Introduction Dysregulation of cerebral glucose consumption, alterations in cerebrospinal fluid (CSF) biomarkers, and cognitive impairment have been reported in patients with obstructive sleep apnoea (OSA). On these bases, OSA has been considered a risk factor for Alzheimer’s disease (AD). This study aimed to measure cognitive performance, CSF biomarkers, and cerebral glucose consumption in OSA patients and to evaluate the effects of continuous positive airway pressure (CPAP) treatment on these biomarkers over a 12-month period. Methods Thirty-four OSA patients and 34 controls underwent 18F-fluoro-2-deoxy-d-glucose positron emission tomography (18F-FDG PET), cognitive evaluation, and CSF analysis. A subgroup of 12 OSA patients treated with beneficial CPAP and performing the 12-month follow-up was included in the longitudinal analysis, and cognitive evaluation and 18F-FDG PET were repeated. Results Significantly reduced glucose consumption was observed in the bilateral praecuneus, posterior cingulate cortex, and frontal areas in OSA patients than controls. At baseline, OSA patients also showed lower β-amyloid42 and higher phosphorylated-tau CSF levels than controls. Increased total tau and phosphorylated tau levels correlated with a reduction in brain glucose consumption in a cluster of different brain areas. In the longitudinal analysis, OSA patients showed an improvement in cognition and a global increase in cerebral 18F-FDG uptake. Conclusions Cognitive impairment, reduced cerebral glucose consumption, and alterations in CSF biomarkers were observed in OSA patients, which may reinforce the hypothesis of AD neurodegenerative processes triggered by OSA. Notably, cognition and brain glucose consumption improved after beneficial CPAP treatment. Further studies are needed to evaluate the long-term effects of CPAP treatment on these AD biomarkers.

Abnormal accumulation of Tau protein is closely associated with neurodegeneration and cognitive impairment and it is a biomarker of neurodegeneration in the dementia field, especially in Alzheimer’s disease (AD); therefore, it is crucial to be able to assess the Tau deposits in vivo. Beyond the fluid biomarkers of tauopathy described in this review in relationship with the brain glucose metabolic patterns, this review aims to focus on tauopathy assessment by using Tau PET imaging. In recent years, several first-generation Tau PET tracers have been developed and applied in the dementia field. Common limitations of first-generation tracers include off-target binding and subcortical white-matter uptake; therefore, several institutions are working on developing second-generation Tau tracers. The increasing knowledge about the distribution of first- and second-generation Tau PET tracers in the brain may support physicians with Tau PET data interpretation, both in the research and in the clinical field, but an updated description of differences in distribution patterns among different Tau tracers, and in different clinical conditions, has not been reported yet. We provide an overview of first- and second-generation tracers used in ongoing clinical trials, also describing the differences and the properties of novel tracers, with a special focus on the distribution patterns of different Tau tracers. We also describe the distribution patterns of Tau tracers in AD, in atypical AD, and further neurodegenerative diseases in the dementia field.

Background: in recent years, the role of positron emission tomography (PET) and PET/computed tomography (PET/CT) has emerged as a reliable diagnostic tool in a wide variety of pathological conditions. This review aims to collect and review PET criteria developed for interpretation and treatment response assessment in cases of non-[18F]fluorodeoxyglucose ([18F]FDG) imaging in oncology. Methods: A wide literature search of the PubMed/MEDLINE, Scopus and Google Scholar databases was made to find relevant published articles about non-[18F]FDG PET response criteria. Results: The comprehensive computer literature search revealed 183 articles. On reviewing the titles and abstracts, 149 articles were excluded because the reported data were not within the field of interest. Finally, 34 articles were selected and retrieved in full-text versions. Conclusions: available criteria are a promising tool for the interpretation of non-FDG PET scans, but also to assess the response to therapy and therefore to predict the prognosis. However, oriented clinical trials are needed to clearly evaluate their impact on patient management.

Background Idiopathic rapid eye movement sleep behavior disorder (iRBD) represents the prodromal stage of α‐synucleinopathies. Reliable biomarkers are needed to predict phenoconversion. Objective The aim was to derive and validate a brain glucose metabolism pattern related to phenoconversion in iRBD (iRBDconvRP) using spatial covariance analysis (Scaled Subprofile Model and Principal Component Analysis [SSM‐PCA]). Methods Seventy‐six consecutive iRBD patients (70 ± 6 years, 15 women) were enrolled in two centers and prospectively evaluated to assess phenoconversion (30 converters, 73 ± 6 years, 14 Parkinson's disease and 16 dementia with Lewy bodies, follow‐up time: 21 ± 14 months; 46 nonconverters, 69 ± 6 years, follow‐up time: 33 ± 19 months). All patients underwent [18F]FDG‐PET (18F‐fluorodeoxyglucose positron emitting tomography) to investigate brain glucose metabolism at baseline. SSM‐PCA was applied to obtain the iRBDconvRP; nonconverter patients were considered as the reference group. Survival analysis and Cox regression were applied to explore prediction power. Results First, we derived and validated two distinct center‐specific iRBDconvRP that were comparable and significantly able to predict phenoconversion. Then, SSM‐PCA was applied to the whole set, identifying the iRBDconvRP. The iRBDconvRP included positive voxel weights in cerebellum; brainstem; anterior cingulate cortex; lentiform nucleus; and middle, mesial temporal, and postcentral areas. Negative voxel weights were found in posterior cingulate, precuneus, middle frontal gyrus, and parietal areas. Receiver operating characteristic analysis showed an area under the curve of 0.85 (sensitivity: 87%, specificity: 72%), discriminating converters from nonconverters. The iRBDconvRP significantly predicted phenoconversion (hazard ratio: 7.42, 95% confidence interval: 2.6–21.4). Conclusions We derived and validated an iRBDconvRP to efficiently discriminate converter from nonconverter iRBD patients. [18F]FDG‐PET pattern analysis has potential as a phenoconversion biomarker in iRBD patients. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

No abstract