Chao‐Gan Yan

The paper identifies sgACC functional connectivity abnormalities in depression using large-scale brain imaging data and develops an individualized TMS targeting algorithm to improve treatment outcomes.

The paper evaluates the impact of different preprocessing pipelines on functional MRI reproducibility and shows moderate inter-pipeline agreement, affecting brainwide association study consistency.

This study examines intracranial EEG during rumination, finding increased slow frequency power in the precuneus and decreased high gamma power in the hippocampus, linked to reflection tendency.

This study evaluates harmonization strategies for multisite resting-state fMRI data, finding that SMA outperforms other methods in reducing site effects and improving identifiability and replicability.

This study evaluates the test-retest reliability and sex/age-related effects on the temporal clustering coefficient of dynamic brain networks using resting-state fMRI data from the Human Connectome Project.

This study investigates brain activation during think-aloud fMRI, finding that self-generated thoughts engage widespread brain regions and are represented across multiple neural scales.

This paper develops a deep learning classifier for Alzheimer's disease using brain MRI data from 85,721 scans, achieving high accuracy across multiple independent datasets.

This study compares the predictive power of brain structural and functional metrics for age, sex, and schizophrenia treatment outcomes using variance partitioning and permutation tests.

This paper evaluates test-retest reliability of multilayer network measures in neuroimaging, identifying methodological factors affecting reliability and task-specific requirements.

This study investigates the subsystem mechanism of the default mode network (DMN) underlying rumination using neuroimaging data from healthy participants.

The paper examines the stability of dynamic functional connectivity in different brain regions and states, finding that higher-order networks show greater stability than primary sensory-motor areas during resting and movie watching.

The paper analyzes resting-state fMRI data from 1,300 MDD patients and 1,128 controls to find reduced default mode network functional connectivity in recurrent MDD, not first-episode MDD.

This study evaluates whether R-fMRI indices can differentiate brain tumors from normal tissue and finds that ALFF and VMHC, with Z-standardization, are effective in this task.

This paper shows that small effect sizes in resting-state fMRI studies lead to reproducibility issues, even after multiple comparison corrections, and recommends meta-analysis of un-thresholded data for better results.

The paper examines age-related changes in brain network connectivity in ASD and identifies distinct maturation patterns in three core networks.

The paper examines inter- and intra-individual variation in resting-state fMRI indices and finds high covariation, age-related decline in concordance, and temporal increases in concordance associated with decreased arousal.

This study evaluates how multiple comparison correction strategies and sample sizes affect reproducibility of R-fMRI metrics, finding that permutation tests with TFCE balance error rates and reliability, while larger samples improve replicability.