Sleep architecture changes in the APP23 mouse model manifest at onset of cognitive deficits

Alzheimer's disease (AD), which accounts for most of the dementia cases, is, aside from cognitive deterioration, often characterized by the presence of non-cognitive symptoms such as activity and sleep disturbances. AD patients typically experience increased sleep fragmentation, excessive daytime sleepiness and night-time insomnia. Here, we sought to investigate the link between sleep architecture, cognition and amyloid pathology in the APP23 amyloidosis mouse model for AD. By means of polysomnographic recordings the sleep-wake cycle of freely-moving APP23 and wild-type (WT) littermates of 3, 6 and 12 months of age was examined. In addition, ambulatory cage activity was assessed by interruption of infrared beams surrounding the home cage. To assess visuo-spatial learning and memory a hidden-platform Morris-type Water Maze (MWM) experiment was performed. We found that sleep architecture is only slightly altered at early stages of pathology, but significantly deteriorates from 12 months of age, when amyloid plaques become diffusely present. APP23 mice of 12 months old had quantitative reductions of NREM and REM sleep and were more awake during the dark phase compared to WT littermates. These findings were confirmed by increased ambulatory cage activity during that phase of the light-dark cycle. No quantitative differences in sleep parameters were observed during the light phase. However, during this light phase, the sleep pattern of APP23 mice was more fragmented from 6 months of age, the point at which also cognitive abilities started to be affected in the MWM. Sleep time also positively correlated with MWM performance. We also found that spectral components in the EEG started to alter at the age of 6 months. To conclude, our results indicate that sleep architectural changes arise around the time the first amyloid plaques start to form and cognitive deterioration becomes apparent. These changes start subtle, but gradually worsen with age, adequately mimicking the clinical condition

Recommendations on the use of EEG monitoring in critically ill patients: consensus statement from the neurointensive care section of the ESICM.

OBJECTIVES: Recommendations for EEG monitoring in the ICU are lacking. The Neurointensive Care Section of the ESICM assembled a multidisciplinary group to establish consensus recommendations on the use of EEG in the ICU. METHODS: A systematic review was performed and 42 studies were included. Data were extracted using the PICO approach, including: (a) population, i.e. ICU patients with at least one of the following: traumatic brain injury, subarachnoid hemorrhage, intracerebral hemorrhage, stroke, coma after cardiac arrest, septic and metabolic encephalopathy, encephalitis, and status epilepticus; (b) intervention, i.e. EEG monitoring of at least 30 min duration; (c) control, i.e. intermittent vs. continuous EEG, as no studies compared patients with a specific clinical condition, with and without EEG monitoring; (d) outcome endpoints, i.e. seizure detection, ischemia detection, and prognostication. After selection, evidence was classified and recommendations developed using the GRADE system. RECOMMENDATIONS: The panel recommends EEG in generalized convulsive status epilepticus and to rule out nonconvulsive seizures in brain-injured patients and in comatose ICU patients without primary brain injury who have unexplained and persistent altered consciousness. We suggest EEG to detect ischemia in comatose patients with subarachnoid hemorrhage and to improve prognostication of coma after cardiac arrest. We recommend continuous over intermittent EEG for refractory status epilepticus and suggest it for patients with status epilepticus and suspected ongoing seizures and for comatose patients with unexplained and persistent altered consciousness. CONCLUSIONS: EEG monitoring is an important diagnostic tool for specific indications. Further data are necessary to understand its potential for ischemia assessment and coma prognostication