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Background: Postoperative delirium (POD) is a serious complication following deep brain stimulation (DBS) but only received little attention. Its main risk factors are higher age and preoperative cognitive deficits. These are also main risk factors for long-term cognitive decline after DBS in Parkinson's disease (PD). Objective: To identify risk factors for POD severity after DBS surgery in PD. Methods: 57 patients underwent DBS (21 female; age 60.2±8.2; disease duration 10.5±5.9 years). Preoperatively, general, PD– and surgery-specific predictors were recorded. Montreal Cognitive Assessment and the neuropsychological test battery CANTAB ConnectTM were used to test domain-specific cognition. Volumes of the cholinergic basal forebrain were calculated with voxel-based morphometry. POD severity was recorded with the delirium scales Confusion Assessment Method for Intensive Care Unit (CAM-ICU) and Nursing Delirium Scale (NU-DESC). Spearman correlations were calculated for univariate analysis of predictors and POD severity and linear regression with elastic net regularization and leave-one-out cross-validation was performed to fit a multivariable model. Results: 21 patients (36.8%) showed mainly mild courses of POD following DBS. Correlation between predicted and true POD severity was significant (spearman rho = 0.365, p = 0.001). Influential predictors were age (p < 0.001), deficits in attention and motor speed (p = 0.002), visual learning (p = 0.036) as well as working memory (p < 0.001), Nucleus basalis of Meynert volumes (p = 0.003) and burst suppression (p = 0.005). Conclusions: General but also PD– and surgery-specific factors were predictive of POD severity. These findings underline the multifaceted etiology of POD after DBS in PD. Valid predictive models must therefore consider general, PD– and surgery-specific factors. [ABSTRACT FROM AUTHOR]

Background: Patients suffering from neurological symptoms after COVID-19 vaccination (post-COVID-19 vaccination syndrome (PCVS)) have imposed an increasing challenge on medical practice, as diagnostic precision and therapeutic options are lacking. Underlying autoimmune dysfunctions, including autoantibodies, have been discussed in neurological disorders after SARSCoV-2 infection and vaccination. Here, we describe the frequency and targets of autoantibodies against peripheral nervous system tissues in PCVS. Methods: Sera from50 PCVS patients with peripheral neurological symptoms after COVID-19 vaccination and 35 vaccinated healthy controls were used in this study. IgG autoreactivity was measured via indirect immunofluorescence assays on mouse sciatic nerve teased fibers. The frequencies of autoantibodies were compared between groups using Fisher's exact test. Serum anti-ganglioside antibodies were measured in ganglioside blots. Autoantibody target identification was performed using immunoprecipitation coupled to mass spectrometry. Subsequent target confirmation was conducted via cell-based assays and ELISA. Results: Compared with controls, PCVS patients had a significantly greater frequency of autoantibodies against peripheral nervous system structures (9/50 (18%) vs 1/35(3%); p=0.04). Autoantibodies bound to paranodes (n=5), axons (n=4), Schmidt-Lanterman incisures (n=2) and Schwann cell nuclei (n=1). Conversely, antibodies against gangliosides were absent in PCVS patients. Target identification and subsequent confirmation revealed various subunits of neurofilaments as well as DFS-70 as autoantibody epitopes. Conclusion: Our data suggest that autoantibodies against nervous system tissue could be relevant in PCVS patients. Autoantibodies against neurofilaments and cell nuclei with so far non-established links to this disease spectrum should be further elucidated to determine their biomarker potential. [ABSTRACT FROM AUTHOR]

Objective: Persisting neurological symptoms after COVID‐19 affect up to 10% of patients and can manifest in fatigue and cognitive complaints. Based on recent evidence, we evaluated whether cerebral hemodynamic changes contribute to post‐COVID syndrome (PCS). Methods: Using resting‐state functional magnetic resonance imaging, we investigated brain perfusion and oxygen level estimates in 47 patients (44.4 ± 11.6 years; F:M = 38:9) and 47 individually matched healthy control participants. Group differences were calculated using two‐sample t‐tests. Multivariable linear regression was used for associations of each regional perfusion and oxygen level measure with cognition and sleepiness measures. Exploratory hazard ratios were calculated for each brain metric with clinical measures. Results: Patients presented with high levels of fatigue (79%) and daytime sleepiness (45%). We found widespread decreased brain oxygen levels, most evident in the white matter (false discovery rate adjusted‐p‐value (p‐FDR) = 0.038) and cortical grey matter (p‐FDR = 0.015). Brain perfusion did not differ between patients and healthy participants. However, delayed patient caudate nucleus perfusion was associated with better executive function (p‐FDR = 0.008). Delayed perfusion in the cortical grey matter and hippocampus were associated with a reduced risk of daytime sleepiness (hazard ratio (HR) = 0.07, p = 0.037 and HR = 0.06, p = 0.034). Decreased putamen oxygen levels were associated with a reduced risk of poor cognitive outcome (HR = 0.22, p = 0.019). Meanwhile, lower thalamic oxygen levels were associated with a higher risk of cognitive fatigue (HR = 6.29, p = 0.017). Interpretation: Our findings of lower regional brain blood oxygen levels suggest increased cerebral metabolism in PCS, which potentially holds a compensatory function. These hemodynamic changes were related to symptom severity, possibly representing metabolic adaptations. [ABSTRACT FROM AUTHOR]

