Applications of dynamical complexity theory in traditional Chinese medicine

Abstract

Traditional Chinese medicine (TCM) has been gradually accepted by the world. Despite its widespread use in clinical settings, a major challenge in TCM is to study it scientifically. This difficulty arises from the fact that TCM views human body as a complex dynamical system, and focuses on the balance of the human body, both internally and with its external environment. As a result, conventional tools that are based on reductionist approach are not adequate. Methods that can quantify the dynamics of complex integrative systems may bring new insights and utilities about the clinical practice and evaluation of efficacy of TCM. The dynamical complexity theory recently proposed and its computational algorithm, Multiscale Entropy (MSE) analysis, are consistent with TCM concepts. This new system level analysis has been successfully applied to many health and disease related topics in medicine. We believe that there could be many promising applications of this dynamical complexity concept in TCM. In this article, we propose some promising applications and research areas that TCM practitioners and researchers can pursue.

Keywords

traditional Chinese medicine / Multiscale Entropy / dynamical complexity / system level / applications

1 The challenges in traditional Chinese medicine research

Originated in ancient China, traditional Chinese medicine (TCM) evolved over thousands of years. TCM contains a broad range of philosophical and medical concepts and has developed into a comprehensive healthcare-focused medical system. Over long period of continuous practice and refinement in treating patients, as well as preventing health problems, TCM has shown to be an indispensable part of medical practice by its effects and efficacy on various diseases and disorders. Today, TCM is practiced side by side with western medicine in many of China’s hospitals and clinics [1], and it has been gradually accepted in many Asian countries. In spite of the widespread use of TCM in China and the continuous increase of utilization in other parts of the world, scientific understanding of TCM’s effectiveness is, for the most part, limited [1] for several reasons.

First of all, the philosophy and culture based theories and non-standardized treatment practices make TCM hard to be quantified. The theoretical framework of TCM has a number of key components, such as Yin-Yang Theory, five elements, eight principles, Zang-Fu organs and meridians, as well as principles of its unique physiological, pathological, diagnostic and therapeutic features. Based on these fundamental concepts, TCM applies multiple therapeutic methods such as prescribed herbal formulas, acupuncture, moxibustion, cupping, Tuina (medical massage), Tai Chi/Qigong, life-style adjustment dietary therapy, or even emotional adjustment to alleviate illnesses.

Furthermore, TCM’s view of the human body is only marginally concerned with anatomical structures, instead it focuses primarily on the body’s functions [2,3]. One of the TCM’s fundamental theories is to manage health and to consider the human body as a complex dynamical system as a whole, which is also known as holism. TCM emphasizes not only the regulation of the human body, but also the integrity of body functions and its interaction with the environments. In general, health is perceived as harmonious regulation and interaction, while disease is interpreted as a disharmony (or imbalance) in the functions or interactions. TCM practitioners aim to trace symptoms and patterns of underlying disharmony by assessing the human body as a whole. TCM practice tends to seek out dynamical functional activities rather than to look for the fixed somatic structures that perform the activities [4].

In comparison, western medicine emphasizes detailed investigations with modern technologies at microscopic scales, while TCM follows a traditional approach, emphasizes theories and clinical implementations, but lacks modernization [5,6]. Therefore, it is important to expand TCM by utilizing frontier scientific knowledge. To further study TCM and to evaluate its efficacy, while preserving its strength and unique framework, we need to develop quantitative approaches that can offer system-level measurements and understandings.

Recently, one of us and his colleagues have proposed a complex system approach to quantify the dynamical complexity of complex systems [7–9]. This complexity measurement, called Multiscale Entropy (MSE) analysis, has been successfully applied to many health and disease related topics. Furthermore, over the past several years, it has had a wide range of applications across many disciplines when complex systems are involved. Interestingly, this innovative theory of dynamical complexity is perfectly consistent with the concepts of TCM in viewing the human body as a holistic dynamical system. Thus, we expect that the MSE analysis of complex fluctuations might be a good candidate to advance our understanding of TCM and to serve as a valuable quantitative tool in TCM research. In this paper, we will first briefly discuss basic concepts of the dynamical complexity theory and its applications, and then discuss some promising applications in TCM.

