The futility of long-term predictions in bipolar disorder: mood fluctuations are the result of deterministic chaotic processes
Status PubMed-not-MEDLINE Jazyk angličtina Země Německo Médium electronic
Typ dokumentu časopisecké články
PubMed
34596784
PubMed Central
PMC8486895
DOI
10.1186/s40345-021-00235-3
PII: 10.1186/s40345-021-00235-3
Knihovny.cz E-zdroje
- Klíčová slova
- Bipolar disorder, Episode prediction, Mood fluctuations, Nonlinear analyses, Unaffected first-degree relatives,
- Publikační typ
- časopisecké články MeSH
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.
Centre for Addiction and Mental Health CAMH 100 Stokes St Rm 4229 Toronto ON M6J 1H4 Canada
Department of Electrical Engineering University of Ottawa Ottawa ON Canada
Department of Psychiatry Dalhousie University Halifax NS Canada
Department of Psychiatry University of Toronto Toronto ON Canada
Institute for Mental Health Research The Royal Ottawa Hospital Ottawa ON Canada
National Institute of Mental Health Klecany Czech Republic
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Bayani A, Hadaeghi F, Jafari S, Murray G. Critical slowing down as an early warning of transitions in episodes of bipolar disorder: a simulation study based on a computational model of circadian activity rhythms. Chronobiol Int. 2017;34(2):235–245. doi: 10.1080/07420528.2016.1272608. PubMed DOI
Bonsall MB, Wallace-Hadrill SM, Geddes JR, Goodwin GM, Holmes EA. Nonlinear time-series approaches in characterizing mood stability and mood instability in bipolar disorder. Proc Biol Sci. 2012;279(1730):916–924. PubMed PMC
Bonsall MB, Geddes JR, Goodwin GM, Holmes EA. Bipolar disorder dynamics: affective instabilities, relaxation oscillations and noise. J R Soc Interface. 2015;12(112):20150670. doi: 10.1098/rsif.2015.0670. PubMed DOI PMC
Chatfield C. The analysis of time series: an introduction. Boca Raton: CRC Press; 2016.
Clements CF, Ozgul A. Indicators of transitions in biological systems. Ecol Lett. 2018;21(6):905–919. doi: 10.1111/ele.12948. PubMed DOI
Cochran AL, Schultz A, McInnis MG, Forger DB. Testing frameworks for personalizing bipolar disorder. Transl Psychiatry. 2018;8(1):36. doi: 10.1038/s41398-017-0084-4. PubMed DOI PMC
Cowdry RW, Gardner DL, O'Leary KM, Leibenluft E, Rubinow DR. Mood variability: a study of four groups. Am J Psychiatry. 1991;148(11):1505–1511. doi: 10.1176/ajp.148.11.1505. PubMed DOI
Doyle TLA, Dugan EL, Humphries B, Newton RU. Discriminating between elderly and young using a fractal dimension analysis of centre of pressure. Int J Med Sci. 2004;1(1):11–20. doi: 10.7150/ijms.1.11. PubMed DOI PMC
Ehlers CL. Chaos and complexity. Can it help us to understand mood and behavior? Arch General Psychiatry. 1995;52(11):960–4. doi: 10.1001/archpsyc.1995.03950230074010. PubMed DOI
Endicott J, Spitzer RL. A diagnostic interview: the schedule for affective disorders and schizophrenia. Arch Gen Psychiatry. 1978;35(7):837–844. doi: 10.1001/archpsyc.1978.01770310043002. PubMed DOI
Gao J, Gao Y, Tung WW, Hu J. Multiscale analysis of complex time series: integration of chaos and random fractal theory. Hoboken: Wiley; 2007.