BackgroundWhite matter hyperintensities (WMH), a biomarker of small vessel disease, are often found in Alzheimer’s disease (AD) and their advanced detection and quantification can be beneficial for research and clinical applications. To investigate WMH in large-scale multicenter studies on cognitive impairment and AD, appropriate automated WMH segmentation algorithms are required. This study aimed to compare the performance of segmentation tools and provide information on their application in multicenter research.MethodsWe used a pseudo-randomly selected dataset (n = 50) from the DZNE-multicenter observational Longitudinal Cognitive Impairment and Dementia Study (DELCODE) that included 3D fluid-attenuated inversion recovery (FLAIR) images from participants across the cognitive continuum. Performances of top-rated algorithms for automated WMH segmentation [Brain Intensity Abnormality Classification Algorithm (BIANCA), lesion segmentation toolbox (LST), lesion growth algorithm (LGA), LST lesion prediction algorithm (LPA), pgs, and sysu_media] were compared to manual reference segmentation (RS).ResultsAcross tools, segmentation performance was moderate for global WMH volume and number of detected lesions. After retraining on a DELCODE subset, the deep learning algorithm sysu_media showed the highest performances with an average Dice’s coefficient of 0.702 (±0.109 SD) for volume and a mean F1-score of 0.642 (±0.109 SD) for the number of lesions. The intra-class correlation was excellent for all algorithms (>0.9) but BIANCA (0.835). Performance improved with high WMH burden and varied across brain regions.ConclusionTo conclude, the deep learning algorithm, when retrained, performed well in the multicenter context. Nevertheless, the performance was close to traditional methods. We provide methodological recommendations for future studies using automated WMH segmentation to quantify and assess WMH along the continuum of cognitive impairment and AD dementia.

Patients with Alzheimer's disease vary in their ability to sustain cognitive abilities in the presence of brain pathology. A major open question is which brain mechanisms may support higher reserve capacity, i.e. relatively high cognitive performance at a given level of Alzheimer's pathology. Higher functional MRI-assessed functional connectivity of a hub in the left frontal cortex is a core candidate brain mechanism underlying reserve as it is associated with education (i.e. a protective factor often associated with higher reserve) and attenuated cognitive impairment in prodromal Alzheimer's disease. However, no study has yet assessed whether such hub connectivity of the left frontal cortex supports reserve throughout the evolution of pathological brain changes in Alzheimer's disease, including the presymptomatic stage when cognitive decline is subtle. To address this research gap, we obtained cross-sectional resting state functional MRI in 74 participants with autosomal dominant Alzheimer's disease, 55 controls from the Dominantly Inherited Alzheimer's Network and 75 amyloid-positive elderly participants, as well as 41 amyloid-negative cognitively normal elderly subjects from the German Center of Neurodegenerative Diseases multicentre study on biomarkers in sporadic Alzheimer's disease. For each participant, global left frontal cortex connectivity was computed as the average resting state functional connectivity between the left frontal cortex (seed) and each voxel in the grey matter. As a marker of disease stage, we applied estimated years from symptom onset in autosomal dominantly inherited Alzheimer's disease and cerebrospinal fluid tau levels in sporadic Alzheimer's disease cases. In both autosomal dominant and sporadic Alzheimer's disease patients, higher levels of left frontal cortex connectivity were correlated with greater education. For autosomal dominant Alzheimer's disease, a significant left frontal cortex connectivity × estimated years of onset interaction was found, indicating slower decline of memory and global cognition at higher levels of connectivity. Similarly, in sporadic amyloid-positive elderly subjects, the effect of tau on cognition was attenuated at higher levels of left frontal cortex connectivity. Polynomial regression analysis showed that the trajectory of cognitive decline was shifted towards a later stage of Alzheimer's disease in patients with higher levels of left frontal cortex connectivity. Together, our findings suggest that higher resilience against the development of cognitive impairment throughout the early stages of Alzheimer's disease is at least partially attributable to higher left frontal cortex-hub connectivity.