Non-rapid eye movement sleep instability in mild cognitive impairment: a pilot study

Maestri, Michelangelo; Carnicelli, Luca; Tognoni, Gloria; Coscio, Elisa Di; Giorgi, Filippo Sean; Volpi, L.; Economou, Nicholas-Tiberio; Ktonas, P.Y.; Ferri, Raffaele; Bonuccelli, Ubaldo; Bonanni, Enrica

doi:10.1016/j.sleep.2015.04.027

Cited by 68 publications

(61 citation statements)

References 53 publications

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“…In our study, total sleep time was decreased in both subgroups of patients with aMCI as compared with HE, but inter‐individual variability did not allow reaching statistical significance. We found that REM sleep was reduced in aMCI subjects compared with HE, in line with other previous papers (Liguori et al., ; Maestri et al., ), and even more reduced REM sleep among patients with AD has been shown. Even if the majority of the most recent papers focused on NREM sleep and cognition, an impairment of REM sleep in AD is well known (Moe, Vitiello, Larsen, & Prinz, ) and has been related to the dysfunction of cholinergic system, especially in the basal forebrain that was reported in AD pathology.…”

Section: Discussionsupporting

confidence: 93%

“…As concerns sleep microstructure, in this population a reduced CAP rate that reached statistical significance only for N2 and a reduction in A1 and A2 index was shown, in line with our previous observation (Maestri et al., ). It is known that CAP defines a sleep instability to be considered, within specific ranges, as a spontaneous and endogenous phenomenon.…”

Section: Discussionsupporting

confidence: 92%

“…Subjects with amnestic MCI (aMCI) were consecutively recruited among outpatients evaluated at the Cognitive Disorders Center of the Neurology Clinic of the University of Pisa for isolated memory complaints, and classified as aMCI subjects according to the diagnostic criteria in Petersen () after an extensive neuropsychological evaluation (see Maestri et al., for details). A detailed clinical evaluation, brain magnetic resonance imaging and routine laboratory tests (blood test for complete blood count, blood glucose, kidney and liver function panel, thyroid function, electrolyte panel, folic acid, vitamin B12, homocysteine, syphilis when clinically suspected) excluded focal encephalic disorders or other non‐degenerative major causes of cognitive impairment.…”

Section: Methodsmentioning

confidence: 99%

“…Analyses of sleep spindles, delta power (period‐amplitude and spectral analysis) and cyclic alternating pattern (CAP; Parrino, Ferri, Bruni, & Terzano, ) have been related to cognitive function in healthy subjects, in sleep‐disturbed subjects and in children with cognitive deficit (Aricò et al., ; Bruni et al., ). More recently, sleep has been related with cognition in neurodegenerative disorders; the analysis of CAP in a population of patients with MCI revealed a progressive reduction of sleep instability (CAP rate) and slow‐wave‐related arousals (CAP phases A1) in parallel with cognitive decline from cognitively healthy aging to dementia due to Alzheimer's disease (AD; Maestri et al., ). Slow‐wave activity appears critical both for sleep and cognition, and it may represent a potential link between sleep alteration and neurodegeneration (Mander, Winer, & Walker, ).…”

Section: Introductionmentioning

confidence: 99%

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A longitudinal study of polysomnographic variables in patients with mild cognitive impairment converting to Alzheimer's disease

Carnicelli

Maestri

Coscio

et al. 2019

Journal of Sleep Research

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The main condition at increased risk of dementia is considered to be mild cognitive impairment. Mild cognitive impairment has been defined as a transitional state between normal aging and dementia, of which it may represent a prodrome. The aim of our study was to evaluate whether sleep variables (both conventional and microstructural ones) in subjects with mild cognitive impairment correlate with conversion to dementia. Nineteen subjects with amnestic mild cognitive impairment (mean age 68.5 ± 7.0 years) and 11 cognitively intact healthy elderly individuals (mean age 69.2 ± 12.6 years) underwent ambulatory polysomnography for the evaluation of nocturnal sleep architecture and cyclic alternating pattern parameters. Amnestic mild cognitive impairment subjects were clinically and cognitively re‐evaluated after 2 years, during routine follow‐up, and further classified as amnestic mild cognitive impairment converters (that is, patients developing Alzheimer's disease, N = 11) and amnestic mild cognitive impairment non‐converters. Compared with healthy elderly individuals, amnestic mild cognitive impairment showed disrupted sleep with decreased rapid eye movement sleep, cyclic alternating pattern rate and cyclic alternating pattern slow‐wave‐related phases (A1 index). Standard sleep architecture analysis did not show significant differences between the two subgroups of amnestic mild cognitive impairment, whereas cyclic alternating pattern analysis showed that cyclic alternating pattern rate, A1 index and A3 index are significantly reduced in converters compared with non‐converters. Our data confirm that in amnestic mild cognitive impairment subjects there is a sleep impairment, particularly when considering more refined sleep parameters and that sleep variables at baseline are different among converters versus non‐converters at the 2‐year follow‐up. Specific sleep alterations might represent potential further biomarkers for the diagnosis and prognosis of early‐phase cognitive impairment.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionsupporting

confidence: 92%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A longitudinal study of polysomnographic variables in patients with mild cognitive impairment converting to Alzheimer's disease

