Neuroscience Research Center, Department of Medical and Surgical Sciences, University "Magna Graecia" of Catanzaro, Italy.
Institute of Neurology, University Magna Graecia of Catanzaro, Italy.
J Neurosci Methods. 2021 Mar 15;352:109084. doi: 10.1016/j.jneumeth.2021.109084. Epub 2021 Jan 25.
Resting-state-fMRI is a technique used to explore the functional brain architecture in term of brain networks and their interactions. However, the robustness of Resting-state-fMRI analysis is negatively affected by physiological noise caused by subject head motion. The aim of our study was to provide new knowledge about the effect of normal aging on the head motion signals.
For the first time, we proposed a method for evaluating the most sensitive head motion parameters linked to subjects'aging. We enrolled 14-young(9females; mean-age = 28 ± 4.07) and 14-elderly(9females; mean-age = 66 ± 5.19) subjects. Along three axes(X,Y,Z), we extracted six motions parameters which reflected the head's movements to characterize translations(x,y,z) and rotations(angles phi,theta,psi). We performed:1)univariate analysis for comparing the groups and correlation to investigate the relationship between age and movement parameters; 2)Support-Vector-Machine, using bootstrap and calculating the feature importance.
Statistical analyses showed significant association between the aging and some motion's parameters(rotation psi; translations y and z). These results were also confirmed by multivariate analysis with Support-Vector-Machine that presented an AUC of 90 %.
The proposed method shows that normal aging produces significant increase in head motion parameters, highlighting the critical effect of motion on resting data analyses in particular considering psi, y and z movements. To our knowledge and at the present, this represents the first study investigating the accurate characterization of motion parameters in aging.
Our results have a high impact to improve healthy control recruitment and appropriately decreasing the risk of signal distortion, according to the age of enrolled subjects.
静息态 fMRI 是一种用于探索脑网络及其相互作用的功能脑结构的技术。然而，由于受试者头部运动引起的生理噪声，静息态 fMRI 分析的稳健性受到负面影响。我们研究的目的是提供关于正常衰老对头部运动信号影响的新知识。
我们首次提出了一种评估与受试者年龄相关的最敏感头部运动参数的方法。我们招募了 14 名年轻人（9 名女性；平均年龄=28±4.07）和 14 名老年人（9 名女性；平均年龄=66±5.19）。我们沿着三个轴（X、Y、Z）提取了六个运动参数，这些参数反映了头部的运动，用于描述平移（x、y、z）和旋转（角度 phi、theta、psi）。我们进行了：1）单变量分析以比较组并进行相关性分析，以研究年龄与运动参数之间的关系；2）支持向量机，使用引导并计算特征重要性。
统计分析显示，衰老与某些运动参数（旋转 psi；平移 y 和 z）之间存在显著关联。多元分析支持向量机也证实了这一结果，其 AUC 为 90%。
所提出的方法表明，正常衰老会导致头部运动参数显著增加，这突出了运动对静息数据分析的关键影响，特别是考虑到 psi、y 和 z 的运动。据我们所知，目前这是第一项研究，该研究调查了衰老过程中运动参数的精确特征。
我们的研究结果具有很大的影响，可以根据受试者的年龄，改善健康对照招募，并适当降低信号失真的风险。
