Consolidation makes it possible for memories of our daily experiences to be stored in an enduring way. We propose that memory consolidation depends on the covert reactivation of previously learned material both during sleep and wakefulness. Here we tested whether the operation of covert memory reactivation influences the fundamental selectivity of memory storage—of all the events we experience each day, which will be retained and which forgotten? We systematically manipulated the value of information learned by 60 young subjects; they learned 72 object-location associations while hearing characteristic object sounds, and a number on each object indicated the reward value that could potentially be earned during a future memory test. Recall accuracy declined to a greater extent for low-value than for high-value associations after either a 90 min nap or a 90 min wake interval. Yet, via targeted memory reactivation of half of the low-value associations using the corresponding sounds, these memories were rescued from forgetting. Only cued associations were rescued when sounds were applied during wakefulness, whereas the entire set of low-value associations was rescued from forgetting when the manipulation occurred during sleep. The benefits accrued from presenting corresponding sounds show that covert reactivation is a major factor determining the selectivity of memory consolidation in these circumstances. By extension, covert reactivation may determine the ultimate fate of our memories, though wake and sleep reactivation might play distinct roles in this process, the former helping to strengthen individual, salient memories, and the latter strengthening, while also linking, categorically related memories together.
These findings substantiate the use of targeted memory reactivation (TMR) methods for manipulating consolidation during sleep. TMR can selectively strengthen memory storage for object-location associations learned prior to sleep, except for those near-perfectly memorized. Neural measures found in conjunction with TMR-induced strengthening provide additional evidence about mechanisms of sleep consolidation.
Background: Sleep disorders including insomnia, movements during sleep, and daytime sleepiness are common but poorly studied in Huntington disease (HD).Objective: To evaluate the HD sleep-wake phenotype (including abnormal motor activity during sleep) in patients with various HD stages and the length of CAG repeats. Because a mild hypocretin deficiency has been found in the brains of some patients with HD (hereinafter referred to as HD patients), we also tested the HD patients for narcolepsy. Design and Patients:Twenty-five HD patients (including 2 premanifest carriers) underwent clinical interview, nighttime video and sleep monitoring, and daytime multiple sleep latency tests. Their results were compared with those of patients with narcolepsy and control patients. Results: The HD patients had frequent insomnia, earlier sleep onset, lower sleep efficiency, increased stage 1 sleep, delayed and shortened rapid eye movement (REM) sleep, and increased periodic leg movements. Three HD patients (12%) had REM sleep behavior disorders. No sleep abnormality correlated with CAG repeat length. Reduced REM sleep duration (but not REM sleep behavior disorders) was present in premanifest carriers and patients with very mild HD and worsened with disease severity. In contrast to narcoleptic patients, HD patients had no cataplexy, hypnagogic hallucinations, or sleep paralysis. Four HD patients had abnormally low (Ͻ8 minutes) daytime sleep latencies, but none had multiple sleeponset REM periods. Conclusions: The sleep phenotype of HD includes insomnia, advanced sleep phase, periodic leg movements, REM sleep behavior disorders, and reduced REM sleep but not narcolepsy. Reduced REM sleep may precede chorea. Mutant huntingtin may exert an effect on REM sleep and motor control during sleep.
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.
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