The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Network Physiology: How Organ Systems Dynamically Interact
Open accessAbstract
We systematically study how diverse physiologic systems in the human organism dynamically interact and collectively behave to produce distinct physiologic states and functions. This is a fundamental question in the new interdisciplinary field of Network Physiology, and has not been previously explored. Introducing the novel concept of Time Delay Stability (TDS), we develop a computational approach to identify and quantify networks of physiologic interactions from long-term continuous, multi-channel physiological recordings. We also develop a physiologically-motivated visualization framework to map networks of dynamical organ interactions to graphical objects encoded with information about the coupling strength of network links quantified using the TDS measure. Applying a system-wide integrative approach, we identify distinct patterns in the network structure of organ interactions, as well as the frequency bands through which these interactions are mediated. We establish first maps representing physiologic organ network interactions and discover basic rules underlying the complex hierarchical reorganization in physiologic networks with transitions across physiologic states. Our findings demonstrate a direct association between network topology and physiologic function, and provide new insights into understanding how health and distinct physiologic states emerge from networked interactions among nonlinear multi-component complex systems. The presented here investigations are initial steps in building a first atlas of dynamic interactions among organ systems.
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Cites
Network physiology reveals relations between network topology and physiological function
Bashan, A., Bartsch, R. P., Kantelhardt, J. W., Havlin, S., & Ivanov, P. C. (2012) · Nature Communications · doi:10.1038/ncomms1705 · Cited by 721
Self-citationCorrelated and Uncorrelated Regions in Heart-Rate Fluctuations during Sleep
Bunde, A., Havlin, S., Kantelhardt, J. W., Penzel, T., Peter, J.-H., & Voigt, K. (2000) · Physical Review Letters · doi:10.1103/physrevlett.85.3736 · Cited by 543
Networks of Networks: The Last Frontier of Complexity
(2014) · Understanding Complex Systems · doi:10.1007/978-3-319-03518-5 · Cited by 293
Ivanov, P. C., Bunde, A., Amaral, L. A. N., Havlin, S., Fritsch-Yelle, J., Baevsky, R. M., Stanley, H. E., & Goldberger, A. L. (1999) · Europhysics Letters (EPL) · doi:10.1209/epl/i1999-00525-0 · Cited by 239
Self-citationIdentifying and Modeling Delay Feedback Systems
Hegger, R., Bünner, M. J., Kantz, H., et al. (1998) · Physical Review Letters · doi:10.1103/physrevlett.81.558 · Cited by 205
The SIESTA project polygraphic and clinical database
Klosh, G., Kemp, B., Penzel, T., Schlogl, A., Rappelsberger, P., Trenker, E., Gruber, G., Zeithofer, J., Saletu, B., Herrmann, W. M., et al. (2001) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/51.932725 · Cited by 187
Characterization of sleep stages by correlations in the magnitude and sign of heartbeat increments
Kantelhardt, J. W., Ashkenazy, Y., Ivanov, P. C., Bunde, A., Havlin, S., Penzel, T., Peter, J.-H., & Stanley, H. E. (2002) · Physical Review E · doi:10.1103/physreve.65.051908 · Cited by 186
Self-citationEndogenous circadian rhythm in an index of cardiac vulnerability independent of changes in behavior
Hu, K., Ivanov, P. C., Hilton, M. F., Chen, Z., Ayers, R. T., Stanley, H. E., & Shea, S. A. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0408243101 · Cited by 141
Self-citationNetwork Physiology: Mapping Interactions Between Networks of Physiologic Networks
Ivanov, P. C., & Bartsch, R. P. (2014) · Understanding complex systems · doi:10.1007/978-3-319-03518-5_10 · Cited by 130
Self-citationCorrelation differences in heartbeat fluctuations during rest and exercise
Karasik, R., Sapir, N., Ashkenazy, Y., et al. (2002) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.66.062902 · Cited by 127
Cited by
Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality
Tian, Y. E., Cropley, V., Maier, A. B., Lautenschlager, N. T., Breakspear, M., & Zalesky, A. (2023) · Nature Medicine · doi:10.1038/s41591-023-02296-6 · Cited by 618
Wearable Sleep Technology in Clinical and Research Settings
DE ZAMBOTTI, M., CELLINI, N., GOLDSTONE, A., COLRAIN, I. M., & BAKER, F. C. (2019) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000001947 · Cited by 564
Dynamic coupling between the central and autonomic nervous systems during sleep: a review
de Zambotti, M., Trinder, J., Silvani, A., Colrain, I. M., & Baker, F. C. (2018) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.03.027 · Cited by 194
Dynamic Hormone Control of Stress and Fertility
Zavala, E., Voliotis, M., Zerenner, T., Tabak, J., Walker, J. J., Li, X. F., Terry, J. R., Lightman, S. L., O'Byrne, K., & Tsaneva-Atanasova, K. (2020) · Frontiers in Physiology · doi:10.3389/fphys.2020.598845 · Cited by 34
“This can be done in combination with a range of tools from network physiology and machine learning that consider the dynamic links between coupled body rhythms, such as body temperature and sleep (Bashan et al., 2012; Bartsch et al., 2015; Ivanov et al., 2016).”
Background
Metadata
- Authors
- Bartsch, R. P., Liu, K. K. L., Bashan, A., & Ivanov, P. C.
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-11-10
- Volume
- 10
- Issue
- 11
- Pages
- e0142143
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W2165515236
- Cited by
- 411
- References
- 34
- FWCI
- 16.51
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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