The Triple Helix @ UChicago

Winter 2016

"Complexity Theory: Chaotic Hearts Lead to New Understanding of Depression" by Stephanie Williams

 "Life exists at the edge of chaos. The fate of all complex adapting systems in the biosphere—from single cells to economies—is to evolve to a natural state between order and chaos” -Stuart Kauffman

Researchers are reviving a decades-old hypothesis that challenges classical homeostasis models of physiology: the more complex an individual’s physiological signals are, the healthier the individual is. As humans grow older and encounter disease, their physiological signals degrade and lose complexity. In young populations, EKGs show healthy, irregular patterns, and exhibit fractal-like complexity. In aged populations, EKGs show regular, simple sinus rhythms. These differences in heart signal complexity allow researchers to measure beat-to-beat heart rate (HR) fluctuations in diseased and healthy backgrounds, and form inferences from the coded information. This is especially useful for collecting information about neuroanatomic control in disease backgrounds that have been elusive to regular diagnostic methods, such as mental illness (Listed et al., 2011). By comparing the signals of ill patients with those of healthy patients, researchers can now detect disruptions and begin quantifying the degree of neuroanatomic disruptions in mental illnesses. 

“Complexity,” in the pure, mathematical sense of the word, refers to “the length of the shortest binary input into a universal Turing machine such that the output is the initial string”(Cover and Thomas, Elements of Information Theory). Put less technically, complexity refers to behavior that does not repeat itself, yet is deterministic within dynamic nonlinear systems. The human body needs to adapt to internal and external stressors, which requires it to be nimble and adaptive (Goldberger, 2002) Overly rigid patterns in EKGs indicate the presence of a sick heart that can’t respond to the body’s demands (Goldberger, 2002). Thus, the more “complex” an individual’s physiology is, the more likely they are to survive. Dr. Madalena Costa, Ph.D, an authority on chaotic dynamics, says that complex physiological systems are distinguished by their “multiscale organization” and “temporal and spatial organization” (Costa, 2002). Essentially, the healthiest signals are the most time irreversible, which allows them to adapt to both “internal and external variables.” Importantly, this correlation between complexity and health extends beyond heart dynamics to other biological measures, including brain waves, breathing rates, balance and gait.  

The challenge now facing researchers is the development of accurate algorithms and computational tools that can detect when signal complexity is breaking down. If an individual’s heart is running counter to a regular sinus pattern, but suddenly begins exhibiting regular behavior, then the individual is very likely on the verge of experiencing a cardiac attack. Anticipatory medical devices, currently under development at the Wyss Institute at MIT, aim to detect these changes in physiological signals and alert individuals to the possibility of an attack. Goldberger hopes to develop wearable devices that can read out these signals: “What we’d like to do is probe those signals for encoded information telling us that the body’s physiology is about to drive off a cliff.” However, realistically developing devices that can quantify the complexity of physiological dynamics is indeed a challenge. To apply the idea of complexity to physiology, researchers first have to map the states of the systems into strings of characters (Costa, 2002). Entropy-based measures can be used, but there is no “straightforward correspondence” between entropy and complexity (Costa, 2002). Ultimately, researchers will need not only to develop foolproof methods of quantifying complexity, but also standard models for complexity under healthy conditions as well as with the changes that occur with the onset of pathology and aging (Costa, 2002).  

Major depression (MD), a highly prevalent disorder, is an important target of nonlinear dynamics research. The psychopathology, which is currently diagnosed according to the protocol of the DSM-5, affects multiple physiological functions, and leads to a loss of complexity in heart rate dynamics (Listed et al., 2011). The excitement surrounding the loss of complexity in MD patients stems from the fact that researchers can assume MD patients have neuroautonomic perturbations, and can use this when analyzing the degradation of complexity in patient’s signals (Listed et. al, 2011). The central autonomic network, led primarily by the prefrontal cortex, allows the brain to send integrated output commands to the heart. Perturbations in neuroautonomy can therefore manifest as perturbations in the heart signals. Analyses of the heart signals and the degrees of complexity among varying degrees of depression in patients has potential to lead to an elucidation of the degree of dysfunction in the physiology of each patient, and thus, potential to lead to a quantifier for the disease. 

Investigations of MD using the complexity hypothesis have made significant progress in quantifying depression. In one study, a group of researchers including EKG expert Ary Goldberger compared the heart rate dynamics of depressed young to middle aged men to healthy counterparts, and found that the depressed patients’ signals were less complex. The study observed 24 unmedicated adults during an acute episode of MD, which was diagnosed according to DSM-IV-TR criteria. Researchers measured the complexity in the men’s cardiac interval time series using a method called multi scale entropy (MSE), and assessed the complexity of these fluctuations during sleeping hours. Their results revealed a significant reduction (p<.03) in complexity. These findings strongly support the correlation between HR dynamic complexity and psychopathology. This is profound in two ways. Firstly, researchers now have a method, albeit tentative, to probe the dynamical changes that occur in MD. Secondly, researchers now have a substantive, novel biomarker for MD that could replace the highly debated diagnostic criteria of the DSM-IV. 

Though the field of nonlinear dynamics in physiology is rapidly advancing, significant obstacles still stand in the way of its definitive application in clinical settings. Current models of the complex dynamics will require a great deal of improvement before they can be used as analytic tools in medicine. However, when the research does finally catch up to the mathematics, it will be hard to understate the benefits of nonlinear dynamics in medicine. 


[1] M. Cover, J.A. Thomas, Elements of Information Theory, Wiley, USA, 1991. 
[2] Costa, M., Goldberger, C.-K. (2002). Multiscale Entropy Analysis of Complex Physiologic Time Series. Physical Review Letters, 89(6), 102-106.
[3] Goldberger AL, Peng CK, Lipsitz LA (2002). What is physiologic complexity and how does it change with aging and disease? Neurobiol Aging. Jan-Feb;23(1):23-6
[4] Leistedt SJ-J, Linkowski P, Lanquart J-P, et al. Decreased neuroautonomic complexity in men during an acute major depressive episode: analysis of heart rate dynamics. Translational Psychiatry. 2011;1(7):e27-. doi:10.1038/tp.2011.23.

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