Comparison of polysomnography in people with Alzheimer's disease and insomnia versus non-demented elderly people with insomnia

SLEEP DISORDERS AND RISK OF ALZHEIMER'S DISEASE: A TWO-WAY ROAD

Substantial sleep impairment in patients with Alzheimer's disease (AD) is one of the emerging points for continued efforts to better understand the disease. Individuals without cognitive decline, an important marker of the clinical phase of AD, may show early alterations in the sleep-wake cycle. The objective of this critical narrative review is to explore the bidirectional pathophysiological correlation between sleep disturbances and Alzheimer's Disease. Specifically, it examines how the disruption of sleep homeostasis in individuals without dementia could contribute to the pathogenesis of AD, and conversely, how neurodegeneration in individuals with Alzheimer's Disease might lead to dysregulation of the sleep-wake cycle. Recent scientific results indicate that sleep disturbances, particularly those related to impaired glymphatic clearance, may act as an important mechanism associated with the genesis of Alzheimer's Disease. Additionally, amyloid deposition and tau protein hyperphosphorylation, along with astrocytic hyperactivation, appear to trigger changes in neurotransmission dynamics in areas related to sleep, which may explain the onset of sleep disturbances in individuals with AD. Disruption of sleep homeostasis appears to be a modifiable risk factor in Alzheimer's disease. Whenever possible, the use of non-pharmacological strategies becomes important in this context. From a different perspective, additional research is needed to understand and treat the dysfunction of the sleep-wake cycle in individuals already affected by AD. Early recognition and correction of sleep disturbances in this population could potentially mitigate the progression of dementia and improve the quality of life for those with AD.

Sleep spindles are reduced in children with Down syndrome and sleep-disordered breathing

BACKGROUND

Children with Down syndrome (DS) are at increased risk of sleep-disordered breathing (SDB). We investigated sleep spindle activity, as a marker of sleep quality, and its relationship with daytime functioning in children with DS compared to typically developing (TD) children.

METHODS

Children with DS and SDB (n = 44) and TD children matched for age, sex and SDB severity underwent overnight polysomnography. Fast or Slow sleep spindles were identified manually during N2/N3 sleep. Spindle activity was characterized as spindle number, density (number of spindles/h) and intensity (density × average duration) on central (C) and frontal (F) electrodes. Parents completed the Child Behavior Check List and OSA-18 questionnaires.

RESULTS

In children with DS, spindle activity was lower compared to TD children for F Slow and F Slow&Fast spindles combined (p < 0.001 for all). Furthermore, there were no correlations between spindle activity and CBCL subscales; however, spindle activity for C Fast and C Slow&Fast was negatively correlated with OSA-18 emotional symptoms and caregiver concerns and C Fast activity was also negatively correlated with daytime function and total problems.

CONCLUSIONS

Reduced spindle activity in children with DS may underpin the increased sleep disruption and negative effects of SDB on quality of life and behavior.

IMPACT

Children with Down syndrome (DS) are at increased risk of sleep-disordered breathing (SDB), which is associated with sleep disruption affecting daytime functioning. Sleep spindles are a sensitive marker of sleep quality. We identified for the first time that children with DS had reduced sleep spindle activity compared to typically developing children matched for SDB severity. The reduced spindle activity likely underpins the more disrupted sleep and may be associated with reduced daytime functioning and quality of life and may also be an early biomarker for an increased risk of developing dementia later in life in children with DS.

Orexin Receptor Antagonism: Normalizing Sleep Architecture in Old Age and Disease

Sleep is essential for human well-being, yet the quality and quantity of sleep reduce as age advances. Older persons (>65 years old) are more at risk of disorders accompanied and/or exacerbated by poor sleep. Furthermore, evidence supports a bidirectional relationship between disrupted sleep and Alzheimer's disease (AD) or related dementias. Orexin/hypocretin neuropeptides stabilize wakefulness, and several orexin receptor antagonists (ORAs) are approved for the treatment of insomnia in adults. Dysregulation of the orexin system occurs in aging and AD, positioning ORAs as advantageous for these populations. Indeed, several clinical studies indicate that ORAs are efficacious hypnotics in older persons and dementia patients and, as in adults, are generally well tolerated. ORAs are likely to be more effective when administered early in sleep/wake dysregulation to reestablish good sleep/wake-related behaviors and reduce the accumulation of dementia-associated proteinopathic substrates. Improving sleep in aging and dementia represents a tremendous opportunity to benefit patients, caregivers, and health systems.

Electroencephalography-Based Depression Detection Using Multiple Machine Learning Techniques

The growth of biomedical engineering has made depression diagnosis via electroencephalography (EEG) a trendy issue. The two significant challenges to this application are EEG signals' complexity and non-stationarity. Additionally, the effects caused by individual variances may hamper the generalization of detection systems. Given the association between EEG signals and particular demographics, such as gender and age, and the influences of these demographic characteristics on the incidence of depression, it would be preferable to include demographic factors during EEG modeling and depression detection. The main objective of this work is to develop an algorithm that can recognize depression patterns by studying EEG data. Following a multiband analysis of such signals, machine learning and deep learning techniques were used to detect depression patients automatically. EEG signal data are collected from the multi-modal open dataset MODMA and employed in studying mental diseases. The EEG dataset contains information from a traditional 128-electrode elastic cap and a cutting-edge wearable 3-electrode EEG collector for widespread applications. In this project, resting EEG readings of 128 channels are considered. According to CNN, training with 25 epoch iterations had a 97% accuracy rate. The patient's status has to be divided into two basic categories: major depressive disorder (MDD) and healthy control. Additional MDD include the following six classes: obsessive-compulsive disorders, addiction disorders, conditions brought on by trauma and stress, mood disorders, schizophrenia, and the anxiety disorders discussed in this paper are a few examples of mental illnesses. According to the study, a natural combination of EEG signals and demographic data is promising for the diagnosis of depression.