女性生理周期与脑功能连通性的内在动态关系及其对压力、生活满意度和社会困扰的影响

Ishani Gupta
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引用次数: 0

摘要

研究报告称,雌激素和黄体酮等性激素的波动会导致月经周期和月经期(月经期、卵泡期和黄体期)大脑不同区域的神经系统变化。虽然也有一些关于女性月经周期的心理变化的报道,但从神经心理学的角度来看,还没有足够的研究来探讨这个话题。出于这个原因,我正在研究32天月经周期中大脑连接的波动及其对女性生活满意度(LS)、感知压力(PS)和情感支持(ES)的影响。在这项研究中,我使用的数据(n=406)是由Human Connectome Project收集的。我正在使用一种实验研究方法,观察在32天的月经周期中,大脑区域与月经周期阶段(月经、卵泡和黄体)相关变量之间的相关性(连通性),以及这告诉我们参与者的LS、PS和ES总分。在神经科学领域,人工智能(AI)技术被用于理解复杂的大脑功能、大脑和行为之间的关系,并显示出诊断神经系统疾病和干预措施的潜力。人工智能和弹性网络回归,这是一种神经成像数据的统计技术,将被用来分析我正在研究的大脑活动关系。在我的演讲中,我的目的是讨论大脑区域的功能连接是否与女性的LS, PS和ES有关,以及这对月经健康的影响。这项试点研究通过使用相对健康的参与者的大数据集来进一步了解这一主题,将有助于为未来的研究打开大门,以类似的调查目标为更有影响的月经相关健康状况,如经前综合征和经前烦躁不安障碍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intrinsic Dynamics of Brain Functional Connectivity in Relation to Women’s Menstrual Cyclic Phases and its Impact on Their Stress, Life Satisfaction, and Social Distress
Research has reported that the fluctuations of sex hormones like estrogen and progesterone lead to neurological changes in the different brain regions across the menstrual cycle and its phases: menses, follicular, and luteal. Although some psychological changes have also been reported for women across their menstrual cycle, not enough studies have approached this topic from a neuropsychological perspective. For this reason, I am investigating brain connectivity fluctuations across a 32-day menstruation cycle and its impact on women’s life satisfaction (LS), perceived stress (PS), and emotional support (ES). For this study, I am using the data (n=406) that has been collected by the Human Connectome Project. I am using an experimental research methodology looking at correlations (connectivity) between brain regions and associated variables of menstrual cycle phases (menses, follicular, and luteal) across a 32-day menstrual cycle, and what this tells us about the participants’ LS, PS, and ES total scores. In neuroscience, artificial intelligence (AI) techniques are applied to understand complex brain functions, brain and behaviour relationships, and shows potential to diagnose neurological disorders and interventions. AI, and elastic net regression, which is a type of statistical technique for neuroimaging data, will be used to analyse the brain activity relationships that I am investigating. In my presentation, I aim to discuss whether functional connectivity in brain regions is linked to women’s LS, PS, and ES, and the implications of this for menstrual health. This pilot study, by using a big data set of relatively healthy participants to further our understanding of this topic, will help to open doors for future research with similar investigating aims for more impactful menstrual related health conditions like Premenstrual Syndrome and Premenstrual Dysphoric Disorder.
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