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The perils of being too stable: mood regulation in bipolar disorder

Bipolar disorder or recurrent depression are traditionally conceptualized as series of discrete mood episodes interspersed with “normal” mood. But what does “normal” mean in this context? Mood that is completely stable? No mood changes at all? Does it mean that those adjustments (i.e., fluctuations) in mood that result from our continual interaction with the environment are problematic? It has long been recognized clinically that minor mood fluctuations are an intrinsic property of mood regulation both in healthy individuals and in euthymic patients with bipolar disorder, but methods for assessing mood variability are only now being developed and tested. Major mood episodes have been the focus of both treatment and neurobiological studies, but there is growing recognition that interepisode intervals may provide important clues about the nature of mood disorders. Some authors view bipolar disorder as a condition of mood instability that persists into illness remission. Interepisode symptoms and functional impairment are relevant, as they are associated with risk of relapse and their absence is one of the best predictors of long-term lithium response. But less is known about the physiological underpinnings of mood regulation and mood changes during remission periods. Mood regulation is a complex and poorly understood process. It can be conceived as a system allowing individuals to respond flexibly to changing and often unpredictable conditions. The study of mood disorders to date, however, has paid relatively little attention to this potentially key feature. From a dynamic point of view, the properties that enable this type of adaptability are considered to be complex. These properties include nonlinearity (systems do not respond proportionally to stimulation; i.e., small changes can have huge effects), lack of a single or characteristic scale (fractalor treelike organization) and emergent properties (properties that emerge from the whole, but were not present in the parts). Nonlinearity, however, does not imply a lack of underlying structure, as nonlinear systems have organized, discoverable principles. The challenge is that these principles are difficult to uncover using conventional analyses. Mood patterns in individuals with bipolar disorder, although aperiodic, may be more organized than those of healthy individuals, which led to the description of mood in patients with bipolar disorder in terms of chaotic (nonlinear) dynamics.1 Nonlinear methods offer new tools with which to quantify, model and attempt to predict the behaviour of complex systems.2,3 Entropy is a type of nonlinear measure that reflects the amount of noise or disorganization in a system. A type of entropy, multiscale entropy, measures the complexity of a system and is often used in biology. Typically, lower entropy levels (corresponding to recognizable patterns in the data) may indicate impaired physiological regulations, as in cardiovascular4 and metabolic diseases,5 premenstrual dysphoric disorder6 and bipolar disorder.7 Time-series analysis is another type of nonlinear method that is usually used to study a collection of observations made sequentially in time.8 Mood also depends on time, as successive observations are not independent. Mood is continuously regulated by processes that reflect the behaviour of complex systems, with relatively simple mechanisms responsible for their functioning; the adaptability and variability of mood become impaired in patients with bipolar disorder.7 This reduced flexibility, as indicative of illness, is not limited to mood processes: findings implicitly associating greater regularity with illness have been found in other clinical situations, such as in ventricular tachyarrhythmias or prior to a seizure.9,10 If we can conceptualize mood regulation as a complex system, suitable mathematical models have the potential to provide insight into this entity.11–13