Yu Zhang

This study identifies energy inefficiency as a mechanism underlying brain state dysregulation in major depressive disorder, linking it to specific brain regions and biological factors.

The paper develops a cross-species cortical alignment framework to analyze structural connectivity and cortical expansion across primates, revealing conserved geometric patterns and human-specific expansions.

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

The paper investigates theta phase resetting in the human parahippocampal gyrus during navigation, finding it correlates with BOLD activation in specific PHG clusters.

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

This paper introduces biologically-constrained graph neural networks to decode cognitive processes, revealing functional gradients from sensation to cognition and showing improved inter-subject alignment and behavioral prediction.

This study shows that deep graph neural networks using human connectomes improve cognitive decoding accuracy and robustness by integrating brain dynamics with high-order graph convolutions.

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.

This paper uses deep graph convolutions to decode brain activity across cognitive domains, finding that integration boosts high-order cognition decoding while segregation suffices for motor tasks.

This paper develops a multidomain brain decoder to identify cognitive states from fMRI data with high accuracy across multiple cognitive domains.

This paper introduces a method to identify dynamic states of brain parcellations, finding that dynamic parcellations are more reproducible than static ones and reveal distinct states in heteromodal cortices.

This study investigates the transferability of deep neural networks in brain decoding using fMRI data, finding that scanner site conditions significantly impact feature transfer compared to task domains.

The paper uses an agent-based model to simulate neurodegenerative disease spread, showing how local vulnerability and network connectivity drive Parkinson's disease progression.

This study investigates the genetic overlap between obesity and neurobehavioural factors using brain imaging data from the Human Connectome Project, finding significant genetic correlations between obesity and brain morphology, cognitive function, and personality traits.

The paper uses diffusion and functional MRI data to identify a tripartite parcellation of the human substantia nigra based on connectivity and functional coding of value and salience, linking these to impulsivity traits.