Hongtu Zhu B

The paper presents a connection-level genetic atlas of human cortical functional connectivity, identifying genetic loci associated with resting and task states.

This paper benchmarks longitudinal local centile maps for predicting radiographic knee osteoarthritis progression using MRI data.

This study links gut microbiome features to brain development in infants using multimodal neuroimaging and metagenomics.

This paper maps genetic, proteomic, and metabolomic effects along human white-matter tracts using UK Biobank data, revealing localized associations with neurodegenerative and psychiatric traits.

This paper introduces CBSS, a framework for analyzing structural connectomes across multiple cohorts, showing its reliability and utility in cross-cohort diffusion MRI studies.

This paper introduces RVGA, a framework for efficient genome-wide association analysis in imaging genetics, identifying novel loci and genetic correlations in brain structures using UK Biobank data.

The paper identifies rare protein-coding variants associated with multi-organ MRI traits using exome-wide association analysis in the UK Biobank.

This study uses Mendelian randomization to identify genetic causal links between 58 diseases, 56 imaging traits, and multiple organs across 402 imaging traits and 372 clinical outcomes.

The paper constructs an Alzheimer's Disease Knowledge Graph (ADKG) to enhance disease prediction and knowledge discovery, showing improved performance in predictive modeling.

This paper introduces BGOB, a deep learning framework for longitudinal microbiome interpolation, improving analysis of microbial community trajectories and revealing disease-related associations.

This study introduces epiBrainLLM, a framework using genomic LLMs to improve causal analysis of genotype-brain-clinical pathways in Alzheimer's disease.

The paper develops CliPP to estimate subclonal mutation load (sML) and shows it predicts survival and immunotherapy response in cancers with low to moderate TMB.

MAST-Decon improves spatial transcriptomics deconvolution by incorporating spatial neighborhood and histological image features to enhance accuracy and reliability.

The paper investigates the genetic impact of the X-chromosome and sex differences in the human brain, identifying 72 genome-wide significant trait-locus pairs linked to brain-related disorders.

The paper uses genetic data to predict imaging-derived phenotypes from the UK Biobank, creating polygenic scores that identify disease risk in external cohorts.

The paper identifies 60 genetic loci associated with brain ventricular and subcortical shape features using shape measures from over 45,000 UK Biobank and ABCD study subjects.

This study identifies genetic overlaps between sleep traits and multi-organ imaging biomarkers, showing strong genetic links between sleep and psychiatric disorders.

This paper introduces spectral fiber-photometry methods to correct hemoglobin absorption artifacts in fluorescent sensor recordings, improving accuracy in measuring neuronal activity.

This paper compares eight ST deconvolution methods using four real datasets to evaluate their performance in inferring cell type composition from spatial transcriptomics data.

The paper examines how various human milk nutrients are associated with cognitive and temperament traits in infants up to 18 months old, finding persistent effects of certain nutrients on development.

This study identifies common genetic variants influencing intrinsic brain networks using rsfMRI data from 44,190 individuals, finding associations with cognitive and clinical traits.

The paper investigates phenotypic and genetic links between heart and brain using MRI data from 40,000 UK Biobank subjects, finding strong correlations and shared genetic influences.

This study identifies genetic loci associated with functional brain networks and areas using large-scale fMRI data, linking them to cognitive traits and brain disorders.

This study identifies common genetic variants influencing white matter microstructure using dMRI data from 42,919 individuals, revealing genetic links to neuropsychiatric disorders and other traits.

CliPP reconstructs subclonal architecture in cancer DNA sequencing data efficiently and accurately, outperforming existing methods in speed while maintaining high concordance with consensus calls.

The paper quantifies tumor-specific total mRNA expression from bulk data and shows its association with clinical outcomes across 15 cancer types.

This study performs transcriptome-wide association analysis on 211 neuroimaging traits, identifies 204 genes, and shows TWAS-derived polygenic risk scores explain up to 6.97% of phenotypic variance.

This study identifies 287 novel genetic variants associated with brain volumes using GWAS in 19,629 UK Biobank participants and finds genetic correlations with cognitive and mental health traits.

This study evaluates modern diffusion tractography methods using a standardized challenge, finding no significant improvement in anatomical accuracy despite methodological advances.