Erik D. Roberson

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.

Blocking the carboxy terminus of progranulin enhances gene therapy efficacy in progranulin-deficient mice by reducing sortilin interactions and improving biomarker outcomes.

This study generates a conditional tau knockout model and shows that tau reduction in excitatory neurons provides excitoprotective effects, while reduction in inhibitory neurons increases seizure susceptibility, even when induced in adulthood.

This paper evaluates the polygenic risk score for Alzheimer's disease in Japanese individuals, showing its utility despite genetic differences from European populations.

The paper analyzes genomic data from early-onset and atypical dementia patients to show that both rare and common genetic variations contribute to dementia risk.

This study shows that AD mouse models have impaired clock gene expression and disrupted diurnal regulation of hippocampal inhibitory transmission and spatial memory.

This study identifies a non-coding variant in SCN1A that causes aberrant inclusion of a poison exon, leading to reduced Nav1.1 expression and Dravet syndrome in a mouse model.

This study shows that increased BIN1 expression in neurons leads to hyperexcitability, which is modulated by Tau, suggesting a role for BIN1 in Alzheimer's disease pathogenesis.

The paper identifies rare variants in TET2 associated with early-onset Alzheimer's disease and frontotemporal dementia, highlighting both coding and non-coding variants as risk factors.

Genome sequencing for early-onset dementia identified genetic variants in 50% of patients, including pathogenic variants and moderate penetrance alleles, supporting an oligogenic model.

The paper develops a peptide inhibitor of Tau-SH3 interactions and shows it reduces Aβ oligomer toxicity in neurons.

BLAzER is a semi-automated workflow for regional brain PET quantification validated on ADNI data, showing strong agreement with ADNI and minimal discrepancies.

This paper develops a blood-based machine learning model to predict cerebral spinal fluid Aβ1-42 levels, showing high accuracy in identifying Alzheimer's disease risk.