Búzás András, Makai András, Groma Géza I, Dancsházy Zsolt, Szendi István, Kish Laszlo B, Santa-Maria Ana Raquel, Dér András
Institute of Biophysics, HUN-REN Biological Research Centre, Temesvári Krt. 62, P.O.B. 521, Szeged, 6701, Hungary.
Department of Psychiatry, Kiskunhalas Semmelweis Hospital, 1 Dr. Monszpart László Street, Kiskunhalas, 6400, Hungary.
Sci Rep. 2024 Mar 12;14(1):5981. doi: 10.1038/s41598-024-56185-0.
Human physical activity (HPA), a fundamental physiological signal characteristic of bodily motion is of rapidly growing interest in multidisciplinary research. Here we report the existence of hitherto unidentified hierarchical levels in the temporal organization of HPA on the ultradian scale: on the minute's scale, passive periods are followed by activity bursts of similar intensity ('quanta') that are organized into superstructures on the hours- and on the daily scale. The time course of HPA can be considered a stochastic, quasi-binary process, where quanta, assigned to task-oriented actions are organized into work packages on higher levels of hierarchy. In order to grasp the essence of this complex dynamic behaviour, we established a stochastic mathematical model which could reproduce the main statistical features of real activity time series. The results are expected to provide important data for developing novel behavioural models and advancing the diagnostics of neurological or psychiatric diseases.
人类身体活动(HPA)是身体运动的一种基本生理信号特征,在多学科研究中受到越来越多的关注。在此,我们报告了在超日尺度上,HPA时间组织中迄今未被识别的层次结构的存在:在分钟尺度上,被动期之后是强度相似的活动爆发(“量子”),这些活动爆发在小时和日尺度上被组织成上层结构。HPA的时间进程可以被视为一个随机的、准二元过程,其中分配给面向任务行动的量子在更高层次的层次结构中被组织成工作包。为了理解这种复杂动态行为的本质,我们建立了一个随机数学模型,该模型可以再现真实活动时间序列的主要统计特征。这些结果有望为开发新的行为模型和推进神经或精神疾病的诊断提供重要数据。