Friedemann Paul

The paper identifies an intrathecal antibody response to EBV BRRF2 and human antigens in MS, showing cross-reactivity and association with increased IgG synthesis.

The paper uses mRNA-engineered dendritic cells to detect AQP4-specific T cell responses in NMOSD patients, showing strong activation in a patient with recent disease activity.

The paper replicates and extends the complexome framework by linking functional connectivity to symbolic BOLD patterns, showing clinical relevance in anti-NMDA receptor encephalitis and introducing a new measure of pattern dissimilarity.

This study links altered structural brain complexity in post-acute anti-NMDA receptor encephalitis to memory dysfunction and psychiatric outcomes using fractal dimensionality analysis.

This study uses graph theoretical analysis of structural brain networks to show that hippocampal hub failure in NMDAR encephalitis is linked to long-term memory impairment.

This study links persistent cognitive deficits in anti-LGI1 encephalitis to structural brain network reorganization despite immunotherapy and clinical recovery.

The paper uses multilayer network analysis and multi-omics data to identify biological pathways in MS that explain disease severity and central nervous system damage.

The paper investigates neural pathways linking psychological stress and brain health in healthy individuals and multiple sclerosis patients, finding similar functional connectivity patterns.

This study uses time-resolved functional connectivity analysis to show that altered brain network dynamics correlate with disease severity and multi-domain impairments in relapsing-remitting multiple sclerosis.

The paper predicts combination therapies for MS by modeling patient-specific signaling networks using phosphoproteomics data and identifies TAK1 as a co-druggable target.

This study uses dynamic functional connectivity analysis to identify state-dependent brain activity patterns in anti-NMDA receptor encephalitis, finding distinct differences from controls and strong predictive power.

DeepWAS integrates regulatory effect predictions of variants into GWAS to identify regulatory SNPs associated with diseases and traits.

The paper identifies low-frequency coding variants contributing to multiple sclerosis risk using a large case-control study.