Goldberger AL, Amaral LA, Hausdorff JM, Ivanov P, Peng CK, Stanley HE. Fractal dynamics in physiology: alterations with disease and aging. Proc Natl Acad Sci USA. 2002;99(Suppl 1):2466–2472. doi: 10.1073/pnas.012579499. PubMed DOI PMC
Goldberger AL, Peng CK, Lipsitz LA. What is physiologic complexity and how does it change with aging and disease? Neurobiol Aging. 2002;23(1):23–26. doi: 10.1016/S0197-4580(01)00266-4. PubMed DOI
Golier JA, Yehuda R, Schmeidler J, Siever LJ. Variability and severity of depression and anxiety in post traumatic stress disorder and major depressive disorder. Depress Anxiety. 2001;13(2):97–100. doi: 10.1002/da.1022. PubMed DOI
Gomez C, Mediavilla A, Hornero R, Abasolo D, Fernandez A. Use of the Higuchi's fractal dimension for the analysis of MEG recordings from Alzheimer's disease patients. Med Eng Phys. 2009;31(3):306–313. doi: 10.1016/j.medengphy.2008.06.010. PubMed DOI
Gomolka RS, Kampusch S, Kaniusas E, Thurk F, Szeles JC, Klonowski W. Higuchi fractal dimension of heart rate variability during percutaneous auricular vagus nerve stimulation in healthy and diabetic subjects. Front Physiol. 2018;9:1162. doi: 10.3389/fphys.2018.01162. PubMed DOI PMC
Gottschalk A, Bauer MS, Whybrow PC. Evidence of chaotic mood variation in bipolar disorder. Arch Gen Psychiatry. 1995;52(11):947–959. doi: 10.1001/archpsyc.1995.03950230061009. PubMed DOI
Hamilton M. A rating scale for depression. J Neurol Neurosurg Psychiatry. 1960;23:56–62. doi: 10.1136/jnnp.23.1.56. PubMed DOI PMC
Higuchi T. Approach to an irregular time series on the basis of the fractal theory. Physica D. 1988;31(2):277–283. doi: 10.1016/0167-2789(88)90081-4. DOI
Hu K, Ivanov PC, Chen Z, Carpena P, Stanley HE. Effect of trends on detrended fluctuation analysis. Phys Rev E Stat Nonlin Soft Matter Phys. 2001;64(1 Pt 1):011114. doi: 10.1103/PhysRevE.64.011114. PubMed DOI
Hu J, Gao J, Tung WW. Characterizing heart rate variability by scale-dependent Lyapunov exponent. Chaos. 2009;19(2):028506. doi: 10.1063/1.3152007. PubMed DOI
Hu J, Gao J, Tung WW, Cao Y. Multiscale analysis of heart rate variability: a comparison of different complexity measures. Ann Biomed Eng. 2010;38(3):854–864. doi: 10.1007/s10439-009-9863-2. PubMed DOI
Huber MT, Braun HA, Krieg JC. Consequences of deterministic and random dynamics for the course of affective disorders. Biol Psychiatry. 1999;46(2):256–262. doi: 10.1016/S0006-3223(98)00311-4. PubMed DOI
Huber MT, Braun HA, Krieg JC. Effects of noise on different disease states of recurrent affective disorders. Biol Psychiatry. 2000;47(7):634–642. doi: 10.1016/S0006-3223(99)00174-2. PubMed DOI
Jelinek HF, Md Imam H, Al-Aubaidy H, Khandoker AH. Association of cardiovascular risk using non-linear heart rate variability measures with the framingham risk score in a rural population. Front Physiol. 2013;4:186. PubMed PMC
Katerndahl D, Ferrer R, Best R, Wang CP. Dynamic patterns in mood among newly diagnosed patients with major depressive episode or panic disorder and normal controls. Prim Care Companion J Clin Psychiatry. 2007;9(3):183–187. doi: 10.4088/PCC.v09n0303. PubMed DOI PMC
Katz MJ. Fractals and the analysis of waveforms. Comput Biol Med. 1988;18(3):145–156. doi: 10.1016/0010-4825(88)90041-8. PubMed DOI
Kesic S, Spasic SZ. Application of Higuchi's fractal dimension from basic to clinical neurophysiology: a review. Comput Methods Programs Biomed. 2016;133:55–70. doi: 10.1016/j.cmpb.2016.05.014. PubMed DOI
Klonowski W. From conformons to human brains: an informal overview of nonlinear dynamics and its applications in biomedicine. Nonlinear Biomed Phys. 2007;1(1):5. doi: 10.1186/1753-4631-1-5. PubMed DOI PMC
Kossakowski JJ, Gordijn MCM, Riese H, Waldorp LJ. Applying a dynamical systems model and network theory to major depressive disorder. Front Psychol. 2019;10:1762. doi: 10.3389/fpsyg.2019.01762. PubMed DOI PMC
