5 citations · 11 across the 14 of their papers we have counts for
16 papers
MnemoDyn: Learning Resting State Dynamics from 40K FMRI sequences
Sourav Pal, Viet Luong, Hoseok Lee +5
We present a dynamical-systems based model for resting-state functional magnetic resonance imaging (rs-fMRI), trained on a dataset of roughly 40K rs-fMRI sequences covering a wide…
ENC-ODE: Event-level Neurodegenerative Modeling in Continuous Time with Neural ODEs
Yujee Song, Seunghun Baek, Guorong Wu +1
Accurately predicting the temporal evolution of clinical biomarkers is crucial for the early diagnosis and management of neurodegenerative diseases such as Alzheimer's disease. How…
Multi-Modal Graph Neural Network with Transformer-Guided Adaptive Diffusion for Preclinical Alzheimer Classification
Jaeyoon Sim, Minjae Lee, Guorong Wu +1
The graphical representation of the brain offers critical insights into diagnosing and prognosing neurodegenerative disease via relationships between regions of interest (ROIs). De…
GeoDynamics: A Geometric State-Space Neural Network for Understanding Brain Dynamics on Riemannian Manifolds
Tingting Dan, Jiaqi Ding, Guorong Wu
State-space models (SSMs) have become a cornerstone for unraveling brain dynamics, revealing how latent neural states evolve over time and give rise to observed signals. By combini…
Explore Brain-Inspired Machine Intelligence for Connecting Dots on Graphs Through Holographic Blueprint of Oscillatory Synchronization
Tingting Dan, Jiaqi Ding, Guorong Wu
Neural coupling in both neuroscience and artificial intelligence emerges as dynamic oscillatory patterns that encode abstract concepts. To this end, we hypothesize that a deeper un…
De-Individualizing fMRI Signals via Mahalanobis Whitening and Bures Geometry
Aaron Jacobson, Tingting Dan, Martin Styner +3
Functional connectivity has been widely investigated to understand brain disease in clinical studies and imaging-based neuroscience, and analyzing changes in functional connectivit…
Understanding Mechanistic Role of Structural and Functional Connectivity in Tau Propagation Through Multi-Layer Modeling
Tingting Dan, Xinwei Huang, Jiaqi Ding +2
Emerging neuroimaging evidence shows that pathological tau proteins build up along specific brain networks, suggesting that large-scale network architecture plays a key role in the…
Large Connectome Model: An fMRI Foundation Model of Brain Connectomes Empowered by Brain-Environment Interaction in Multitask Learning Landscape
Ziquan Wei, Tingting Dan, Guorong Wu
A reliable foundation model of functional neuroimages is critical to promote clinical applications where the performance of current AI models is significantly impeded by a limited…
Optimal Control For Anti-Abeta Treatment in Alzheimer's Disease using a Reaction-Diffusion Model
Sun Lee, Chiu-Yen Kao, Zhiyuan Li +3
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that severely impairs survival and quality of life. While anti-amyloid beta (Abeta) therapies can slow disease…
OCL: Ordinal Contrastive Learning for Imputating Features with Progressive Labels
Seunghun Baek, Jaeyoon Sim, Guorong Wu +1
Accurately discriminating progressive stages of Alzheimer's Disease (AD) is crucial for early diagnosis and prevention. It often involves multiple imaging modalities to understand…
Modality-Agnostic Style Transfer for Holistic Feature Imputation
Seunghun Baek, Jaeyoon Sim, Mustafa Dere +3
Characterizing a preclinical stage of Alzheimer's Disease (AD) via single imaging is difficult as its early symptoms are quite subtle. Therefore, many neuroimaging studies are cura…
NeuroPath: A Neural Pathway Transformer for Joining the Dots of Human Connectomes
Ziquan Wei, Tingting Dan, Jiaqi Ding +1
Although modern imaging technologies allow us to study connectivity between two distinct brain regions in-vivo, an in-depth understanding of how anatomical structure supports brain…
Machine Learning on Dynamic Functional Connectivity: Promise, Pitfalls, and Interpretations
Jiaqi Ding, Tingting Dan, Ziquan Wei +4
An unprecedented amount of existing functional Magnetic Resonance Imaging (fMRI) data provides a new opportunity to understand the relationship between functional fluctuation and h…
Exploring the Enigma of Neural Dynamics Through A Scattering-Transform Mixer Landscape for Riemannian Manifold
Tingting Dan, Ziquan Wei, Won Hwa Kim +1
The human brain is a complex inter-wired system that emerges spontaneous functional fluctuations. In spite of tremendous success in the experimental neuroscience field, a system-le…
Message Detouring: A Simple Yet Effective Cycle Representation for Expressive Graph Learning
Ziquan Wei, Tingting Dan, Guorong Wu
Graph learning is crucial in the fields of bioinformatics, social networks, and chemicals. Although high-order graphlets, such as cycles, are critical to achieving an informative g…
Learning to Approximate Adaptive Kernel Convolution on Graphs
Jaeyoon Sim, Sooyeon Jeon, InJun Choi +2
Various Graph Neural Networks (GNNs) have been successful in analyzing data in non-Euclidean spaces, however, they have limitations such as oversmoothing, i.e., information becomes…