2 Dynamical complexity theory and its applications

Dynamical fluctuations in the output of complex biological systems with multiple interacting components often exhibit remarkably complicated patterns. Such fluctuations have long been ignored by conventional analyses. Indeed, the presence of these fluctuations is often assumed to simply reflect the fact that biological systems are being constantly perturbed by external and intrinsic noise. However, recent findings by many research groups clearly indicate that these complex fluctuations exhibit interesting structures that were not previously anticipated. More importantly, these fluctuations may also contain useful information about the emerging complexity of the systems. Here we briefly describe a dynamical system perspective to understand the origin of these fluctuations.

In dynamical systems research, it is common to describe a system by a set of variables. If defined properly, these so-called “state variables” can uniquely determine the state of the system and the time course of its revolution. The state space representation provides a useful conceptual picture for dynamical systems. A meaningful quantification of the complexity of a biological system should be related to the system’s capacity to adapt and function in an ever-changing environment. The system that can adapt to the most external challenges (stresses) will have the best advantage for survival. Therefore, biological systems should evolve to increase their dynamical capacity (complexity). As a result, biological systems we observed today are highly complex since they are the products of a very long evolutionary process. We also hypothesized that aging and disease will degrade a system’s complexity, since they represent a less adapted system. Using the state space concept, an external perturbation (challenge or stress) to a biological system requires the system to move from one location to a different area of the state space in order to adapt to the perturbation. A healthy system should be able to easily move from one area to another, while a diseased system has a very limited ability to adapt, and thus cannot move to other regions of the state space. Complexity is a measure of a system’s capacity to adapt, therefore, it should be related to the available space and ways that a system can move around in the state space.

By applying the fluctuation dissipation theorem [10] concept, namely that we can simply measure the spontaneous fluctuations of a system in the state space when it is under free-running condition, and use that information to predict the ability that a system can adapt when encounters a challenge. This hypothesis dramatically simplifies our task of defining a system’s complexity, since we can now measure it under a “stress free” condition, which is easier to achieve in the real world. The MSE analysis [7–9] was thus proposed as a measure of a system’s dynamical complexity, based on the above rationale.

Due to its capacity to characterize complex dynamics within and between physiological systems [11], the application of MSE analysis as a measure of complexity has shown to be very useful in improving our understanding of human body and its functions. To date, MSE-based measurements have been used in studies involving various biological outputs, including heart beats [12–15], pulse waves [13], blood pressure [16,17], breathing [18], brain activities [19–23], neuroimaging [24], body temperature [25,26], muscle activity [27,28], center of pressure (COP) of body sway [11,29], bone and cartilage [30], human gait [31,32], mood or mental stability [33–35].

The complexity-based approach has shown to be useful on various targeted populations in multiple studies and research projects. Moreover, some novel applications have been proposed as promising clinical indicators. According to Google Scholar, the original articles describing the MSE analysis received over 1300 citations, including hundreds of studies in biomedical fields. Biomedical applications can be found in diverse disciplines such as neurology, cardiology, pulmonary medicine, endocrinology, nephrology, geriatrics, obstetrics and gynecology, critical care, orthopedics, psychiatry, psychology, physiology, pathology and more.

3 Potential applications of complexity measures in TCM

The MSE index can be used as a sensitive tool for detecting dysregulation [17] or abnormalities [36], predicting risks [37], deterioration [25,26,30] or improvement, revealing the connections or adaptability [38], facilitating classifications of disorders [27]. In general, the analysis is particularly powerful in studying characteristics on the system level, as a result, it brings new insight to complementary and alternative medicine.

Taking Tai Chi as an example, the MSE analysis has not only helped explain the mechanism, but also led to the development of useful clinical practices that benefit a specific target group, i.e., elderly people with higher fall risks. Tai Chi has been considered as a comprehensive exercise involving breathing exercise, body movement, balance control and even mindfulness training. Due to the multifaceted nature of Tai Chi, it has been long considered difficult to be scientifically evaluated. However, the MSE analysis has addressed the challenge by providing scientific evidences from a different aspect. For example, for the center of pressure (COP) dynamics of postural sway, Kang et al. demonstrated that complexity decreases when performing a dual task; and postural complexity during quiet standing is independent of other conventional correlates of balance control, such as age and vision [39]. Wayne et al. carried out further complexity investigation on Tai Chi and aging [11]. Those studies lead to the development of a series of alternative interventions to prevent elderly people from falling. Wayne et al. are completing a complexity-based randomized controlled trial with different arms to evaluate novel interventions, including promising mind-body exercises that treat age-related disease and promote healthy aging [11].

