Sex differences in electrical activity of the brain during sleep: a systematic review of electroencephalographic findings across the human lifespan.

IF 2.9 4区 医学 Q3 ENGINEERING, BIOMEDICAL
Rhea Chapman, Sarasa Najima, Thaisa Tylinski Sant'Ana, Christy Chi Kiu Lee, Francesco Filice, Jessica Babineau, Tatyana Mollayeva
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引用次数: 0

Abstract

Background: With the explosion of techniques for recording electrical brain activity, our recognition of neurodiversity has expanded significantly. Yet, uncertainty exists regarding sex differences in electrical activity during sleep and whether these differences, if any, are associated with social parameters. We synthesised existing evidence applying the PROGRESS-Plus framework, which captures social parameters that may influence brain activity and function.

Methods: We searched five databases from inception to December 2024, and included English language peer-reviewed research examining sex differences in electrical activity during sleep in healthy participants. We performed risk of bias assessment following recommended criteria for observational studies. We reported results on sex differences by wave frequency (delta, theta, alpha, sigma, beta, and gamma) and waveforms (spindle and sawtooth), positioning results across age-related developmental stages. We created visualizations of results linking study quality and consideration of PROGRESS-Plus parameters, which facilitated certainty assessment.

Results: Of the 2,783 unique citations identified, 28 studies with a total of 3,374 participants (47% male, age range 4-5 months to 101 years) were included in data synthesis. Evidence of high certainty reported no sex differences in alpha and delta relative power among participants in middle-to-late adulthood. Findings of moderate certainty suggest no sex differences in alpha power; and theta, sigma and beta relative power; and delta density. There is evidence of moderate certainty suggesting that female participants had a steeper delta wave slope and male participants had greater normalized delta power. Evidence that female participants have higher spindle power density is of low certainty. All other findings were regarded as very low in certainty. The PROGRESS-Plus parameters were rarely integrated into the methodology of studies included in this review.

Conclusion: Evidence on the topic of sex differences in sleep wave parameters is variable. It is possible that the reported results reflect unmeasured social parameters, instead of biological sex. Future research on sex differences in sleep should be discussed in relevance to functional or clinical outcomes. Development of uniform testing procedures across research settings is timely.

Prospero: CRD42022327644.

Funding: Canada Research Chairs (Neurological Disorders and Brain Health, CRC-2021-00074); UK Pilot Award for Global Brain Health Leaders (GBHI ALZ UK-23-971123).

睡眠期间脑电活动的性别差异:对人类一生中脑电图发现的系统回顾。
背景:随着脑电活动记录技术的爆炸式发展,我们对神经多样性的认识已经显著扩大。然而,关于睡眠中脑电活动的性别差异,以及这些差异是否与社会参数有关(如果有的话),仍存在不确定性。我们运用PROGRESS-Plus框架综合了现有的证据,该框架捕捉了可能影响大脑活动和功能的社会参数。方法:我们检索了五个数据库,从建立到2024年12月,包括英语同行评议的研究,研究健康参与者睡眠时脑电活动的性别差异。我们按照观察性研究的推荐标准进行了偏倚风险评估。我们通过波频率(delta, theta, alpha, sigma, beta和gamma)和波形(纺锤形和锯齿形)报告了性别差异的结果,并在与年龄相关的发育阶段定位结果。我们创建了将研究质量与PROGRESS-Plus参数的考虑联系起来的结果可视化,从而促进了确定性评估。结果:在确定的2783条独特引用中,28项研究共3374名参与者(47%为男性,年龄范围4-5个月至101岁)被纳入数据合成。高确定性的证据表明,在成年中后期的参与者中,α和δ相对权力没有性别差异。中等确定性的研究结果表明,阿尔法权力没有性别差异;和相对功率;和密度。有中等确定性的证据表明,女性参与者有更陡峭的δ波斜率,而男性参与者有更大的标准化δ功率。女性参与者具有更高纺锤波功率密度的证据是低确定性的。其他所有发现的确定性都很低。PROGRESS-Plus参数很少被纳入本综述纳入的研究方法中。结论:关于睡眠波参数的性别差异的证据是可变的。报告的结果可能反映了未测量的社会参数,而不是生理性别。未来关于睡眠性别差异的研究应该在功能或临床结果方面进行讨论。制定跨研究机构的统一测试程序是及时的。普洛斯彼罗:CRD42022327644。资助:加拿大研究主席(神经疾病和大脑健康,CRC-2021-00074);英国全球脑健康领袖试点奖(GBHI ALZ UK-23-971123)。
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来源期刊
BioMedical Engineering OnLine
BioMedical Engineering OnLine 工程技术-工程:生物医学
CiteScore
6.70
自引率
2.60%
发文量
79
审稿时长
1 months
期刊介绍: BioMedical Engineering OnLine is an open access, peer-reviewed journal that is dedicated to publishing research in all areas of biomedical engineering. BioMedical Engineering OnLine is aimed at readers and authors throughout the world, with an interest in using tools of the physical and data sciences and techniques in engineering to understand and solve problems in the biological and medical sciences. Topical areas include, but are not limited to: Bioinformatics- Bioinstrumentation- Biomechanics- Biomedical Devices & Instrumentation- Biomedical Signal Processing- Healthcare Information Systems- Human Dynamics- Neural Engineering- Rehabilitation Engineering- Biomaterials- Biomedical Imaging & Image Processing- BioMEMS and On-Chip Devices- Bio-Micro/Nano Technologies- Biomolecular Engineering- Biosensors- Cardiovascular Systems Engineering- Cellular Engineering- Clinical Engineering- Computational Biology- Drug Delivery Technologies- Modeling Methodologies- Nanomaterials and Nanotechnology in Biomedicine- Respiratory Systems Engineering- Robotics in Medicine- Systems and Synthetic Biology- Systems Biology- Telemedicine/Smartphone Applications in Medicine- Therapeutic Systems, Devices and Technologies- Tissue Engineering
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