7 citations · 8 across the 8 of their papers we have counts for
9 papers
Isolating Nonlinear Independent Sources in fMRI with -TCVAE Models
Qiang Li, Shujian Yu, Jesus Malo +3
Learning meaningful latent representations from nonlinear fMRI data remains a fundamental challenge in neuroimaging analysis. Traditional independent component analysis, widely use…
Modeling Higher-Order Brain Interactions via a Multi-View Information Bottleneck Framework for fMRI-based Psychiatric Diagnosis
Kunyu Zhang, Qiang Li, Vince D. Calhoun +1
Resting-state functional magnetic resonance imaging (fMRI) has emerged as a cornerstone for psychiatric diagnosis, yet most approaches rely on pairwise brain cortical or sub-cortic…
PIME: Prototype-based Interpretable MCTS-Enhanced Brain Network Analysis for Disorder Diagnosis
Kunyu Zhang, Yanwu Yang, Jing Zhang +2
Recent deep learning methods for fMRI-based diagnosis have achieved promising accuracy by modeling functional connectivity networks. However, standard approaches often struggle wit…
Deep Deterministic Nonlinear ICA via Total Correlation Minimization with Matrix-Based Entropy Functional
Qiang Li, Shujian Yu, Liang Ma +4
Blind source separation, particularly through independent component analysis (ICA), is widely utilized across various signal processing domains for disentangling underlying compone…
Adapting HFMCA to Graph Data: Self-Supervised Learning for Generalizable fMRI Representations
Jakub Frac, Alexander Schmatz, Qiang Li +2
Functional magnetic resonance imaging (fMRI) analysis faces significant challenges due to limited dataset sizes and domain variability between studies. Traditional self-supervised…
BrainIB++: Leveraging Graph Neural Networks and Information Bottleneck for Functional Brain Biomarkers in Schizophrenia
Tianzheng Hu, Qiang Li, Shu Liu +3
The development of diagnostic models is gaining traction in the field of psychiatric disorders. Recently, machine learning classifiers based on resting-state functional magnetic re…
Efficient Brain Network Estimation with Sparse ICA in Non-Human Primate Neuroimaging
Qiang Li, Liang Ma, Masoud Seraji +4
Independent component analysis (ICA) is widely used to separate mixed signals and recover statistically independent components. However, in non-human primate neuroimaging studies,…
MvHo-IB: Multi-View Higher-Order Information Bottleneck for Brain Disorder Diagnosis
Kunyu Zhang, Qiang Li, Shujian Yu
Recent evidence suggests that modeling higher-order interactions (HOIs) in functional magnetic resonance imaging (fMRI) data can enhance the diagnostic accuracy of machine learning…
Aberrant High-Order Dependencies in Schizophrenia Resting-State Functional MRI Networks
Qiang Li, Vince D. Calhoun, Adithya Ram Ballem +3
The human brain has a complex, intricate functional architecture. While many studies primarily emphasize pairwise interactions, delving into high-order associations is crucial for…