Carnicelli

Maestri

Coscio

et al. 2019

Journal of Sleep Research

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“…For instance, the effect of ageing could include changes in sleep patterns. Sometimes people over 75 experience fragmentation of night-time sleep [7], whereas individuals with cognitive disorders could present problems related to the continuity of the sleep-awake cycle [8,9]. Other research demonstrates a close connection between brain function and the walking speed of older adults [10].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Machine Learning for Developing Personalised Behaviour Models Using Activity Data

Fiorini

Cavallo

Dario

et al. 2017

Sensors

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The goal of this study is to address two major issues that undermine the large scale deployment of smart home sensing solutions in people’s homes. These include the costs associated with having to install and maintain a large number of sensors, and the pragmatics of annotating numerous sensor data streams for activity classification. Our aim was therefore to propose a method to describe individual users’ behavioural patterns starting from unannotated data analysis of a minimal number of sensors and a ”blind” approach for activity recognition. The methodology included processing and analysing sensor data from 17 older adults living in community-based housing to extract activity information at different times of the day. The findings illustrate that 55 days of sensor data from a sensor configuration comprising three sensors, and extracting appropriate features including a “busyness” measure, are adequate to build robust models which can be used for clustering individuals based on their behaviour patterns with a high degree of accuracy (>85%). The obtained clusters can be used to describe individual behaviour over different times of the day. This approach suggests a scalable solution to support optimising the personalisation of care by utilising low-cost sensing and analysis. This approach could be used to track a person’s needs over time and fine-tune their care plan on an ongoing basis in a cost-effective manner.

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Rapid Eye Movement Sleep, Neurodegeneration, and Amyloid Deposition in Aging

André

Champetier

Rehel

et al. 2023

Annals of Neurology

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Rapid eye movement (REM) sleep is markedly altered in Alzheimer's disease (AD), and its reduction in older populations is associated with AD risk. However, little is known about the underlying brain mechanisms. Our objective was to investigate the relationships between REM sleep integrity and amyloid deposition, gray matter volume, and perfusion in aging. Methods: We included 121 cognitively unimpaired older adults (76 women, mean age 68.96 AE 3.82 years), who underwent a polysomnography, T1-weighted magnetic resonance imaging, early and late Florbetapir positron emission tomography scans to evaluate gray matter volume, perfusion, and amyloid deposition. We computed indices reflecting REM sleep macro-and microstructural integrity (ie, normalized electroencephalographic spectral power values). Voxelwise multiple regression analyses were conducted between REM sleep indices and neuroimaging data, controlling for age, sex, education, the apnea-hypopnea index, and the apolipoprotein E ε4 status. Results: Lower perfusion in frontal, anterior and posterior cingulate, and precuneus areas was associated with decreased delta power and electroencephalographic slowing (slow/fast frequencies ratio), and increased alpha and beta power. To a lower extent, similar results were obtained between gray matter volume and delta, alpha, and beta power. In addition, lower REM sleep theta power was more marginally associated with greater diffuse amyloid deposition and lower gray matter volume in fronto-temporal and parieto-occipital areas. Interpretation: These results suggest that alterations of REM sleep microstructure are associated with greater neurodegeneration and neocortical amyloid deposition in older adults. Further studies are warranted to replicate these findings, and determine whether older adults exhibiting REM sleep alterations are more at risk of cognitive decline and belonging to the Alzheimer's continuum.

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Non-rapid eye movement sleep instability in mild cognitive impairment: a pilot study

Cited by 68 publications

References 53 publications

A longitudinal study of polysomnographic variables in patients with mild cognitive impairment converting to Alzheimer's disease

A longitudinal study of polysomnographic variables in patients with mild cognitive impairment converting to Alzheimer's disease

Unsupervised Machine Learning for Developing Personalised Behaviour Models Using Activity Data

Rapid Eye Movement Sleep, Neurodegeneration, and Amyloid Deposition in Aging

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