Graz. Sanatorium am Rosenberg

Ehemaliges Sanatorium in Mariagrü

Multi-tracer model for staging cortical amyloid deposition using PET imaging

OBJECTIVE: To develop and evaluate a model for staging cortical amyloid deposition using PET with high generalizability. METHODS: 3027 subjects (1763 Cognitively Unimpaired (CU), 658 Impaired, 467 Alzheimer's disease (AD) dementia, 111 non-AD dementia, and 28 with missing diagnosis) from six cohorts (EMIF-AD, ALFA, ABIDE, ADC, OASIS-3, ADNI) who underwent amyloid PET were retrospectively included; 1049 subjects had follow-up scans. Applying dataset-specific cut-offs to global Standard Uptake Value ratio (SUVr) values from 27 regions, single-tracer and pooled multi-tracer regional rankings were constructed from the frequency of abnormality across 400 CU subjects (100 per tracer). The pooled multi-tracer ranking was used to create a staging model consisting of four clusters of regions as it displayed a high and consistent correlation with each single-tracer ranking. Relationships between amyloid stage, clinical variables and longitudinal cognitive decline were investigated. RESULTS: SUVr abnormality was most frequently observed in cingulate, followed by orbitofrontal, precuneal, and insular cortices, then the associative, temporal and occipital regions. Abnormal amyloid levels based on binary global SUVr classification were observed in 1.0%, 5.5%, 17.9%, 90.0%, and 100.0% of stage 0-4 subjects, respectively. Baseline stage predicted decline in MMSE (ADNI: N=867, F=67.37, p3000 subjects across cohorts and radiotracers, and detects pre-global amyloid burden and distinct risk profiles of cognitive decline within globally amyloid-positive subjects

Correction to: The impact of drug resistance on the risk of tuberculosis infection and disease in child household contacts: a cross sectional study.

After publication of the original article [1] the authors noted that the following errors had occurred

Tau protein spreads through functionally connected neurons in Alzheimer’s disease: a combined MEG/PET study

Abstract Background: Recent studies on Alzheimer’s disease (AD) suggest that tau proteins spread through the brain following neuronal connections. Several mechanisms could be involved in this process: spreading between brain regions that interact strongly (functional connectivity); through the pattern of anatomical connections (structural connectivity); or simple diffusion. Using magnetoencephalography (MEG), we investigated which spreading pathways influence tau protein spreading by modelling the tau propagation process using an epidemic spreading model. We compared the modelled tau depositions with [18F]flortaucipir PET binding potentials at several stages of the AD continuum. Methods: In this cross-sectional study, we analysed source-reconstructed MEG data and dynamic 100-minutes [18F]flortaucipir PET from 57 subjects positive for amyloid-beta (Aβ)-pathology (preclinical AD (n=16), mild cognitive impairment (MCI) due to AD (n=16) and AD dementia (n=25)). Cognitively healthy subjects without Aβ-pathology were included as controls (n=25). Tau propagation was modelled as an epidemic process (susceptible-infected model) on MEG-based functional networks (in alpha (8-13Hz) and beta (13-30Hz) bands), a structural, or diffusion network, starting from the middle and inferior temporal lobe. The group-level network of the control group was used as input for the model to predict tau deposition in 3 stages of the AD continuum. To assess performance, model output was compared to the group-specific tau deposition patterns as measured with [18F]flortaucipir PET. We repeated the analysis by using networks of the preceding disease stage and/or using regions with most observed tau deposition during the preceding stage as seeds. Results: In the preclinical AD stage, the functional networks predicted most of the modelled tau-PET binding potential, with best correlations between model and tau-PET (AEC-c alpha C=0.584; AEC-c beta C=0.569), followed by the structural network (C=0.451) and simple diffusion (C=0.451). Prediction accuracy declined for the MCI and AD dementia stages, although the correlation between modelled tau and tau-PET binding remained highest for the functional networks (C=0.384; C=0.376). Replacing the control-network with the network from the preceding disease stage and/or alternative seeds improved prediction accuracy in MCI but not in the dementia stage. Conclusion: These results suggest that in addition to structural connections, functional connections play an important role in tau spread, and highlight that neuronal dynamics play a key role in promoting this pathological process. Aberrant neuronal communication patterns should be taken into account when identifying targets for future therapy. Our results also suggest that this process is more important in earlier disease stages (preclinical AD/MCI); possibly, in later stages, other processes may be influential