Benedetta Nacmias

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.

The paper identifies ERP biomarkers distinguishing subjective cognitive decline, MCI, and healthy controls during a sustained visual attention task, showing condition-specific dynamics and task performance correlations.

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 uses proteomic analysis of patient iPSC-derived brain cells and post-mortem hippocampal tissue to study early AD pathogenesis and prion-like spread.

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.

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.