Mingrui Xia

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

This paper constructs sex-specific lifespan brain charts using neuroimaging data from 59,915 individuals, revealing dynamic sex differences in brain development and improving detection of neuropsychiatric abnormalities.

This study identifies two distinct T2D biotypes using cortical network analysis, showing different brain-metabolic coupling patterns and cognitive consequences.

This paper identifies a tripartite organization of the human ventromedial prefrontal cortex using multimodal analyses and shows its reproducibility and cross-species correspondence.

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.

The paper examines how infant brain language networks relate to school-age reading abilities through phonological skills development over seven years.

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 identifies two distinct ADHD biotypes based on brain morphology, showing different treatment responses and genetic correlates.

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.

The paper shows that a sensorimotor-to-visual connectome gradient present at birth predicts cognitive outcomes at 2 years of age in preterm and term babies.

The paper identifies two neurophysiological subtypes of MDD using normative models of functional connectivity in a large multisite fMRI dataset.

This paper introduces a dual-projection based ICA denoising method to remove scanner/site effects in multi-site MRI data, showing improved performance over traditional ICA and GLM methods.

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 paper introduces a deep learning framework to harmonize multisite neuroimaging data by removing site effects while preserving biological features, showing improved performance over traditional methods.

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.

This study identifies altered brain network dynamics and associated transcriptomic signatures in autism spectrum disorder using resting-state fMRI and postmortem gene expression data from the ABIDE dataset.

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.

This study identifies dysfunction in the principal connectome gradient in MDD and links it to gene expression profiles related to transsynaptic signaling and calcium ion binding.

This study identifies transcriptional signatures associated with the hierarchical organization of dynamic brain networks using fMRI and gene expression data.

This study investigates reproducibility of functional brain alterations in MDD using a large multisite R-fMRI dataset and finds consistent patterns across centers but notes influences from clinical factors.