Zhengyi Yang

The paper creates a spatially-resolved human brain proteome atlas, revealing protein diversity and functional specialization, and identifies therapeutic targets in epilepsy.

This study identifies developmental differences between dorsomedial and dorsolateral prefrontal cortex using multimodal data and transcriptomics, revealing a medial-lateral topographic division.

This paper demonstrates that the Digital Twin Brain can predict rTMS effects on brain state dynamics in chronic tinnitus patients with high accuracy.

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 identifies genetic and connectivity gradients that organize cortical patterning along three topographic axes.

This paper introduces a framework for optimal TMS coil placement without segmentation or meshing, validated across multiple datasets showing consistent results.

This study investigates structural brain network changes in methamphetamine users during long-term abstinence and links them to craving mechanisms using network control theory and multimodal analyses.

The paper creates a macaque brain atlas using diffusion MRI data to map connectivity, parcellation, and histology, revealing structural differences between macaque and human brains.

BAI-Net improves individualized brain mapping by integrating topological priors and connectivity data, showing better performance than traditional clustering methods.

This study uses diffusion-weighted imaging to identify structural connectivity gradients and subdivisions in the macaque insular cortex, revealing a rostrocaudal organization and significant correlations with T1w/T2w and cortical thickness maps.

The paper identifies 443 network-specific genes in the human cerebellum and links their co-expression patterns to intra-cerebellar functional connectivity, revealing gene subsets related to neurodevelopment and neurotransmission.

The paper shows that pairwise gene interactions can predict whole-genome transcription patterns in human brain areas and the entire brain, revealing a simple structure underlying transcriptome complexity.

This study shows that spatiotemporal feature selection improves multi-voxel pattern classification accuracy in fMRI data compared to single time-point spatial approaches.

This study shows that controlling emulsification in organic solvent-based tissue clearing improves transparency and preserves fluorescence without complete dehydration.

The paper shows that neural decoding of visual stimuli is higher during periods of high global network efficiency compared to low efficiency.