Xinhui Li

This paper introduces MSIVA, a method to capture latent relationships between brain structure and function, showing it identifies phenotype-associated brain regions in neuroimaging data.

This paper introduces a variational autoencoder framework to interpolate functional network connectivity data across neuropsychiatric disorders, revealing continuous gradients and heterogeneity.

This paper develops a method to detect linked features across neuroimaging modalities and identifies markers related to age, sex, cognition, and schizophrenia in large datasets.

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

The paper develops a transfer-learning U-Net model for brain extraction in non-human primates using human data, achieving better performance than traditional methods and popular MRI packages.