Frederik Barkhof

This paper develops normative models to detect deviations in molecular-enriched functional connectivity associated with amyloid burden in healthy aging brains.

The paper improves cognitive decline prediction by integrating multi-threshold polygenic scores into hippocampal-based normative models using UK Biobank data.

This study examines whether TREM2 risk variants in Alzheimer's disease patients lead to distinct clinical phenotypes, finding no baseline differences but faster cognitive decline in carriers.

This paper improves cerebrovascular brain age predictions by harmonizing arterial spin labeling (ASL) features across multiple cohorts using various methods, showing that ASL-specific harmonization enhances model performance.

The paper models disease progression in Lewy body disorders using neuropathological data, identifying three distinct trajectories of alpha-synuclein pathology with different clinical associations.

The paper identifies a tau-first subtype of Alzheimer's disease through analysis of post mortem and in vivo data, showing higher soluble TREM2 levels in tau-first cases.

This study examines the regional distribution of neurofilaments in Parkinson's disease and its association with pathological markers and MRI measures of brain atrophy and diffusivity.

This study performs GWAS on quantitative AD biomarkers and cognitive traits, identifying new loci associated with memory performance and cognitive decline.

The paper describes an MRI dataset and processing pipeline for the EPAD study, identifying quality control features and image-derived phenotypes related to aging and dementia.

This study identifies histopathological correlates of MRI cortical atrophy in Alzheimer's disease, finding that phosphorylated-tau and reactive microglia loads strongly correlate with atrophy in specific brain regions.

The paper conducts GWAS on CSF Alzheimer's disease biomarkers, identifying genetic associations with NF-L, YKL-40, and Ng levels.

The paper conducts a genome-wide association study (GWAS) on Alzheimer’s disease CSF biomarkers in the EMIF-AD dataset, identifying novel genetic signals and confirming existing associations.

The paper identifies cognitive subgroups in Alzheimer's disease based on MRI-derived atrophy patterns and links them to specific biological pathways using transcriptomic data.

The paper identifies four latent atrophy factors in posterior cortical atrophy using Bayesian modeling, finding that most patients exhibit mixed profiles rather than distinct variants.

ExploreASL provides a standardized image processing pipeline for multi-center ASL MRI studies, improving reproducibility and enabling group-level statistical analysis across diverse clinical populations.

The paper finds that patient features differentially predict amyloid-β status based on PET or CSF, with variations by disease stage, suggesting PET and CSF discordance provides unique information.

The paper uses automated MRI segmentation to quantify SVD brain changes and shows they predict cognitive and functional decline.

The paper applies multivariate mechanistic models to a clinical trial in multiple sclerosis to investigate pathways linking simvastatin to clinical outcomes.

This study finds that multiple sclerosis accelerates brain ageing, with older-appearing brains linked to faster disability progression.

This study identifies the sequence of grey matter atrophy progression in multiple sclerosis using longitudinal MRI data and shows its association with disability accumulation in relapsing-remitting MS.

This study shows that deep grey matter volume loss predicts faster disability progression in multiple sclerosis, with specific patterns in different disease subtypes.