Nordbeck Patric C, Andrade Valéria, Silva Paula L, Kuznetsov Nikita A
Department of Psychology, Lund University, Lund, Sweden.
Center for Cognition, Action, and Perception, Department of Psychology, University of Cincinnati, Cincinnati, OH, United States.
Front Netw Physiol. 2023 Aug 7;3:1233894. doi: 10.3389/fnetp.2023.1233894. eCollection 2023.
Detrended Fluctuation Analysis (DFA) has been used to investigate self-similarity in center of pressure (CoP) time series. For fractional gaussian noise (fGn) signals, the analysis returns a scaling exponent, DFA-α, whose value characterizes the temporal correlations as persistent, random, or anti-persistent. In the study of postural control, DFA has revealed two time scaling regions, one at the short-term and one at the long-term scaling regions in the diffusion plots, suggesting different types of postural dynamics. Much attention has been given to the selection of minimum and maximum scales, but the choice of spacing (step size) between the window sizes at which the fluctuation function is evaluated may also affect the estimates of scaling exponents. The aim of this study is twofold. First, to determine whether DFA can reveal postural adjustments supporting performance of an upper limb task under variable demands. Second, to compare evenly-spaced DFA with two different step sizes, 0.5 and 1.0 in log units, applied to CoP time series. We analyzed time series of anterior-posterior (AP) and medial-lateral (ML) CoP displacement from healthy participants performing a sequential upper limb task under variable demand. DFA diffusion plots revealed two scaling regions in the AP and ML CoP time series. The short-term scaling region generally showed hyper-diffusive dynamics and long-term scaling revealed mildly persistent dynamics in the ML direction and random-like dynamics in the AP direction. There was a systematic tendency for higher estimates of DFA-α and lower estimates for crossover points for the 0.5-unit step size vs. 1.0-unit size. Results provide evidence that DFA-α captures task-related differences between postural adjustments in the AP and ML directions. Results also showed that DFA-α estimates and crossover points are sensitive to step size. A step size of 0.5 led to less variable DFA-α for the long-term scaling region, higher estimation for the short-term scaling region, lower estimate for crossover points, and revealed anomalous estimates at the very short range that had implications for choice of minimum window size. We, therefore, recommend the use of 0.5 step size in evenly spaced DFAs for CoP time series similar to ours.
去趋势波动分析（DFA）已被用于研究压力中心（CoP）时间序列中的自相似性。对于分数高斯噪声（fGn）信号，该分析会返回一个标度指数，即DFA-α，其值将时间相关性表征为持续性、随机性或反持续性。在姿势控制研究中，DFA在扩散图中揭示了两个时间标度区域，一个在短期，一个在长期标度区域，这表明存在不同类型的姿势动力学。人们对最小和最大标度的选择给予了很多关注，但在评估波动函数时窗口大小之间的间距（步长）选择也可能影响标度指数的估计。本研究的目的有两个。第一，确定DFA是否能揭示在可变需求下支持上肢任务表现的姿势调整。第二，将等间距DFA与应用于CoP时间序列的两种不同步长（对数单位下为0.5和1.0）进行比较。我们分析了健康参与者在可变需求下执行连续上肢任务时前后（AP）和内外侧（ML）CoP位移的时间序列。DFA扩散图在AP和ML CoP时间序列中揭示了两个标度区域。短期标度区域通常显示超扩散动力学，长期标度显示在ML方向上有轻度持续性动力学，在AP方向上有类似随机的动力学。与1.0单位大小相比，0.5单位步长的DFA-α估计值更高，交叉点估计值更低，存在系统趋势。结果提供了证据表明DFA-α捕捉了AP和ML方向姿势调整之间与任务相关的差异。结果还表明，DFA-α估计值和交叉点对步长敏感。步长为0.5时，长期标度区域的DFA-α变化较小，短期标度区域的估计值较高，交叉点的估计值较低，并且在非常短的范围内显示出异常估计，这对最小窗口大小的选择有影响。因此，对于与我们的CoP时间序列类似的情况，我们建议在等间距DFA中使用0.5的步长。
