Department of Radiological and Medical Laboratory Sciences, Nagoya University Graduate School of Medicine.
Brain & Mind Research Center, Nagoya University.
Magn Reson Med Sci. 2021 Dec 1;20(4):338-346. doi: 10.2463/mrms.mp.2020-0081. Epub 2020 Oct 27.
The estimation of functional connectivity (FC) measures using resting state functional MRI (fMRI) is often affected by head motion during functional imaging scans. Head motion is more common in the elderly than in young participants and could therefore affect the evaluation of age-related changes in brain networks. Thus, this study aimed to investigate the influence of head motion in FC estimation when evaluating age-related changes in brain networks.
This study involved 132 healthy volunteers divided into 3 groups: elderly participants with high motion (OldHM, mean age (±SD) = 69.6 (±5.31), N = 44), elderly participants with low motion (OldLM, mean age (±SD) = 68.7 (±4.59), N = 43), and young adult participants with low motion (YugLM, mean age (±SD) = 27.6 (±5.26), N = 45). Head motion was quantified using the mean of the framewise displacement of resting state fMRI data. After preprocessing all resting state fMRI datasets, several resting state networks (RSNs) were extracted using independent component analysis (ICA). In addition, several network metrics were also calculated using network analysis. These FC measures were then compared among the 3 groups.
In ICA, the number of voxels with significant differences in RSNs was higher in YugLM vs. OldLM comparison than in YugLM vs. OldHM. In network analysis, all network metrics showed significant (P < 0.05) differences in comparisons involving low vs. high motion groups (OldHM vs. OldLM and OldHM vs. YugLM). However, there was no significant (P > 0.05) difference in the comparison involving the low motion groups (OldLM vs. YugLM).
Our findings showed that head motion during functional imaging could significantly affect the evaluation of age-related brain network changes using resting state fMRI data.
使用静息态功能磁共振成像（fMRI）估计功能连接（FC）测量值通常会受到功能成像扫描期间头部运动的影响。与年轻参与者相比，老年人头部运动更为常见，因此可能会影响大脑网络与年龄相关变化的评估。因此，本研究旨在调查在评估大脑网络与年龄相关变化时，头部运动对 FC 估计的影响。
本研究涉及 132 名健康志愿者，分为 3 组：高头部运动的老年参与者（OldHM，平均年龄（±标准差）= 69.6（±5.31），N = 44）、低头部运动的老年参与者（OldLM，平均年龄（±标准差）= 68.7（±4.59），N = 43）和低头部运动的年轻成年参与者（YugLM，平均年龄（±标准差）= 27.6（±5.26），N = 45）。使用静息态 fMRI 数据的帧位移平均值来量化头部运动。对所有静息态 fMRI 数据集进行预处理后，使用独立成分分析（ICA）提取多个静息态网络（RSN）。此外，还使用网络分析计算了多个网络指标。然后在 3 组之间比较这些 FC 测量值。
在 ICA 中，与 YugLM 与 OldLM 比较相比，YugLM 与 OldHM 比较中 RSN 差异具有显著差异的体素数量更高。在网络分析中，所有网络指标在涉及低与高运动组的比较中（OldHM 与 OldLM 和 OldHM 与 YugLM）均显示出显著（P < 0.05）差异。然而，在涉及低运动组的比较中（OldLM 与 YugLM），没有显著（P > 0.05）差异。
我们的研究结果表明，功能成像期间的头部运动会显著影响使用静息态 fMRI 数据评估与年龄相关的大脑网络变化。
