ArticleClinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology2021
Night-to-night variability of sleep electroencephalography-based brain age measurements.
Article in Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Article
- Quantitative sleep EEG identifies CSF core biomarker-related subgroups in Alzheimer's disease.GeroScience · 2026Article
- Harvard Electroencephalography Database: A comprehensive clinical electroencephalographic resource from four Boston hospitals.Epilepsia · 2025Article
- Artificial Intelligence Can Drive Sleep Medicine.Sleep medicine clinics · 2025Review
- Article
- Effects of Aerobic Exercise on Brain Age and Health in Middle-Aged and Older Adults: A Single-Arm Pilot Clinical Trial.Life (Basel, Switzerland) · 2024Article
- Assessing Risk of Health Outcomes From Brain Activity in Sleep: A Retrospective Cohort Study.Neurology. Clinical practice · 2024Article
- Epilepsy is associated with the accelerated aging of brain activity in sleep.Frontiers in physiology · 2024Article
- Do try this at home: Age prediction from sleep and meditation with large-scale low-cost mobile EEG.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Decoding information about cognitive health from the brainwaves of sleep.Scientific reports · 2023Article
- How Machine Learning is Powering Neuroimaging to Improve Brain Health.Neuroinformatics · 2022Review
- Association of Sleep Electroencephalography-Based Brain Age Index With Dementia.JAMA network open · 2020Article
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Authors and funding
12 authors.
Funding
Abstract
objectiveBrain Age Index (BAI), calculated from sleep electroencephalography (EEG), has been proposed as a biomarker of brain health. This study quantifies night-to-night variability of BAI and establishes probability thresholds for inferring underlying brain pathology based on a patient's BAI.
methods86 patients with multiple nights of consecutive EEG recordings were selected from Epilepsy Monitoring Unit patients whose EEGs reported as within normal limits. While EEGs with epileptiform activity were excluded, the majority of patients included in the study had a diagnosis of chronic epilepsy. BAI was calculated for each 12-hour segment of patient data using a previously established algorithm, and the night-to-night variability in BAI was measured.
resultsThe within-patient night-to-night standard deviation in BAI was 7.5 years. Estimates of BAI derived by averaging over 2, 3, and 4 nights had standard deviations of 4.7, 3.7, and 3.0 years, respectively.
conclusionsAveraging BAI over n nights reduces night-to-night variability of BAI by a factor of n, rendering BAI a more suitable biomarker of brain health at the individual level. A brain age risk lookup table of results provides thresholds above which a patient has a high probability of excess BAI. SIGNIFICANCE: With increasing ease of EEG acquisition, including wearable technology, BAI has the potential to track brain health and detect deviations from normal physiologic function. The measure of night-to-night variability and how this is reduced by averaging across multiple nights provides a basis for using BAI in patients' homes to identify patients who should undergo further investigation or monitoring.
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