Recent functional magnetic resonance imaging (fMRI) studies report that moment-to-moment variability in the BOLD signal is related to differences in age and cognition and, thus, may be sensitive to age-dependent decline. However, head motion and/or cardiovascular health (CVH) may contaminate these relationships. We evaluated relationships between resting-state BOLD variability, age, and cognition, after characterizing and controlling for motion-related and cardiovascular influences, including pulse, blood pressure, BMI, and white matter hyperintensities (WMH), in a large (N = 422) resting-state fMRI sample of cognitively normal individuals (age 43–89). We found that resting-state BOLD variability was negatively related to age and positively related to cognition after maximally controlling for head motion. Age relationships also survived correction for CVH, but were greatly reduced when correcting for WMH alone. Our results suggest that network-based machine learning analyses of resting-state BOLD variability might yield reliable, sensitive measures to characterize age-related decline across a broad range of networks. Age-related differences in resting-state BOLD variability may be largely sensitive to processes related to WMH burden.

中文翻译：

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在控制运动和心血管影响后评估静息状态 BOLD 变异对年龄和认知的敏感性：一种基于网络的方法。

最近的功能性磁共振成像 (fMRI) 研究报告说，BOLD 信号的瞬时变化与年龄和认知的差异有关，因此可能对年龄相关的衰退很敏感。然而，头部运动和/或心血管健康 (CVH) 可能会污染这些关系。在对运动相关和心血管影响（包括脉搏、血压、BMI 和白质高信号 (WMH)）进行表征和控制后，我们评估了静息状态 BOLD 变异性、年龄和认知之间的关系（N = 422) 认知正常个体（43-89 岁）的静息状态 fMRI 样本。我们发现，在最大限度地控制头部运动后，静息状态 BOLD 变异性与年龄呈负相关，与认知呈正相关。年龄关系在 CVH 校正后也幸免于难，但在单独校正 WMH 时大大降低。我们的结果表明，基于网络的机器学习对静息状态 BOLD 变异性的分析可能会产生可靠、敏感的措施，以表征各种网络中与年龄相关的衰退。静息状态 BOLD 变异性的年龄相关差异可能在很大程度上对与 WMH 负担相关的过程敏感。