Caroline Graff

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.

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.

This study performs APOE-stratified GWAS to uncover genetic mechanisms underlying Alzheimer's disease risk and response to therapies.

This study identifies genetic risk factors for FTLD-TDP subtypes using whole-genome sequencing and meta-analysis across multiple cohorts.

The paper introduces a method to compress genetic data using autoencoders that leverage linkage disequilibrium structure, achieving high reconstruction accuracy while reducing data size.

This study uses fMRI to examine empathy processing in bvFTD patients and controls, finding reduced BOLD signal in key regions and altered connectivity patterns.

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 reports cryo-EM structures of amyloid-beta filaments with the Arctic mutation from human and mouse brains, revealing structural differences and conformational changes.

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The paper was withdrawn due to lack of full author consent and is not intended for citation.

This study analyzes plasma metabolomics in presymptomatic PSEN1-H163Y mutation carriers to identify metabolic changes associated with early-stage Alzheimer's pathology.

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 describes a method to stabilize A{beta} protofibrils using an engineered chaperone-like protein, enabling the discovery of conformation-sensitive anti-A{beta} monoclonal antibodies that target protofibrils over monomers or plaques.

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.