RESUMO
BACKGROUND: Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is characterized by distinct structural and functional brain alterations, predominantly affecting the medial temporal lobes and the hippocampus. Structural connectome analysis with graph-based investigations of network properties allows for an in-depth characterization of global and local network changes and their relationship with clinical deficits in NMDAR encephalitis. METHODS: Structural networks from 61 NMDAR encephalitis patients in the post-acute stage (median time from acute hospital discharge: 18 months) and 61 age- and sex-matched healthy controls (HC) were analyzed using diffusion-weighted imaging (DWI)-based probabilistic anatomically constrained tractography and volumetry of a selection of subcortical and white matter brain volumes was performed. We calculated global, modular, and nodal graph measures with special focus on default-mode network, medial temporal lobe, and hippocampus. Pathologically altered metrics were investigated regarding their potential association with clinical course, disease severity, and cognitive outcome. RESULTS: Patients with NMDAR encephalitis showed regular global graph metrics, but bilateral reductions of hippocampal node strength (left: p = 0.049; right: p = 0.013) and increased node strength of right precuneus (p = 0.013) compared to HC. Betweenness centrality was decreased for left-sided entorhinal cortex (p = 0.042) and left caudal middle frontal gyrus (p = 0.037). Correlation analyses showed a significant association between reduced left hippocampal node strength and verbal long-term memory impairment (p = 0.021). We found decreased left (p = 0.013) and right (p = 0.001) hippocampal volumes that were associated with hippocampal node strength (left p = 0.009; right p < 0.001). CONCLUSIONS: Focal network property changes of the medial temporal lobes indicate hippocampal hub failure that is associated with memory impairment in NMDAR encephalitis at the post-acute stage, while global structural network properties remain unaltered. Graph theory analysis provides new pathophysiological insight into structural network changes and their association with persistent cognitive deficits in NMDAR encephalitis.
RESUMO
The rise of cancer immunotherapy has been a milestone in clinical oncology. Above all, immune checkpoint inhibitor treatment (ICI) with monoclonal antibodies targeting programmed cell death protein 1 (PD-1), programmed cell death-ligand 1 (PD-L1), and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) has improved survival rates for an increasing number of malignancies. However, despite the clinical benefits, ICI-related autoimmunity has become a significant cause of non-relapse-related morbidity and mortality. Neurological immune-related adverse events (irAE-n) are particularly severe toxicities with a high risk for chronic illness, long-term steroid dependency, and early ICI treatment termination. While the clinical characteristics of irAE-n are well described, little is known about underlying immune mechanisms and potential biomarkers. Recently, high frequencies of neuronal autoantibodies in patients with irAE-n have been reported, however, their clinical relevance is unclear. Here, we present a dataset on neuronal autoantibody profiles in ICI-treated cancer patients with and without irAE-n, which was generated to investigate the potential role of neuronal autoantibodies in ICI-induced autoimmunity. Between September 2017 and January 2022 serum samples of 29 cancer patients with irAE-n post-ICI treatment) and 44 cancer control patients without high-grade immune-related adverse events (irAEs, n = 44 pre- and post-ICI treatment) were collected and tested for a large panel of brain-reactive and neuromuscular autoantibodies using indirect immunofluorescence and immunoblot assays. Prevalence of autoantibodies was compared between the groups and correlated with clinical characteristics such as outcome and irAE-n manifestation. These data represent the first systematic comparison of neuronal autoantibody profiles between ICI-treated cancer patients with and without irAE-n, providing valuable information for both researchers and clinicians. In the future, this dataset may be valuable for meta-analyses on the prevalence of neuronal autoantibodies in cancer patients.