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ZusammenfassungAufgrund steigender Fallzahlen der Alzheimer-Erkrankung und bislang eher mäßigem Erfolg der verfügbaren symptomatischen und kausalen pharmakologischen Therapien besteht ein erheblicher Bedarf, nicht-pharmakologische Behandlungsmöglichkeiten zu erforschen. Im Bereich der nicht-invasiven Hirnstimulation (non-invasive brain stimulation; NIBS) wurden verschiedene Verfahren untersucht, insbesondere die transkranielle Magnetstimulation und die transkranielle elektrische Stimulation. Zusätzlich wird derzeit die Tiefe Hirnstimulation (deep brain stimulation; DBS) als innovatives Verfahren zur gezielten Neuromodulation erforscht. Nicht-invasiven und invasiven Ansätzen gemein ist der Versuch, neuronale Aktivität zu modulieren und kognitiv-mnestische Funktionen zu verbessern. Durch sekundäre Mechanismen wie die Langzeit-Potenzierung bei NIBS oder Neurogenese bei DBS könnten auch längerfristige positive Effekte erzielt werden. Präklinische und klinische Studien ergaben bereits vielversprechende Ergebnisse bei Patientinnen und Patienten in frühen Stadien der Alzheimer-Erkrankung. Inkonsistente Studien- und Stimulationsprotokolle sowie kleine Stichproben erschweren jedoch die Bewertung der Wirksamkeit. Weitere Forschungsbemühungen sind angebracht, um einen zeitnahen Einzug nicht-invasiver oder invasiver neuromodulatorischer Ansätze in die klinische Praxis zu ermöglichen.

OBJECTIVE We aimed to estimate the incidence of cerebral sinus and venous thrombosis (CVT) within 1 month from first dose administration and the frequency of vaccine-induced immune thrombotic thrombocytopenia (VITT) as the underlying mechanism after vaccination with BNT162b2, ChAdOx1, and mRNA-1273, in Germany. METHODS A web-based questionnaire was e-mailed to all departments of neurology. We requested a report of cases of CVT occurring within 1 month of a COVID-19 vaccination. Other cerebral events could also be reported. Incidence rates of CVT were calculated by using official statistics of 9 German states. RESULTS A total of 45 CVT cases were reported. In addition, 9 primary ischemic strokes, 4 primary intracerebral hemorrhages, and 4 other neurological events were recorded. Of the CVT patients, 35 (77.8%) were female, and 36 (80.0%) were younger than 60 years. Fifty-three events were observed after vaccination with ChAdOx1 (85.5%), 9 after BNT162b2 (14.5%) vaccination, and none after mRNA-1273 vaccination. After 7,126,434 first vaccine doses, the incidence rate of CVT within 1 month from first dose administration was 0.55 (95% confidence interval [CI] = 0.38-0.78) per 100,000 person-months (which corresponds to a risk of CVT within the first 31 days of 0.55 per 100,000 individuals) for all vaccines and 1.52 (95% CI = 1.00-2.21) for ChAdOx1 (after 2,320,535 ChAdOx1 first doses). The adjusted incidence rate ratio was 9.68 (95% CI = 3.46-34.98) for ChAdOx1 compared to mRNA-based vaccines and 3.14 (95% CI = 1.22-10.65) for females compared to non-females. In 26 of 45 patients with CVT (57.8%), VITT was graded highly probable. INTERPRETATION Given an incidence of 0.02 to 0.15 per 100,000 person-months for CVT in the general population, these findings point toward a higher risk for CVT after ChAdOx1 vaccination, especially for women. ANN NEUROL 2021;90:627-639.

[Background] Early and severe neuronal loss in the cholinergic basal forebrain is observed in Alzheimer’s disease (AD). To date, cholinomimetics play a central role in the symptomatic treatment of AD dementia. Although basic research indicates that a cholinergic deficit is present in AD before dementia, the efficacy of cholinomimetics in mild cognitive impairment (MCI) remains controversial. Predictors of cholinergic impairment could guide individualized therapy., [Objective] To investigate if the extent of the cholinergic deficit, measured using positron emission tomography (PET) and the tracer 11C-N-methyl-4-piperidyl acetate (MP4A), could be predicted from the volume of cholinergic basal forebrain nuclei in non-demented AD patients., [Methods] Seventeen patients with a high likelihood of MCI due to AD and 18 age-matched cognitively healthy adults underwent MRI-scanning. Basal forebrain volume was assessed using voxel-based morphometry and a cytoarchitectonic atlas of cholinergic nuclei. Cortical acetylcholinesterase (AChE) activity was measured using MP4A-PET., [Results] Cortical AChE activity and nucleus basalis of Meynert (Ch4 area) volume were significantly decreased in MCI. The extent of the cholinergic deficit varied considerably across patients. Greater volumes of anterior basal forebrain nuclei (Ch1/2 area) and younger age (Spearman’s rho (17) = –0.596, 95% -CI [–0.905, –0.119] and 0.593, 95% -CI [0.092, 0.863])) were associated with a greater cholinergic deficit., [Conclusion] Data suggest that less atrophy of the Ch1/2 area and younger age are associated with a more significant cholinergic deficit in MCI due to AD. Further investigations are warranted to determine if the individual response to cholinomimetics can be inferred from these measures.

