Jonathan Daniel Rohrer

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 conducts a GWAS on Pick's disease and identifies two SNPs associated with risk, though not at genome-wide significance, suggesting potential genetic factors beyond MAPT mutations.

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

The paper shows that loss of TDP-43 function leads to the expression of de novo proteins from mis-spliced mRNA transcripts in ALS/FTD.

This paper introduces a Bayesian segmentation algorithm for thalamic nuclei using diffusion MRI and an improved histological atlas, showing improved accuracy in identifying nuclei and detecting Alzheimer's disease involvement.

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

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 DPUK Data Portal provides secure access to cohort data for dementia research, supporting multi-modal analyses and collaborations across multiple institutions.

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 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.