Tianye Jia

The paper develops a digital twin brain model that simulates and manipulates functional brain networks to predict mental illness symptoms and responses to interventions.

The paper introduces dGAMLSS, a distributed algorithm for fitting GAMLSS models to construct population reference charts without sharing patient data.

The paper introduces DeepTaskGen, a deep learning method that generates synthetic task-based contrast maps from resting-state fMRI data, outperforming benchmarks and enabling biomarker development for cognitive tasks.

This study maps the coupling between cortical geometry and white matter tract connectivity, showing how these aspects jointly shape brain function.

This study identifies two ASD subgroups with distinct brain morphology using cross-cultural datasets, highlighting key brain regions associated with subgroup characteristics and autistic traits.

The paper introduces Brain-DiT, a deep generative model for analyzing task-fMRI data, identifying cognitive regions and subgroups linked to psychiatric conditions like depression and alcohol abuse.

The paper identifies a genetic-dependent brain signature of resilience linked to childhood abuse and depression risk in girls, showing its predictive value for emotional disorders in adolescence and early adulthood.

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

This paper introduces DeCoP, a novel decoding method that separates reward processing into evaluation and salience components, showing improved performance over traditional methods.

This study identifies a causal link between BMI and reduced grey matter volume, finds accelerated brain aging in obesity, and links it to gene expression changes in autophagy pathways.

The paper introduces brain charts as a standardized resource for benchmarking brain morphology across the human lifespan using MRI data from a large and diverse cohort.

This study identifies altered functional connectivity in ADHD adults using resting-state fMRI, linking these changes to genetic risk factors and treatment response.

The paper investigates neural mechanisms for word reading in bilinguals, finding divergent anatomical distributions but convergent cognitive processing across writing systems.

This study introduces an overall reproducibility index for MRI-based association studies and evaluates its performance across multiple datasets, showing that sample size and experimental factors influence reproducibility.

This study identifies genetic variants linked to longitudinal brain structure changes across the lifespan using MRI data from 15,640 individuals.

The paper characterises and stratifies reinforcement-related behaviours by analysing neural activation patterns in adolescents using a statistical framework.

The paper identifies genetic loci associated with human cortical structure and finds genetic correlations with neuropsychiatric disorders.

The paper reports epigenome-wide meta-analyses of blood DNA methylation with subcortical brain volumes, identifying significant associations with hippocampal volume and potential biological mechanisms.

The paper identifies 25 genetic loci associated with subcortical brain structure volumes in over 40,000 individuals, revealing novel biology and potential drug targets.