Area of research
Cognitive Neuroscience · Radiology, Nuclear Medicine and Imaging
Research interest
Research interests include Functional Brain Connectivity Studies, Neural dynamics and brain function, Advanced Neuroimaging Techniques and Applications, and EEG and Brain-Computer Interfaces.
Charting brain morphology in international healthy and neurological populations.
Group Information Guided Smooth Independent Component Analysis Method for Multi-Subject fMRI Data Analysis.
A graph transformer-based foundation model for brain functional connectivity network.
Investigating biotypes and neural characteristics among anxiety, depression, and comorbidity via a novel noisy label learning method
Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade.
A Novel Spatial Fractional-Domain Approach for Short Emotion-Evoked EEG Identity Recognition
Joint consensus kernel learning and adaptive hypergraph regularization for graph-based clustering
Joint Aging Patterns in Brain Function and Structure Revealed Using 27,793 Samples.
Mutualistic Multi-Network Noisy Label Learning (MMNNLL) Method and Its Application to Transdiagnostic Classification of Bipolar Disorder and Schizophrenia.
Searching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities
Searching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities.
A survey of brain functional network extraction methods using fMRI data
A survey of brain functional network extraction methods using fMRI data.
More reliable biomarkers and more accurate prediction for mental disorders using a label-noise filtering-based dimensional prediction method.
SMART (Splitting-Merging Assisted Reliable) Independent Component Analysis for Extracting Accurate Brain Functional Networks.
Common and unique brain aging patterns between females and males quantified by large-scale deep learning.
Local-structure-preservation and redundancy-removal-based feature selection method and its application to the identification of biomarkers for schizophrenia.
Identifying canonical and replicable multi-scale intrinsic connectivity networks in 100k+ resting-state fMRI datasets.
Identifying canonical and replicable multi‐scale intrinsic connectivity networks in 100k+ <scp>resting‐state fMRI</scp> datasets
A Novel Neighborhood Rough Set-Based Feature Selection Method and Its Application to Biomarker Identification of Schizophrenia.
An attention-based hybrid deep learning framework integrating brain connectivity and activity of resting-state functional MRI data
An attention-based hybrid deep learning framework integrating brain connectivity and activity of resting-state functional MRI data
Derivation and utility of schizophrenia polygenic risk associated multimodal MRI frontotemporal network
A new multimodality fusion classification approach to explore the uniqueness of schizophrenia and autism spectrum disorder
Derivation and utility of schizophrenia polygenic risk associated multimodal MRI frontotemporal network.
A new multimodality fusion classification approach to explore the uniqueness of schizophrenia and autism spectrum disorder.
Canonical and Replicable Multi-Scale Intrinsic Connectivity Networks in 100k+ Resting-State fMRI Datasets
SMART (splitting-merging assisted reliable) Independent Component Analysis for Extracting Accurate Brain Functional Networks
Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning
Alzheimer’s Disease Projection From Normal to Mild Dementia Reflected in Functional Network Connectivity: A Longitudinal Study