alzheimer's disease. While t…
A Network-Guided Reaction-Diffusion Model of AT[N] Biomarkers in Alzheimer's Disease
Jingwen Zhang, Defu Yang, Wei He +2
Currently, many studies of Alzheimer's disease (AD) are investigating the neurobiological factors behind the acquisition of beta-amyloid (A), pathologic tau (T), and neurodegenerat…
Learning Common Harmonic Waves on Stiefel Manifold -- A New Mathematical Approach for Brain Network Analyses
Jiazhou Chen, Guoqiang Han, Hongmin Cai +5
Converging evidence shows that disease-relevant brain alterations do not appear in random brain locations, instead, its spatial pattern follows large scale brain networks. In this…
Learning Multi-resolution Graph Edge Embedding for Discovering Brain Network Dysfunction in Neurological Disorders
Xin Ma, Guorong Wu, Seong Jae Hwang +1
Tremendous recent literature show that associations between different brain regions, i.e., brain connectivity, provide early symptoms of neurological disorders. Despite significant…
Synergetic and redundant information flow detected by unnormalized Granger causality: application to resting state fMRI
Sebastiano Stramaglia, Leonardo Angelini, Guorong Wu +3
Objectives: We develop a framework for the analysis of synergy and redundancy in the pattern of information flow between subsystems of a complex network. Methods: The presence of r…
Point-process deconvolution of fMRI reveals effective connectivity alterations in chronic pain patients
Guorong Wu, Enzo Tagliazucchi, Dante R. Chialvo +1
It is now recognized that important information can be extracted from the brain spontaneous activity, as exposed by recent analysis using a repertoire of computational methods. In…
A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data
G. Wu, W. Liao, S. Stramaglia +3
A great improvement to the insight on brain function that we can get from fMRI data can come from effective connectivity analysis, in which the flow of information between even rem…
Expanding the Transfer Entropy to Identify Information Subgraphs in Complex Systems
S. Stramaglia, Guo-Rong Wu, M. Pellicoro +1
We propose a formal expansion of the transfer entropy to put in evidence irreducible sets of variables which provide information for the future state of each assigned target. Multi…
Information flow in a network model and the law of diminishing marginal returns
Daniele Marinazzo, Mario Pellicoro, Guorong Wu +2
We analyze a simple dynamical network model which describes the limited capacity of nodes to process the input information. For a suitable choice of the parameters, the information…