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Author

Qingchuan Zhang

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Oct 2026

Multi-Dimensional Feature Adaptive Gating Network for Multi-View and Whole-Brain Magnetic Resonance Imaging-Based Alzheimer's Disease Classification.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder associated with structural brain alterations detectable by structural magnetic resonance imaging (sMRI). Accurate classification of normal control (NC), mild cognitive impairment (MCI), and AD remains challenging because disease-related changes are su...

Yi-Peng Zhou, Lin Zhu, Zhen-Qiao Liu et al. · 0 citations
Oct 2026

Tri-Stream Disentangled Network with Adaptive Dual-Space Fusion for Electroencephalography-Based Image Reconstruction on THINGS-EEG2.

Reconstructing visual stimuli from non-invasive electroencephalography (EEG) requires mapping short, noisy neural responses to meaningful visual representations despite limited spatial sampling. Existing EEG-to-image approaches employ diverse neural representations and generative strategies, necessitating reproducible...

Yi-Peng Zhou, Jing-Yuan Li, Kai-Zhong Zhao et al. · 0 citations
Sep 2026

Mixture-of-Experts Electroencephalography-Functional Magnetic Resonance Imaging Fusion for Interpretable Computer-Assisted Brain Disorder Diagnosis.

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide complementary information about brain function, and have shown significant promise in detecting functional abnormalities in various brain disorders. However, the distinct signal characteristics and disparate representational spaces of...

Rui-Zhe Zhong, Hai-Sheng Li, Qing-Chuan Zhang · 0 citations
Jul 2026

Multi-Scale Structural MRI Features Reveal Task-Based Functional Connectivity and Its Alterations in Psychiatric Disorders: A Collaborative Graph Attention Network Approach

The Collaborative Graph Attention Multi-Task Network (CoGA-MTN), a deep learning framework that integrates multi-scale structural features to jointly predict task-based functional connectivity (FC) and identify potential disease-related network alterations, is introduced.

Qingchuan Zhang, Zhen-Qiao Liu, Wen-Jie Dou et al. · 0 citations
Open access Jul 2026

TF-JointMAE: a self-supervised multi-representation EEG learning framework for Alzheimer’s disease spectrum classification

A self-supervised masked autoencoder pre-training on multiple public EEG datasets demonstrates that TF-JointMAE effectively improves the robustness of EEG representations, providing potential auxiliary support for AD-spectrum classification and clinical decision-making.

Min Zuo, Hong Zhu, Zhen-Qiao Liu et al. · 0 citations

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