The futility of long-term predictions in bipolar disorder: mood fluctuations are the result of deterministic chaotic processes

Abigail Ortiz; Kamil Bradler; Maxine Mowete; Stephane MacLean; Julie Garnham; Claire Slaney; Benoit H Mulsant; Martin Alda

doi:10.1186/s40345-021-00235-3

The futility of long-term predictions in bipolar disorder: mood fluctuations are the result of deterministic chaotic processes

Int J Bipolar Disord. 2021 Oct 1;9(1):30. doi: 10.1186/s40345-021-00235-3.

Authors

Abigail Ortiz^{1

2}, Kamil Bradler³, Maxine Mowete⁴, Stephane MacLean⁵, Julie Garnham⁶, Claire Slaney⁶, Benoit H Mulsant^{7

8}, Martin Alda^{9

10}

Affiliations

¹ Department of Psychiatry, University of Toronto, Toronto, ON, Canada. abigail.ortiz@utoronto.ca.
² Centre for Addiction & Mental Health, CAMH 100 Stokes St., Rm 4229, Toronto, ON, M6J 1H4, Canada. abigail.ortiz@utoronto.ca.
³ ORCA Quantum Computing, Toronto, ON, Canada.
⁴ Department of Electrical Engineering, University of Ottawa, Ottawa, ON, Canada.
⁵ Institute for Mental Health Research, The Royal Ottawa Hospital, Ottawa, ON, Canada.
⁶ Nova Scotia Health Authority, Halifax, NS, Canada.
⁷ Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
⁸ Centre for Addiction & Mental Health, CAMH 100 Stokes St., Rm 4229, Toronto, ON, M6J 1H4, Canada.
⁹ Department of Psychiatry, Dalhousie University, Halifax, NS, Canada.
¹⁰ National Institute of Mental Health, Klecany, Czech Republic.

Abstract

Background: 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. 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.

Results: There 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.

Conclusions: The 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.

Keywords: Bipolar disorder; Episode prediction; Mood fluctuations; Nonlinear analyses; Unaffected first-degree relatives.