Simon Ducharme

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.

The paper investigates the association between CSF complement proteins and early AD pathology in at-risk individuals, finding C1q and Factor H linked to tau and synaptic markers.

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

The paper examines how changes in functional connectivity during rest and task relate to Alzheimer's pathology and episodic memory in older adults.

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 maps whole-brain cell-cell interactions across neurodegenerative diseases and identifies communication axes linked to regional brain vulnerability.

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 introduces disease-specific neuroimaging templates for frontotemporal dementia (FTD) to improve registration accuracy and reduce biases in analyses of affected populations.

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.

The paper predicts Alzheimer’s dementia onset 2-7 years before clinical diagnosis using combined cognitive scores and MRI biomarkers from a single baseline visit.

This study uses deformation-based morphometry to quantify cerebral atrophy in bvFTD patients, showing ventricular expansion as a sensitive marker for disease progression and providing sample size estimates for clinical trials.