John Cornelis Van Swieten

This study uses optical genome mapping to analyze C9orf72 repeat expansion instability in blood from ALS and FTD patients, finding age-dependent differences in repeat size dynamics between conditions.

The paper analyzes somatic mutations in neurons from FTLD-TDP type C patients and controls, finding extensive low-frequency variants in TARDBP and other genes.

The paper identifies the CSF YWHAG:NPTX2 ratio as a biomarker for predicting clinical severity and phenoconversion in FTLD, showing strong correlation with disease progression and improved trial design potential.

This paper uses machine learning to stratify genetic frontotemporal dementia patients into subtypes and provides progression scores that improve trial enrichment efficiency.

This study reports cryo-EM structures of tau filaments from MAPT mutants, showing how specific mutations alter the Pick fold structure and enable reconstitution of variants.

The paper integrates genotype, clinical, and neuropathological data from 2,553 donors to show genetic risk overlaps across brain disorders and neuropsychiatric symptoms.

The paper develops an MRI-based machine learning classifier to identify presymptomatic C9orf72 carriers at high risk of disease onset within two years.

The paper shows that white matter hyperintensities (WMHs) appear before other biomarkers in GRN-related frontotemporal dementia, suggesting they are early indicators of disease progression.

The paper reports cryo-EM structures of tau filaments from individuals with P301L and P301T MAPT mutations, revealing distinct filament folds associated with FTDP-17.

This paper introduces RISH-GLM, a method to harmonize diffusion MRI data across sites without requiring matched training subjects, showing effectiveness with unmatched groups and multiple sites.

This study investigates the RIβ-L50R variant in NLPD-PKA, showing it causes misfolding, disrupts PKA holoenzyme assembly, and increases C-subunit nuclear translocation, with a potential therapeutic strategy to reduce dissociation.

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

This study identifies six genetic loci associated with progressive supranuclear palsy, including a novel locus, and links them to oligodendrocytic dysfunction and C4A expression.

This study uses whole-genome sequencing to identify new genetic loci and structural variants associated with progressive supranuclear palsy, including novel signals in APOE and H1/H2 haplotype region variants.

This study shows that PLCG2 downregulation impairs synaptic function and increases Alzheimer's disease-related markers in neurons.

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.

This study identifies Nptx2 as a regulator of complement activity in the brain, showing its role in preventing microglial synapse elimination in health and disease.

This study investigates the association between cerebellar and subcortical atrophy and neuropsychiatric symptoms in genetic frontotemporal dementia, finding significant differences across genetic mutations.

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.

This study identifies age-dependent formation of TMEM106B amyloid filaments in human brains using cryo-EM, showing they appear in older individuals but not younger ones.

This study shows that atrophy patterns in bvFTD are shaped by both global brain network structure and local transcriptomic vulnerability from disease-related genes.

The paper investigates the relationship between gene expression and brain atrophy in genetic frontotemporal dementia, finding correlations between atrophy patterns and specific gene sets related to vascular, glial, and neuronal functions.

The paper develops a random forest classifier to diagnose behavioral variant frontotemporal dementia using MRI and cognitive scores, achieving high accuracy in both training and validation cohorts.

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.

The paper detects pre-symptomatic shape changes in the thalamus using spatiotemporal analysis of MRI scans from the GENFI study, finding differences five years before symptoms appear.