Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.
Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States; Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, United States.
Neuroimage. 2021 Oct 1;239:118306. doi: 10.1016/j.neuroimage.2021.118306. Epub 2021 Jun 24.
Cerebrovascular reactivity (CVR), defined here as the Blood Oxygenation Level Dependent (BOLD) response to a CO pressure change, is a useful metric of cerebrovascular function. Both the amplitude and the timing (hemodynamic lag) of the CVR response can bring insight into the nature of a cerebrovascular pathology and aid in understanding noise confounds when using functional Magnetic Resonance Imaging (fMRI) to study neural activity. This research assessed a practical modification to a typical resting-state fMRI protocol, to improve the characterization of cerebrovascular function. In 9 healthy subjects, we modelled CVR and lag in three resting-state data segments, and in data segments which added a 2-3 minute breathing task to the start of a resting-state segment. Two different breathing tasks were used to induce fluctuations in arterial CO pressure: a breath-hold task to induce hypercapnia (CO increase) and a cued deep breathing task to induce hypocapnia (CO decrease). Our analysis produced voxel-wise estimates of the amplitude (CVR) and timing (lag) of the BOLD-fMRI response to CO by systematically shifting the CO regressor in time to optimize the model fit. This optimization inherently increases gray matter CVR values and fit statistics. The inclusion of a simple breathing task, compared to a resting-state scan only, increases the number of voxels in the brain that have a significant relationship between CO and BOLD-fMRI signals, and improves our confidence in the plausibility of voxel-wise CVR and hemodynamic lag estimates. We demonstrate the clinical utility and feasibility of this protocol in an incidental finding of Moyamoya disease, and explore the possibilities and challenges of using this protocol in younger populations. This hybrid protocol has direct applications for CVR mapping in both research and clinical settings and wider applications for fMRI denoising and interpretation.
脑血流反应性(CVR),这里定义为血氧水平依赖(BOLD)对 CO 压力变化的响应,是评估脑血管功能的有用指标。CVR 响应的幅度和时滞(血流动力学滞后)都可以深入了解脑血管病理的性质,并有助于在使用功能磁共振成像(fMRI)研究神经活动时理解噪声混杂。本研究评估了对典型静息态 fMRI 方案的实用改进,以改善对脑血管功能的描述。在 9 名健康受试者中,我们在三个静息态数据段中建模 CVR 和滞后,以及在数据段中,在静息态段开始时添加 2-3 分钟的呼吸任务。使用两种不同的呼吸任务来诱导动脉 CO 压力波动:屏气任务诱导高碳酸血症(CO 增加)和提示深呼吸任务诱导低碳酸血症(CO 减少)。我们的分析通过系统地在时间上移动 CO 回归量来优化模型拟合,从而产生了 CO 对 BOLD-fMRI 响应的幅度(CVR)和时间(滞后)的体素估计。这种优化本质上会增加灰质 CVR 值和拟合统计量。与仅进行静息态扫描相比,包含简单的呼吸任务会增加大脑中具有 CO 和 BOLD-fMRI 信号之间显著关系的体素数量,并提高我们对体素 CVR 和血流动力学滞后估计的可信度。我们在偶然发现的烟雾病中展示了该方案的临床实用性和可行性,并探讨了在年轻人群中使用该方案的可能性和挑战。这种混合方案在研究和临床环境中的 CVR 映射以及 fMRI 去噪和解释的更广泛应用中具有直接的应用价值。