Introduction: In this multicenter study on subjective cognitive decline (SCD) in community-based and memory clinic settings, we assessed the (1) incidence of Alzheimer’s disease (AD) and non-AD dementia and (2) determinants of progression to dementia. Methods: Eleven cohorts provided 2978 participants with SCD and 1391 controls. We estimated dementia incidence and identified risk factors using Cox proportional hazards models. Results: In SCD, incidence of dementia was 17.7 (95% Poisson confidence interval 15.2–20.3)/1000 person-years (AD: 11.5 [9.6–13.7], non-AD: 6.1 [4.7–7.7]), compared with 14.2 (11.3–17.6) in controls (AD: 10.1 [7.7–13.0], non-AD: 4.1 [2.6–6.0]). The risk of dementia was strongly increased in SCD in a memory clinic setting but less so in a community-based setting. In addition, higher age (hazard ratio 1.1 [95% confidence interval 1.1–1.1]), lower Mini-Mental State Examination (0.7 [0.66–0.8]), and apolipoprotein E ε4 (1.8 [1.3–2.5]) increased the risk of dementia. Discussion: SCD can precede both AD and non-AD dementia. Despite their younger age, individuals with SCD in a memory clinic setting have a higher risk of dementia than those in community-based cohorts.

Background-. A better understanding is needed concerning the risk factors and markers of disease

Aim The aim of this paper is to describe the clinical features of COVID‐19‐related encephalopathy and their metabolic correlates using brain 2‐desoxy‐2‐fluoro‐D‐glucose (FDG)‐positron‐emission tomography (PET)/computed tomography (CT) imaging. Background and purpose A variety of neurological manifestations have been reported in association with COVID‐19. COVID‐19‐related encephalopathy has seldom been reported and studied. Methods We report four cases of COVID‐19‐related encephalopathy. The diagnosis was made in patients with confirmed COVID‐19 who presented with new‐onset cognitive disturbances, central focal neurological signs, or seizures. All patients underwent cognitive screening, brain magnetic resonance imaging (MRI), lumbar puncture, and brain 2‐desoxy‐2‐fluoro‐D‐glucose (FDG)‐positron‐emission tomography (PET)/computed tomography (CT) (FDG‐PET/CT). Results The four patients were aged 60 years or older, and presented with various degrees of cognitive impairment, with predominant frontal lobe impairment. Two patients presented with cerebellar syndrome, one patient had myoclonus, one had psychiatric manifestations, and one had status epilepticus. The delay between first COVID‐19 symptoms and onset of neurological symptoms was between 0 and 12 days. None of the patients had MRI features of encephalitis nor significant cerebrospinal fluid (CSF) abnormalities. SARS‐CoV‐2 RT‐PCR in the CSF was negative for all patients. All patients presented with a consistent brain FDG‐PET/CT pattern of abnormalities, namely frontal hypometabolism and cerebellar hypermetabolism. All patients improved after immunotherapy. Conclusions Despite varied clinical presentations, all patients presented with a consistent FDG‐PET pattern, which may reflect an immune mechanism.

Early biomarkers are needed to identify individuals at high risk of preclinical Alzheimer’s disease and to better understand the pathophysiological processes of disease progression. Preclinical Alzheimer’s disease EEG changes would be non-invasive and cheap screening tools and could also help to predict future progression to clinical Alzheimer’s disease. However, the impact of amyloid-β deposition and neurodegeneration on EEG biomarkers needs to be elucidated. We included participants from the INSIGHT-preAD cohort, which is an ongoing single-centre multimodal observational study that was designed to identify risk factors and markers of progression to clinical Alzheimer’s disease in 318 cognitively normal individuals aged 70–85 years with a subjective memory complaint. We divided the subjects into four groups, according to their amyloid status (based on 18F-florbetapir PET) and neurodegeneration status (evidenced by 18F-fluorodeoxyglucose PET brain metabolism in Alzheimer’s disease signature regions). The first group was amyloid-positive and neurodegeneration-positive, which corresponds to stage 2 of preclinical Alzheimer’s disease. The second group was amyloid-positive and neurodegeneration-negative, which corresponds to stage 1 of preclinical Alzheimer’s disease. The third group was amyloid-negative and neurodegeneration-positive, which corresponds to ‘suspected non-Alzheimer’s pathophysiology’. The last group was the control group, defined by amyloid-negative and neurodegeneration-negative subjects. We analysed 314 baseline 256-channel high-density eyes closed 1-min resting state EEG recordings. EEG biomarkers included spectral measures, algorithmic complexity and functional connectivity assessed with a novel information-theoretic measure, weighted symbolic mutual information. The most prominent effects of neurodegeneration on EEG metrics were localized in frontocentral regions with an increase in high frequency oscillations (higher beta and gamma power) and a decrease in low frequency oscillations (lower delta power), higher spectral entropy, higher complexity and increased functional connectivity measured by weighted symbolic mutual information in theta band. Neurodegeneration was associated with a widespread increase of median spectral frequency. We found a non-linear relationship between amyloid burden and EEG metrics in neurodegeneration-positive subjects, either following a U-shape curve for delta power or an inverted U-shape curve for the other metrics, meaning that EEG patterns are modulated differently depending on the degree of amyloid burden. This finding suggests initial compensatory mechanisms that are overwhelmed for the highest amyloid load. Together, these results indicate that EEG metrics are useful biomarkers for the preclinical stage of Alzheimer’s disease.