Barbara Borroni

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 investigates cortical plasticity in the tactile mirror system of individuals with borderline personality disorder using cross-modal paired associative stimulation.

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 uses high-order ICA on PET scans to identify amyloid-beta networks in Alzheimer's Disease, revealing distinct gray and white matter patterns associated with cognitive impairment.

The paper presents a multi-omics dataset for genetic subtypes of frontotemporal dementia to identify molecular mechanisms disrupted in the disease.

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

This paper performs a large meta-analysis of GWAS in dementia with Lewy bodies, identifying sex-specific genetic risk signals and highlighting the need for larger cohorts.

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.

This study identifies CAP2 as a regulator of Cofilin in synaptic plasticity, showing its role in LTP and its disruption in Alzheimer’s disease.

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.

The paper identifies a genetic variant near CXCR4 associated with increased risk for PSP and PD, showing dysregulation of CXCR4 and microglia-related genes in neurodegenerative diseases.

This study identifies genetic overlaps between frontotemporal dementia and immune-mediated diseases, finding strong enrichment with rheumatoid arthritis and moderate enrichment with other immune conditions.