We believe that there are many promising applications in TCM of applying the dynamical complexity concept. The MSE and other complexity measures can be valuable to quantify TCM studies and evaluations. Here we propose some applications or research areas that TCM practitioners and researchers can pursue.

3.1 Evaluation of health conditions

Dynamical complexity theory suggests that a meaningful quantification of the complexity of a biological system should be related to the system’s capacity to adapt and function in an ever changing environment. The system that can adapt to the most external challenges (stresses) will have the best advantage for survival [9]. TCM encompasses the same notion in defining health, however, it has always lack of a feasible measurement to quantify this idea. Complexity is a measure of a system’s capacity to adapt, therefore, we may be able to use this innovative way to derive the desirable information in order to evaluate a subject’s health status. This can be applied in clinical practice or medical research. One example is to use it for the subclinical conditions, i.e., when a subject is at the very early stage of disease, or in transition from perfectly healthy to the disease state, without signs and symptoms that are detectable by conventional examinations of western medicine. Another example is to use this index to monitor the improvement of health after treatment or intervention.

3.2 New insight to TCM pattern classification

Dynamical fluctuations in the output of complex biological systems with multiple interacting components often exhibit remarkably complicated patterns [9]. This is the same as dynamic phase change of a disease. In TCM theory, a disease usually starts because of the invasion of external environmental factors or the lack of internal balance. As the disease develops, the disturbance may involve different levels of body and show various symptoms. TCM practitioners classify those symptoms by the theories of TCM pattern classification (also known as syndrome differentiation). In TCM practice, this is the basic step to recognize disease and also the key to successful treatment [40,41].

Traditionally, the classification determination is mainly based on standard TCM diagnostic methods: observation, auscultation and olfaction, inquiry and pulse diagnosis (palpation). From a modern medical perspective, heart beats, breaths, electroencephalography (EEG), gaits, laboratory tests, or any other forms of biological output signals generated by human body are physical signs that are measurable. TCM practitioners observe and interpret all those in pattern classification. Since those signals are usually comprised of dynamical, non-stationary, and nonlinear time series, implementation of adaptive data analysis may bring in new insight to objectively distinguish the disharmony and unbalanced functions. Fluctuations of biological output signals may contain useful information about the emerging complexity of the systems.

3.3 Utilization in monitoring chronic disease treatment

TCM emphasizes balance, which is a broader concept of homeostasis, a process that maintains the stability of the human body’s internal environment in response to changes in external conditions. The presence of certain forms of deviation from the norm of physiological or mental conditions may cause the development of chronic diseases. There have been a good number of clinical evidences that valued TCM and believed that it can shift unexpected homeostasis to the overall human body homeostasis [42]. This has been recommended to be complementary to western medicine in the management of chronic diseases [43], which notably improves the qualities of life and reduces the expenses for chronic diseases.

Since the development of disease is dynamic, the process of diagnosis, treatment, and follow-up should be personalized and adjusted according to different phases of diseases [44]. However, the challenge is that it is not practical to monitor the functions of every components of human body. For biological systems, the state space is of very high dimensionality, and not all variables can be measured, therefore, specific measurements that can summarize the health condition of the whole system need to be monitored. The best approach is to take advantage of the fact that an integrative physiological system will have complex coupling between different components of the system. By investigating any given signal at various time scales, we can probe the other dimensions of the system. Thus, system complexity based on any single properly selected signal can be good candidates to monitor, by doing so, TCM practitioners will have objective and quantified indices to evaluate the overall health of a patient.

3.4 Clinical evaluation in the development of herbal medicine

To identify potential side effects of once-promising drugs, it is more important for both western medicine and TCM to evaluate pharmacological effects on a system level rather than at molecular level. Balance is considered to be a complex interplay between body and mind, which is reflected at all levels, ranging from the biochemical component perspective to the energetic system control of the physical body. Research methodology to study the interaction between applications of TCM herbal medicine and the human body also require a system perspective in drug research and development.