Several theories of cognitive compensation have been suggested to explain sustained cognitive abilities in healthy brain aging and early neurodegenerative processes. The growing number of studies investigating various aspects of task-based compensation in these conditions is contrasted by the shortage of data about resting-state compensatory mechanisms. Using our proposed criterion-based framework for compensation, we investigated 45 participants in three groups: (i) patients with mild cognitive impairment (MCI) and positive biomarkers indicative of Alzheimer's disease (AD); (ii) cognitively normal young adults; (iii) cognitively normal older adults. To increase reliability, three sessions of resting-state functional magnetic resonance imaging for each participant were performed on different days (135 scans in total). To elucidate the dimensions and dynamics of resting-state compensatory mechanisms, we used graph theory analysis along with volumetric analysis. Graph theory analysis was applied based on the Brainnetome atlas, which provides a connectivity-based parcellation framework. Comprehensive neuropsychological examinations including the Rey Auditory Verbal Learning Test (RAVLT) and the Trail Making Test (TMT) were performed, to relate graph measures of compensatory nodes to cognition. To avoid false-positive findings, results were corrected for multiple comparisons. First, we observed an increase of degree centrality in cognition related brain regions of the middle frontal gyrus, precentral gyrus and superior parietal lobe despite local atrophy in MCI and healthy aging, indicating a resting-state connectivity increase with positive biomarkers. When relating the degree centrality measures to cognitive performance, we observed that greater connectivity led to better RAVLT and TMT scores in MCI and, hence, might constitute a compensatory mechanism. The detection and improved understanding of the compensatory dynamics in healthy aging and prodromal AD is mandatory for implementing and tailoring preventive interventions aiming at preserved overall cognitive functioning and delayed clinical onset of dementia.

The diagnosis of Alzheimer's disease (AD), especially in the early stage, is still not very reliable and the development of new diagnosis tools is desirable. A diagnosis based on functional magnetic resonance imaging (fMRI) is a suitable candidate, since fMRI is non-invasive, readily available, and indirectly measures synaptic dysfunction, which can be observed even at the earliest stages of AD. However, the results of previous attempts to analyze graph properties of resting state fMRI data are contradictory, presumably caused by methodological differences in graph construction. This comprises two steps: clustering the voxels of the functional image to define the nodes of the graph, and calculating the graph's edge weights based on a functional connectivity measure of the average cluster activities. A variety of methods are available for each step, but the robustness of results to method choice, and the suitability of the methods to support a diagnostic tool, are largely unknown. To address this issue, we employ a range of commonly and rarely used clustering and edge definition methods and analyze their graph theoretic measures (graph weight, shortest path length, clustering coefficient, and weighted degree distribution and modularity) on a small data set of 26 healthy controls, 16 subjects with mild cognitive impairment (MCI) and 14 with Alzheimer's disease. We examine the results with respect to statistical significance of the mean difference in graph properties, the sensitivity of the results to model and parameter choices, and relative diagnostic power based on both a statistical model and support vector machines. We find that different combinations of graph construction techniques yield contradicting, but statistically significant, relations of graph properties between health conditions, explaining the discrepancy across previous studies, but casting doubt on such analyses as a method to gain insight into disease effects. The production of significant differences in mean graph properties turns out not to be a good predictor of future diagnostic capacity. Highest predictive power, expressed by largest negative surprise values, are achieved for both atlas-driven and data-driven clustering (Ward clustering), as long as graphs are small and clusters large, in combination with edge definitions based on correlations and mutual information transfer. Copyright © 2018 Bachmann, Jacobs, Porta Mana, Dillen, Richter, von Reutern, Dronse, Onur, Langen, Fink, Kukolja and Morrison. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.