Abstract
BackgroundUnderstanding the underlying architecture of mood regulation in bipolar disorder (BD) is important, as we are starting to conceptualize BD as a more complex disorder than one of recurring manic or depressive episodes. Nonlinear techniques are employed to understand and model the behavior of complex systems. Our aim was to assess the underlying nonlinear properties that account for mood and energy fluctuations in patients with BD; and to compare whether these processes were different in healthy controls (HC) and unaffected first-degree relatives (FDR). We used three different nonlinear techniques: Lyapunov exponent, detrended fluctuation analysis and fractal dimension to assess the underlying behavior of mood and energy fluctuations in all groups; and subsequently to assess whether these arise from different processes in each of these groups.ResultsThere was a positive, short-term autocorrelation for both mood and energy series in all three groups. In the mood series, the largest Lyapunov exponent was found in HC (1.84), compared to BD (1.63) and FDR (1.71) groups [F (2, 87) = 8.42, p < 0.005]. A post-hoc Tukey test showed that Lyapunov exponent in HC was significantly higher than both the BD (p = 0.003) and FDR groups (p = 0.03). Similarly, in the energy series, the largest Lyapunov exponent was found in HC (1.85), compared to BD (1.76) and FDR (1.67) [F (2, 87) = 11.02; p < 0.005]. There were no significant differences between groups for the detrended fluctuation analysis or fractal dimension.ConclusionsThe underlying nature of mood variability is in keeping with that of a chaotic system, which means that fluctuations are generated by deterministic nonlinear process(es) in HC, BD, and FDR. The value of this complex modeling lies in analyzing the nature of the processes involved in mood regulation. It also suggests that the window for episode prediction in BD will be inevitably short.
Highlights
Understanding the underlying architecture of mood regulation in bipolar disorder (BD) is important, as we are starting to conceptualize BD as a more complex disorder than one of recurring manic or depressive episodes
Demographic and clinical characteristics of the sample Ninety participants were included in the analyses: 30 healthy controls (HC), 30 euthymic BD and 30 first-degree relatives (FDR)
The underlying nature of mood variability is in keeping with that of a chaotic system, rather than noise, which means that fluctuations are generated by deterministic nonlinear process(es) in HC, BD, and FDR participants
Summary
Understanding the underlying architecture of mood regulation in bipolar disorder (BD) is important, as we are starting to conceptualize BD as a more complex disorder than one of recurring manic or depressive episodes. From a dynamic point of view, Ortiz et al Int J Bipolar Disord (2021) 9:30 the properties that enable the system to adapt to these stochastic variations are considered “complex”. These properties include: (i) nonlinearity, i.e., systems do not respond in a way that is proportional to the amount they are stimulated; (ii) the lack of a single or characteristic scale, i.e., fractal organization; and (iii) emergent properties, i.e., properties that emerge from the whole but were not present in the parts. The challenge is that these principles are difficult to uncover by conventional analyses and require nonlinear methods for their study
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