In the field of new drug design and development, the multiple-targets, low-affinity model has gained more attention, network pharmacology was proposed and became popular worldwide, “cocktail” and fixed-dose combinations were brought forth. All those changes show increasing consistency with the medication ideas of Chinese medicine [45]. However, western and Chinese medicine belongs to different theoretical system. In western medicine, application of drugs usually involves trying to influence a system by interacting with a single target molecule, a complex pathway, a cascade of reactions, or a feed-back loop. The reality is that most diseases are multi-factorial which means that treating a single target provides a partial treatment and in case of chronic diseases, serious side effects occur, particularly in the long-term. Although this awareness is not new, it has been very difficult to find alternative routes given the mentioned complexity of the living system, which is almost impossible to reveal [46].

In contrast, TCM views diseases and treatment from a very different perspective. For example, TCM emphasizes bi-directional adjustments, which is treating deficiency by nourishing and tonification methods, while treating excess by controlling the extras and fostering or cultivating the weak. As in complex system approaches, biological systems such as human body are believed to operate across multiple scales of space and time, and hence their complexity is also multiscale and hierarchical. Therefore, dynamical complexity index can be used to evaluate clinical efficacy of pharmacotherapies, particularly in TCM, where herbal compounds are frequently used. The output signals of targeted parts of the body can show the system complexity upon being challenged by the pathological factors and upon being treated by herbal medicine.

In conclusion, novel techniques such as MSE should be further studied, and once the complexity concept is incorporated in TCM field, there will be great amounts of new projects and studies that may promote modernization and qualification of the traditional practice.

References

[1] National Center for Complementary and Alternative Medicine, NIH. Traditional Chinese Medicine: An Introduction. 2010.

[2] Matuk C. Seeing the body: the divergence of ancient Chinese and western medical illustration. J Biocommun 2006; 32(1): 8

[3] Xutian S, Zhang J, Louise W. New exploration and understanding of traditional Chinese medicine. Am J Chin Med 2009; 37(3): 411–426

[4] Kaptchuk T. The Web That Has No Weaver: understanding Chinese medicine. New York: McGraw-Hill, 2000

[5] Jordan JB, Tu X. Advances in heroin addiction treatment with traditional Chinese medicine: a systematic review of recent Chinese language journals. Am J Chin Med 2008; 36(3): 437–447

[6] Zhang NL, Yuan S, Chen T, Wang Y. Statistical validation of traditional chinese medicine theories. J Altern Complement Med 2008; 14(5): 583–587

[7] Costa M, Goldberger AL, Peng CK. Multiscale entropy analysis of complex physiologic time series. Phys Rev Lett 2002; 89(6): 068102

[8] Costa M, Goldberger AL, Peng CK. Multiscale entropy analysis of biological signals. Phys Rev E Stat Nonlin Soft Matter Phys 2005; 71(2 Pt 1): 021906

[9] Peng CK, Costa M, Goldberger AL. Adaptive data analysis of complex fluctuations in physiologic time series. Adv Adapt Data Anal 2009; 1(1): 61–70

[10] Nyquist H. Thermal agitation of electric charge in conductors. Phys Rev 1928; 21: 4

[11] Wayne PM, Manor B, Novak V, Costa MD, Hausdorff JM, Goldberger AL, Ahn AC, Yeh GY, Peng CK, Lough M, Davis RB, Quilty MT, Lipsitz LA. A systems biology approach to studying Tai Chi, physiological complexity and healthy aging: design and rationale of a pragmatic randomized controlled trial. Contemp Clin Trials 2013; 34(1): 21–34

[12] Bari V, Valencia JF, Vallverdu M, Girardengo G, Bassani T, Marchi A, Calvillo L, Caminal P, Cerutti S, Brink PA, Crotti L, Schwartz PJ, Porta A. Refined multiscale entropy analysis of heart period and QT interval variabilities in long QT syndrome type-1 patients. Conf Proc IEEE Eng Med Biol Soc 2013; 2013: 5554–5557

[13] Wu HT, Hsu PC, Lin CF, Wang HJ, Sun CK, Liu AB, Lo MT, Tang CJ. Multiscale entropy analysis of pulse wave velocity for assessing atherosclerosis in the aged and diabetic. IEEE Trans Biomed Eng 2011; 58(10): 2978–2981

