Yuhui Du

The paper presents a WM intrinsic connectivity network template derived from 100,000 fMRI scans, showing distinct WM and GM networks with task-related activity and differences in schizophrenia.

The paper develops a reliable network template and methods to estimate individualized functional networks from resting-state fMRI data across 100k+ individuals, showing how data length and spatial resolution affect network estimation.

This study uses a Siamese CNN framework to predict individual identity from resting-state fMRI data, achieving 88% accuracy in subject discrimination using spatial network pairs.

This study examines dynamic functional connectivity in the cognitive control network across Alzheimer's disease stages, finding altered temporal connectivity patterns associated with symptom progression.

The paper introduces decentralized quality control tools for neuroimaging data in federated environments, showing promising results in visualization and outlier detection while preserving privacy.

This study compares deep learning and standard machine learning on brain imaging data, showing deep learning's superior predictive power and robustness in age and gender classification.

This paper uses ultra-high-order ICA to identify fine overlapping functional dynamic parcellations of the brain, highlighting their evolution over time.

This study analyzes age-related changes in brain structure and function across 5967 individuals from 13 to 72 years old, revealing nonlinear patterns in gray matter volume, structural network correlation, and functional network connectivity.

This paper applies deep residual networks to predict Alzheimer’s disease progression from mild cognitive impairment using neuroimaging data, achieving better performance than existing methods.

This study compares GIG-ICA and STR for identifying brain functional networks in schizophrenia, finding GIG-ICA more sensitive to group differences and better at classification.