Tengda Zhao

The paper identifies two distinct ADHD biotypes using brain imaging and transcriptomic data, showing different developmental, molecular, and treatment responses.

This paper introduces FOCA, a framework to analyze spatial coordination of functional brain networks, revealing convergent and divergent patterns and their developmental origins.

The paper maps personalized functional topography in the neonatal human brain using neuroimaging data and links it to neurocognitive outcomes in early childhood.

The paper analyzes functional and structural MRI data from 33,250 individuals to map lifespan changes in the functional connectome, identifying key developmental stages and differences in neurological disorders.

This study investigates how multiscale structural brain gradients develop from childhood to adolescence and links these changes to cortical morphology and functional specialization.

This study investigates how overlapping functional modules in the developing human brain change with age and their structural correlates using multimodal MRI data.

This study examines neonatal brain connectome dynamics using resting-state fMRI and finds that these dynamics predict cognitive and language outcomes at age 2.

DCP automates diffusion tensor imaging processing on Windows to construct brain structure networks, offering a GUI and integration of existing tools.

The paper maps cortical thinning patterns during childhood to adolescence, showing they are structurally constrained by white matter networks and linked to gene expression signatures.

The paper models the interdependent structural and functional connectome in the human brain, finding topographic heterogeneity and cognitive significance.

This paper identifies a dual long-axis organization in the human hippocampus during youth, showing its role in cortical hierarchy maturation and functional reorganization.

SACNet corrects susceptibility artifacts in dMRI scans using a registration network with neuroanatomical constraints, showing improved performance across diverse datasets.

This paper introduces 3D-MASNet, a 3D mixed-scale asymmetric convolutional network for segmenting infant brain MR images, achieving high Dice coefficients in GM, WM, and CSF.

The paper investigates how structural and functional brain connectome variability are structurally constrained and finds a hierarchical relationship between primary and heteromodal brain regions.

This study tracks the development of a primary-to-transmodal gradient in the brain from childhood to adolescence, linking it to cognitive functions and gene expression.

The paper identifies consistent and reproducible functional connectome hubs in the human brain using meta-connectomic analysis and links them to specific transcriptomic patterns.

The paper examines the longitudinal development of brain network dynamics during childhood and adolescence, linking changes to gene expression profiles and ion transport-related genes.

The paper creates age-appropriate structural brain atlases for Chinese children using MRI data and shows they improve brain age prediction compared to Western-based atlases.

This study examines structural network maturation in preterm and term-born neonates using diffusion MRI, finding faster increases in hub and rich-club connectivity during the third trimester.