[14] Ho YL, Lin C, Lin YH, Lo MT. The prognostic value of non-linear analysis of heart rate variability in patients with congestive heart failure—a pilot study of multiscale entropy. PLoS ONE 2011; 6(4): e18699

[15] Baumert M, Javorka M, Seeck A, Faber R, Sanders P, Voss A. Multiscale entropy and detrended fluctuation analysis of QT interval and heart rate variability during normal pregnancy. Comput Biol Med 2012; 42(3): 347–352

[16] Turianikova Z, Javorka K, Baumert M, Calkovska A, Javorka M. The effect of orthostatic stress on multiscale entropy of heart rate and blood pressure. Physiol Meas 2011; 32(9): 1425–1437

[17] Trunkvalterova Z, Javorka M, Tonhajzerova I, Javorkova J, Lazarova Z, Javorka K, Baumert M. Reduced short-term complexity of heart rate and blood pressure dynamics in patients with diabetes mellitus type 1: multiscale entropy analysis. Physiol Meas 2008; 29(7): 817–828

[18] Papaioannou VE, Chouvarda I, Maglaveras N, Dragoumanis C, Pneumatikos I. Changes of heart and respiratory rate dynamics during weaning from mechanical ventilation: a study of physiologic complexity in surgical critically ill patients. J Crit Care 2011; 26(3): 262–272

[19] Bell IR, Howerter A, Jackson N, Aickin M, Bootzin RR, Brooks AJ. Nonlinear dynamical systems effects of homeopathic remedies on multiscale entropy and correlation dimension of slow wave sleep EEG in young adults with histories of coffee-induced insomnia. Homeopathy 2012; 101(3): 182–192

[20] Catarino A, Churches O, Baron-Cohen S, Andrade A, Ring H. Atypical EEG complexity in autism spectrum conditions: a multiscale entropy analysis. Clin Neurophysiol 2011; 122(12): 2375–2383

[21] Mizuno T, Takahashi T, Cho RY, Kikuchi M, Murata T, Takahashi K, Wada Y. Assessment of EEG dynamical complexity in Alzheimer’s disease using multiscale entropy. Clin Neurophysiol 2010; 121(9): 1438–1446

[22] Takahashi T, Cho RY, Mizuno T, Kikuchi M, Murata T, Takahashi K, Wada Y. Antipsychotics reverse abnormal EEG complexity in drug-naive schizophrenia: a multiscale entropy analysis. Neuroimage 2010; 51(1): 173–182

[23] Takahashi T, Cho RY, Murata T, Mizuno T, Kikuchi M, Mizukami K, Kosaka H, Takahashi K, Wada Y. Age-related variation in EEG complexity to photic stimulation: a multiscale entropy analysis. Clin Neurophysiol 2009; 120(3): 476–483

[24] Heisz J J, McIntosh AR. Applications of EEG neuroimaging data: event-related potentials, spectral power, and multiscale entropy. J Vis Exp 2013; (76): e50131

[25] Papaioannou VE, Chouvarda IG, Maglaveras NK, Baltopoulos GI, Pneumatikos IA. Temperature multiscale entropy analysis: a promising marker for early prediction of mortality in septic patients. Physiol Meas 2013; 34(11): 1449–1466

[26] Papaioannou VE, Chouvarda IG, Maglaveras NK, Pneumatikos IA. Temperature variability analysis using wavelets and multiscale entropy in patients with systemic inflammatory response syndrome, sepsis, and septic shock. Crit Care 2012; 16(2): R51

[27] Istenič R, Kaplanis PA, Pattichis CS, Zazula D. Multiscale entropy-based approach to automated surface EMG classification of neuromuscular disorders. Med Biol Eng Comput 2010; 48(8): 773–781

[28] Zhang X, Chen X, Barkhaus PE, Zhou P. Multiscale entropy analysis of different spontaneous motor unit discharge patterns. IEEE J Biomed Health Inform 2013; 17(2): 470–476

[29] Huang CW, Sue PD, Abbod MF, Jiang BC, Shieh JS. Measuring center of pressure signals to quantify human balance using multivariate multiscale entropy by designing a force platform. Sensors (Basel) 2013; 13(8): 10151–10166

