Jingyu Liu

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

The paper links brain entropy to molecular and cellular architecture in psychosis by analyzing differences in neurotransmitter systems and mitochondrial markers between healthy controls and individuals with schizophrenia or bipolar disorder.

This paper introduces st-DenseViT, a weakly supervised spatiotemporal vision transformer that generates 4D dynamic brain networks from fMRI data, showing significant differences between schizophrenia patients and healthy controls.

This paper uses aNy-way ICA to identify a cortico-thalamo-cerebellar circuit in schizophrenia, showing altered connectivity and cognitive impairments across multiple datasets.

The paper proposes a decentralized sparse federated learning approach for neuroimaging data to reduce communication overhead while maintaining data privacy.

This paper introduces IMSC, a method to analyze coupling between brain structure and function, and finds that reading ability correlates with specific patterns of function-structure coupling in children.

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.

This paper predicts mental health quality using deep learning on resting-state fMRI data, achieving higher accuracy than alternative models.

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.

This work proposes a decentralized method for brain age estimation using MRI data and shows it achieves similar performance to centralized models.

The paper identifies ten genes associated with ADHD-related grey matter brain networks in a sample of 899 individuals.

This study examines dynamic functional connectivity in the cognitive control network across Alzheimer's disease stages, finding altered temporal connectivity patterns associated with symptom progression.

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 study evaluates how model order selection affects parallel independent component analysis (pICA) results in schizophrenia research, finding consistent sensitivity to disorder effects and partial capture of spatial patterns by the MDL algorithm.

The paper identifies structural brain network alterations in ADHD and their association with symptoms and cognitive function using source-based morphometry in a family cohort.

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.

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