Aims Elevated blood pressure (BP) is a prevalent modifiable risk factor for cardiovascular diseases and contributes to cognitive decline in late life. Despite the fact that functional changes may precede irreversible structural damage and emerge in an ongoing manner, studies have been predominantly informed by brain structure and group-level inferences. Here, we aim to delineate neurobiological correlates of BP at an individual level using machine learning and functional connectivity. Methods and results Based on whole-brain functional connectivity from the UK Biobank, we built a machine learning model to identify neural representations for individuals’ past (∼8.9 years before scanning, N = 35 882), current (N = 31 367), and future (∼2.4 years follow-up, N = 3 138) BP levels within a repeated cross-validation framework. We examined the impact of multiple potential covariates, as well as assessed these models’ generalizability across various contexts. The predictive models achieved significant correlations between predicted and actual systolic/diastolic BP and pulse pressure while controlling for multiple confounders. Predictions for participants not on antihypertensive medication were more accurate than for currently medicated patients. Moreover, the models demonstrated robust generalizability across contexts in terms of ethnicities, imaging centres, medication status, participant visits, gender, age, and body mass index. The identified connectivity patterns primarily involved the cerebellum, prefrontal, anterior insula, anterior cingulate cortex, supramarginal gyrus, and precuneus, which are key regions of the central autonomic network, and involved in cognition processing and susceptible to neurodegeneration in Alzheimer’s disease. Results also showed more involvement of default mode and frontoparietal networks in predicting future BP levels and in medicated participants. Conclusion This study, based on the largest neuroimaging sample currently available and using machine learning, identifies brain signatures underlying BP, providing evidence for meaningful BP-associated neural representations in connectivity profiles.

中文翻译：

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英国生物银行 >30 000 名参与者的血压功能连接组特征


血压升高 (BP) 是心血管疾病的一个常见的可改变危险因素，会导致晚年认知能力下降。尽管功能变化可能先于不可逆的结构损伤并以持续的方式出现，但研究主要是根据大脑结构和群体层面的推论得出的。在这里，我们的目标是利用机器学习和功能连接在个体水平上描述血压的神经生物学相关性。方法和结果基于英国生物银行的全脑功能连接，我们建立了一个机器学习模型来识别个体过去（扫描前~8.9年，N = 35 882）、当前（N = 31 367）、以及重复交叉验证框架内的未来（∼2.4 年随访，N = 3 138）血压水平。我们检查了多个潜在协变量的影响，并评估了这些模型在不同背景下的普遍性。预测模型在控制多个混杂因素的同时，实现了预测和实际收缩压/舒张压和脉压之间的显着相关性。对未服用抗高血压药物的参与者的预测比目前正在服用药物的患者更准确。此外，这些模型在种族、成像中心、药物状况、参与者就诊、性别、年龄和体重指数等方面表现出强大的普遍性。已确定的连接模式主要涉及小脑、前额叶、前岛叶、前扣带皮层、边缘上回和楔前叶，这些区域是中枢自主网络的关键区域，参与认知处理并易受阿尔茨海默氏病神经退行性变的影响。 结果还表明，默认模式和额顶叶网络更多地参与预测未来血压水平和接受药物治疗的参与者。结论 这项研究基于目前可用的最大的神经影像样本并使用机器学习，识别了 BP 背后的大脑特征，为连通性概况中与 BP 相关的有意义的神经表征提供了证据。