[30] Fernandez JW, Shim VB, Hunter PJ. Integrating degenerative mechanisms in bone and cartilage: a multiscale approach. Conf Proc IEEE Eng Med Biol Soc 2012; 2012: 6616–6619

[31] Khandoker AH, Karmakar CK, Begg RK, Palaniswami M. Wavelet-based multiscale analysis of minimum toe clearance variability in the young and elderly during walking. Conf Proc IEEE Eng Med Biol Soc 2007; 2007: 1558–1561

[32] Gruber AH, Busa MA, Gorton III GE, Van Emmerik RE, Masso PD, Hamill J. Time-to-contact and multiscale entropy identify differences in postural control in adolescent idiopathic scoliosis. Gait Posture 2011; 34(1): 13–18

[33] Yang AC, Tsai SJ. Is mental illness complex? From behavior to brain. Prog Neuropsychopharmacol Biol Psychiatry 2013; 45: 253–257

[34] Yang AC, Tsai SJ. Complexity of mental illness: a new research dimension. Prog Neuropsychopharmacol Biol Psychiatry 2013; 45: 251–252

[35] Takahashi T. Complexity of spontaneous brain activity in mental disorders. Prog Neuropsychopharmacol Biol Psychiatry 2013; 45: 258–266

[36] Wu HT, Lo MT, Chen GH, Sun CK, Chen JJ. Novel application of a multiscale entropy index as a sensitive tool for detecting subtle vascular abnormalities in the aged and diabetic. Comput Math Methods Med 2013; 2013: 645702

[37] Viceconti M, Taddei F, Van Sint Jan S, Leardini A, Cristofolini L, Stea S, Baruffaldi F, Baleani M. Multiscale modelling of the skeleton for the prediction of the risk of fracture. Clin Biomech (Bristol, Avon) 2008; 23(7): 845–852

[38] Liang WK, Lo MT, Yang AC, Peng CK, Cheng SK, Tseng P, Juan CH. Revealing the brain’s adaptability and the transcranial direct current stimulation facilitating effect in inhibitory control by multiscale entropy. Neuroimage 2014; 90: 218–234

[39] Kang HG, Costa MD, Priplata AA, Starobinets OV, Goldberger AL, Peng CK, Kiely DK, Cupples LA, Lipsitz LA. Frailty and the degradation of complex balance dynamics during a dual-task protocol. J Gerontol A Biol Sci Med Sci 2009; 64(12): 1304–1311

[40] Lu A, Jiang M, Zhang C, Chan K. An integrative approach of linking traditional Chinese medicine pattern classification and biomedicine diagnosis. J Ethnopharmacol 2012; 141(2): 549–556

[41] Jiang M, Lu C, Zhang C, Yang J, Tan Y, Lu A, Chan K. Syndrome differentiation in modern research of traditional Chinese medicine. J Ethnopharmacol 2012; 140(3): 634–642

[42] Wang JH. Traditional Chinese medicine and the positive correlation with homeostatic evolution of human being: based on medical perspective. Chin J Integr Med 2012; 18(8): 629–634

[43] Fan XJ, Yu H, Ren J. Homeostasis and compensatory homeostasis: bridging western medicine and traditional Chinese medicine. Curr Cardiol Rev 2011; 7(1): 43–46

[44] Roberti di Sarsina P, Alivia M, Guadagni P. Traditional, complementary and alternative medical systems and their contribution to personalisation, prediction and prevention in medicine-person-centred medicine. EPMA J 2012; 3(1): 15

[45] Yang HJ, Shen D, Xu HY, Lu P. A new strategy in drug design of Chinese medicine: theory, method and techniques. Chin J Integr Med 2012; 18(11): 803–806

[46] Wang M, Lamers RJ, Korthout HA, van Nesselrooij JH, Witkamp RF, van der Heijden R, Voshol PJ, Havekes LM, Verpoorte R, van der Greef J. Metabolomics in the context of systems biology: bridging traditional Chinese medicine and molecular pharmacology. Phytother Res 2005; 19(3): 173–182

RIGHTS & PERMISSIONS

Higher Education Press and Springer-Verlag Berlin Heidelberg