Rongtao Jiang

The paper identifies inflated effect sizes in functional neuroimaging studies and proposes a correction method to improve study planning and detection of brain effects.

This study analyzes self-citation rates across 100,347 articles from 63 neuroscience journals between 2000-2020, revealing trends in self-citation by author position, country, gender, and field.

This study identifies a structural connectivity pattern shared between physical activity and depressive mood using UK Biobank data, linking it to neurotransmitter receptors and pathways.

The paper evaluates the statistical power of external validation in brain-phenotype predictions and finds that sample size impacts power and effect size inflation.

This study evaluates how data leakage affects machine learning models using neuroimaging data, finding that some leakage types significantly inflate performance while others reduce it or affect model interpretations.

The paper tests predictive models' ability to generalize across diverse, unharmonized neuroimaging datasets and finds that cross-dataset training improves prediction robustness.

This paper improves brain-behavior prediction by imputing missing data in connectome-based models, increasing sample size and explained variance.

CAROT remaps connectomes between atlases without raw data, enabling reuse of processed data across different atlases.

This paper reviews machine learning approaches for predicting cognition and behavior from neuroimaging data, highlighting challenges and future directions in the field.

This paper introduces a method to fuse functional and structural MRI data by integrating temporal information, identifying linked variability and covariation in schizophrenia.