Jiayu Chen

The paper links functional brain network connectivity across age and psychiatric disorders to neurobiological microprofiles, showing stage-specific associations.

This study identifies three psychosis neurosubtypes using imaging data, showing distinct connectivity patterns and supporting their biological validity through relative alignment.

CSU-RSC identifies sex-dominant subgroups in psychosis by integrating supervised and unsupervised clustering on neuroimaging data, revealing sex-specific biological heterogeneity.

The paper identifies sex-dominant neurosubtypes in psychosis using fMRI data and shows their unique dysconnectivity patterns and cognitive implications.

This paper introduces the intrinsic network flow framework to explain cognitive states through phase dynamics, showing that task classification accuracy is higher using phase information than activation patterns.

The paper uses PET imaging data to identify spatial patterns of Amyloid and Tau covariance in Alzheimer's disease, showing distinct disease-stage and cognitive associations.

This paper develops a model using resting-state fMRI to identify functional connectivity patterns in schizophrenia, finding a robust cerebellothalamic disruption signature.

The paper integrates transcriptomic, molecular imaging, and neuroimaging data to show that cellular and molecular architecture aligns with intrinsic connectivity networks and functional network connectivity, mediating cognitive processes.

This study identifies spatiotemporal complexity disruptions in the psychotic brain using resting-state fMRI, revealing altered information integration and network instability.

This study integrates Florbetapir PET and functional network connectivity to identify disease progression markers and genetic risk associations in Alzheimer's disease.

This study compares functional network energy across multiscale brain networks in preterm and full-term adolescents from the ABCD study, identifying differences in network stability and flexibility.

STRiDE improves dynamic brain state estimation by integrating structural and functional connectivity, revealing disruptions in schizophrenia.

This study uses sgr-ICA with 500 components on a large rsfMRI dataset to identify fine-grained intrinsic connectivity networks (ICNs) and reveals dysconnectivity patterns in schizophrenia.

This study links multi-scale brain connectivity networks to cognitive and behavioral functions in adolescents, finding significant associations with attention, working memory, and behavioral regulation.

This study uses high-order ICA on PET scans to identify amyloid-beta networks in Alzheimer's Disease, revealing distinct gray and white matter patterns associated with cognitive impairment.

The paper presents a WM intrinsic connectivity network template derived from 100,000 fMRI scans, showing distinct WM and GM networks with task-related activity and differences in schizophrenia.

This paper introduces a nonlinear method to estimate brain networks from fMRI data, showing it better distinguishes schizophrenia patients from controls than linear methods.

This paper introduces dynamic fusion, a method to link brain structure and function over time, showing it captures dynamic functional processes and detects nuanced structural components.

This paper introduces a multi-modal AI model for schizophrenia classification using imaging and genetic data, achieving 94.10% accuracy and identifying key brain regions and genes associated with the disorder.

This paper refines a brain age model for adolescents, improving prediction accuracy and showing associations between brain age gap and cognitive abilities.

This study investigates environmental and genetic factors influencing anxiety and depression in children and adolescents across three cohorts, identifying significant environmental impacts and a novel SNP associated with the disorders.

The paper develops a method to estimate time-resolved spatial networks and applies it to study sex-specific schizophrenia disruptions and their genetic links.

The paper compares imputed gene expression (gPRS) and SNP-based polygenic risk scores (sPRS) in predicting gray matter phenotypes using UK Biobank data, finding gPRS provides better prediction accuracy.

The paper examines the association between static and dynamic functional network connectivity and genetic risk of Alzheimer's disease using rs-fMRI and APOE data from cognitively normal individuals.

The paper identifies and replicates expression and methylation modules in schizophrenia patients and healthy controls, showing their association with astrocyte and oligodendrocyte functions and genetic risk.

This paper introduces spICA to link genomic and brain imaging data, identifying a SNP and GMV association in ADHD linked to working memory deficits.

This study identifies 45 structural-functional component maps across 9 functional domains using independent component analysis in two cohorts, showing varying S-F coherence in different brain regions.

This study analyzes age-related changes in brain structure and function across 5967 individuals from 13 to 72 years old, revealing nonlinear patterns in gray matter volume, structural network correlation, and functional network connectivity.

This paper introduces a method to fuse functional and structural MRI data by integrating temporal information, identifying linked variability and covariation in schizophrenia.