260 citations · 617 across the 58 of their papers we have counts for
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Supervised Deep Multimodal Matrix Factorization for Interpretable Brain Network Analysis
Amjad Seyedi, Lifang He, Songlin Zhao +2
We present Supervised Deep Multimodal Matrix Factorization (SD3MF), an interpretable framework for integrative brain network analysis that generalizes Symmetric Nonnegative Matrix…
Diffusion-Guided Pretraining for Brain Graph Foundation Models
Xinxu Wei, Rong Zhou, Lifang He +1
With the growing interest in foundation models for brain signals, graph-based pretraining has emerged as a promising paradigm for learning transferable representations from connect…
SDE-Driven Spatio-Temporal Hypergraph Neural Networks for Irregular Longitudinal fMRI Connectome Modeling in Alzheimer's Disease
Ruiying Chen, Yutong Wang, Houliang Zhou +3
Longitudinal neuroimaging is essential for modeling disease progression in Alzheimer's disease (AD), yet irregular sampling and missing visits pose substantial challenges for learn…
R-GenIMA: Integrating Neuroimaging and Genetics with Interpretable Multimodal AI for Alzheimer's Disease Progression
Kun Zhao, Siyuan Dai, Yingying Zhang +9
Early detection of Alzheimer's disease (AD) requires models capable of integrating macro-scale neuroanatomical alterations with micro-scale genetic susceptibility, yet existing mul…
ManifoldFormer: Geometric Deep Learning for Neural Dynamics on Riemannian Manifolds
Yihang Fu, Lifang He, Qingyu Chen
Existing EEG foundation models mainly treat neural signals as generic time series in Euclidean space, ignoring the intrinsic geometric structure of neural dynamics that constrains…
Conditional Neural ODE for Longitudinal Parkinson's Disease Progression Forecasting
Xiaoda Wang, Yuji Zhao, Kaiqiao Han +8
Parkinson's disease (PD) shows heterogeneous, evolving brain-morphometry patterns. Modeling these longitudinal trajectories enables mechanistic insight, treatment development, and…