Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA.
Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Emory University and Georgia State University, Atlanta, GA 30303, USA.
Cardiovasc Res. 2023 Jun 13;119(6):1427-1440. doi: 10.1093/cvr/cvac116.
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.
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.
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.
高血压（BP）是心血管疾病的常见可改变危险因素，也是导致晚年认知能力下降的因素。尽管功能变化可能先于不可逆转的结构损伤，并以持续的方式出现，但这些研究主要是基于大脑结构和群体水平的推断。在这里，我们旨在使用机器学习和功能连接来描绘个体水平的 BP 的神经生物学相关性。
基于英国生物库的全脑功能连接，我们构建了一个机器学习模型，以识别个体过去（扫描前约 8.9 年，N = 35882）、当前（N = 31367）和未来（约 2.4 年随访，N = 3138）BP 水平的神经表示。我们检查了多个潜在协变量的影响，并评估了这些模型在各种情况下的通用性。在控制了多个混杂因素后，预测模型在预测收缩压/舒张压和脉压方面与实际值之间达到了显著相关性。对于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、药物状态、参与者就诊、性别、年龄和体重指数等方面表现出了跨多种情况的稳健通用性。所确定的连接模式主要涉及小脑、前额叶、前岛叶、前扣带皮层、缘上回和楔前叶，这些是中央自主神经网络的关键区域，与认知处理有关，并且易患阿尔茨海默病中的神经退行性变。结果还表明，默认模式和额顶叶网络在预测未来 BP 水平和在服用药物的参与者中更多地参与。
这项研究基于目前最大的神经影像学样本，并使用机器学习，确定了 BP 背后的大脑特征，为连接谱中与 BP 相关的有意义的神经表示提供了证据。
