(66) Fractal analysis of locomotor activity data of geriatric in-patients with dementia
Date:
Contributors: Huber, S. E. , Canazei, M., Marksteiner, J., Mauracher, A., Pohl, W., Sachse, P., & Weiss, E.
Venue: 20. Herbstakademie – Synchronization in Embodied Interaction, Freiburg, Germany, February 28 - March 2, 2019
Abstract: Signals emerging from the highly-interacting coordinative functioning of multi-component systems with feedback give rise to fractal characteristics like scale-invariance or self-similarity (Kello et al., 2010). The control mechanisms underlying the regulation of human motor activity represent a pivotal example of such a system. Fractal scaling of motoric signals appears as a hallmark of (mental) health, indicating the ability of an organism to maintain optimal levels of adaptability and flexibility under variable external conditions (West, 2010). In contrast, changes of scale-invariant signal properties have been associated with impaired health and/or cognitive function (Nakamura et al., 2016; Li et al., 2018). Being grounded in a framework of interrelated mind, body and situated behavior, fractal analyses, which explore the mentioned signal characteristics, are conceptually central to situated embodied cognition research (Van Orden, Holden & Turvey, 2003). Here, we report results of a fractal analysis of locomotor activity data obtained by wrist-actigraphy from 42 geriatric in-patients diagnosed with dementia (Huber et al., in preparation). In particular, the properties of distributions of low-activity periods are assessed (Nakamura et al., 2016) and compared to results obtained with detrended fluctuation analysis (Li et al., 2018). We also discuss potentials and limitations of this method concerning diagnosis and monitoring of dementia (Huber et al., in preparation). Finally, we critically assess several methodological issues such as the dependence of the results on the used time resolution.
Literature
Huber, S. E., Sachse, P., Mauracher, A., Marksteiner, J., Pohl, W., Weiss, E. M., & Canazei, M. (2019). Assessment of Fractal Characteristics of Locomotor Activity of Geriatric In-Patients With Alzheimer’s Dementia. Frontiers in Aging Neuroscience, 11, 272. https://doi.org/10.3389/fnagi.2019.00272
Kello, C. T., Brown, G. D. A., Ferrer-i-Cancho, R., Holden, J. G., Linkenhaer-Hansen, K., Rhodes, T., & Van Orden, G. C. (2010). Scaling laws in cognitive sciences. Trends in Cognitive Sciences, 14(5). https://doi.org/10.1016/j.tics.2010.02.005
Li, P., Yu, L., Lim, A. S. P., Buchman, A. S., Scheer, F. A. J. L., Shea, S. A., Schneider, J. A., Bennett, D. A., Hu, K. (2018). Fractal regulation and incident Alzheimer’s disease in elderly individuals. Alzheimer’s & Dementia, 14(9), 1114-1125. https://doi.org/10.1016/j.jalz.2018.03.010
Nakamura, T., Kiyono, K., Wendt, H., Abry, P., & Yamamoto, Y. (2016). Multiscale Analysis of Intensive Longitudinal Biomedical Signals and Its Clinical Applications. Proceedings of the IEEE, 104(2), 242-261. https://doi.org/10.1109/JPROC.2015.2491979
Van Orden, G. C., Holden, J. G., & Turvey, M. T. (2003). Self-Organization of Cognitive Performance. Journal of Experimental Psychology, 132(3), 331-350. https://doi.org/10.1037/0096-3445.132.3.331
West, B. J. (2010). Fractal physiology and the fractional calculus: a perspective. Frontiers in Physiology, 1, 12. https://doi.org/10.3389/fphys.2010.00012