Raquel Sánchez‐Valle

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 combines GWAS of amyloid endophenotypes with proteomic analysis to identify genes and proteins associated with amyloid burden and its biological pathways.

The paper uses computational modeling to identify key brain regions for personalized neurostimulation strategies to improve cognitive interventions in Alzheimer's 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 describes MRI and PET protocols used in the DIAN study to investigate preclinical Alzheimer's disease and reports key imaging findings.

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

This paper reports six novel genetic variants associated with Alzheimer's disease risk and demonstrates the utility of polygenic risk scores for risk stratification and age at onset prediction.

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 identifies disease-stage-specific patterns of functional degradation and compensation in Alzheimer’s disease using eigenvector centrality mapping and CSF biomarkers.