Jianfeng Feng

The paper identifies three depression subtypes using plasma proteomics, linked to inflammation, aging, and metabolic factors, with distinct clinical and neurobiological features.

SpatialZ generates dense 3D cell atlases from planar spatial transcriptomics data by reconstructing virtual slices, validated with real 3D data and enabling new spatial analyses.

The paper develops population-specific brain charts for Chinese individuals, revealing distinct neurodevelopmental patterns compared to Western populations and showing better performance in predicting brain phenotypes.

This paper investigates context-dependent selection mechanisms using low-rank neural network modeling to distinguish between input modulation and selection vector modulation.

The paper identifies sleep-brain dimensions and biotypes in adolescents using device-based sleep data and imaging, linking them to cognitive and academic outcomes.

The paper develops a neural signature that predicts anxious arousal during uncertain threat anticipation using fMRI and multivariate modeling.

The paper introduces BEN, a deep learning method for brain tissue extraction across species and MRI modalities, showing improved accuracy and generalizability over existing tools.

The paper introduces a toolbox to generate functional and genetic annotations for neuroimaging data, demonstrating its utility in interpreting brain regions and psychiatric disorders.