Jonathan M. Schott

The paper uses plasma proteomics to estimate biological age of multiple cell types and links aging signatures to disease risk and mortality across 60,000 individuals.

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

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.

This study shows microglia produce ABri peptide in FBD, contributing to amyloid plaques and neurodegeneration, and suggests ITM2B/BRI2 is involved in microglial disease responses.

The paper analyzes exome data from 22,319 individuals to identify rare CNVs and 22q11.21 dosage as risk factors for Alzheimer's disease.

The paper identifies high-priority missense variants in SORL1 associated with increased Alzheimer's Disease risk and earlier onset, suggesting clinical counseling considerations for carriers.

The paper identifies rare damaging variants in ATP8B4 and ABCA1 as novel risk factors for Alzheimer's Disease using exome sequencing data from 32,558 individuals.

The paper builds genetically adjusted hippocampal volume nomograms using polygenic scores and evaluates their clinical impact in detecting dementia patients.

This paper introduces a tissue-weighted mean method to reduce bias in NODDI metrics by accounting for CSF partial volume, showing it improves accuracy in ROI analysis for neurodegenerative disease studies.

This study demonstrates that whole genome sequencing can effectively detect repeat expansions in neurological disorders with high sensitivity and specificity, enabling diagnosis in previously undiagnosed patients.

The DPUK Data Portal provides secure access to cohort data for dementia research, supporting multi-modal analyses and collaborations across multiple institutions.

This paper introduces SuStaIn, a machine learning method that identifies disease subtypes and their temporal progression from cross-sectional data, improving prediction of diagnostic conversion in neurodegenerative diseases.

Genetic correlations across 25 brain disorders (215,683 cases) reveal broad psychiatric common-variant sharing, largely distinct neurological disorders, and migraine as the only significant cross-category link.

This paper develops data-driven models to predict Alzheimer’s disease progression in dominantly-inherited cases, showing improved accuracy over traditional estimates of years until onset.