Lagro J, Laurenssen NC, Schalk BW, Schoon Y, Claassen JA, Olde Rikkert MG. Diastolic blood pressure drop after standing as a clinical sign for increased mortality in older falls clinic patients. J Hypertens. 2012;30(6):1195–1202. doi: 10.1097/HJH.0b013e328352b9fd. PubMed DOI
Ma Y, Shi W, Peng CK, Yang AC. Nonlinear dynamical analysis of sleep electroencephalography using fractal and entropy approaches. Sleep Med Rev. 2018;37:85–93. doi: 10.1016/j.smrv.2017.01.003. PubMed DOI
Nayak SK, Bit A, Dey A, Mohapatra B, Pal K. A review on the nonlinear dynamical system analysis of electrocardiogram signal. J Healthc Eng. 2018;2018:6920420. doi: 10.1155/2018/6920420. PubMed DOI PMC
Nelson B, McGorry PD, Wichers M, Wigman JTW, Hartmann JA. Moving from static to dynamic models of the onset of mental disorder: a review. JAMA Psychiat. 2017;74(5):528–534. doi: 10.1001/jamapsychiatry.2017.0001. PubMed DOI
Olde Rikkert MG, Dakos V, Buchman TG, Boer R, Glass L, Cramer AO, et al. Slowing down of recovery as generic risk marker for acute severity transitions in chronic diseases. Crit Care Med. 2016;44(3):601–606. doi: 10.1097/CCM.0000000000001564. PubMed DOI
O'Regan SM, Burton DL. How stochasticity influences leading indicators of critical transitions. Bull Math Biol. 2018;80(6):1630–1654. doi: 10.1007/s11538-018-0429-z. PubMed DOI
Ortiz A, Alda M. The perils of being too stable: mood regulation in bipolar disorder. J Psychiatry Neurosci. 2018;43(6):363–365. doi: 10.1503/jpn.180183. PubMed DOI PMC
Ortiz A, Bradler K, Garnham J, Slaney C, Alda M. Nonlinear dynamics of mood regulation in bipolar disorder. Bipolar Disord. 2015;17(2):139–149. doi: 10.1111/bdi.12246. PubMed DOI
Ortiz A, Bradler K, Hintze A. Episode forecasting in bipolar disorder: is energy better than mood? Bipolar Disord. 2018;20(5):470–476. doi: 10.1111/bdi.12603. PubMed DOI
Ortiz A, Bradler K, Garnham J, Slaney C, MacLean S, Alda M. Corrigendum to nonlinear dynamics of mood regulation in unaffected first-degree relatives of bipolar disorder patients [Journal of Affective disorders 243 (2019) 274–279] J Affect Disord. 2019;245:16. doi: 10.1016/j.jad.2018.10.103. PubMed DOI
Ortiz A, Maslej MM, Husain I, Daskalakis J, Mulsant BH. Apps and gaps in bipolar disorder: a systematic review on electronic monitoring for episode prediction. J Affect Disord. 2021;295:1190–1200. doi: 10.1016/j.jad.2021.08.140. PubMed DOI
Peng CK, Buldyrev SV, Havlin S, Simons M, Stanley HE, Goldberger AL. Mosaic organization of DNA nucleotides. Phys Rev E. 1994;49(2):1685–1689. doi: 10.1103/PhysRevE.49.1685. PubMed DOI
Peng CK, Havlin S, Stanley HE, Goldberger AL. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos. 1995;5(1):82–87. doi: 10.1063/1.166141. PubMed DOI
Peng CK, Havlin S, Hausdorff JM, Mietus JE, Stanley HE, Goldberger AL. Fractal mechanisms and heart rate dynamics Long-range correlations and their breakdown with disease. J Electrocardiol. 1995;28:59–65. doi: 10.1016/S0022-0736(95)80017-4. PubMed DOI
Pincus S. Assessing serial irregularity and its implications for health. Ann N Y Acad Sci. 2001;954:245–267. doi: 10.1111/j.1749-6632.2001.tb02755.x. PubMed DOI
Pincus SM, Schmidt PJ, Palladino-Negro P, Rubinow DR. Differentiation of women with premenstrual dysphoric disorder, recurrent brief depression, and healthy controls by daily mood rating dynamics. J Psychiatr Res. 2008;42(5):337–347. doi: 10.1016/j.jpsychires.2007.01.001. PubMed DOI
Quail T, Shrier A, Glass L. Predicting the onset of period-doubling bifurcations in noisy cardiac systems. Proc Natl Acad Sci USA. 2015;112(30):9358–9363. doi: 10.1073/pnas.1424320112. PubMed DOI PMC
Raghavendra BS, Dutt DN. Signal characterization using fractal dimension. Fractals. 2010;18(3):287–92. doi: 10.1142/S0218348X10004968. DOI
Rosenstein MT, Collins JJ, De Luca CJ. A practical method for calculating largest Lyapunov exponents from small data sets. Physica D. 1993;65(1):117–134. doi: 10.1016/0167-2789(93)90009-P. DOI
Scheffer M, Carpenter S, Foley JA, Folke C, Walker B. Catastrophic shifts in ecosystems. Nature. 2001;413(6856):591–596. doi: 10.1038/35098000. PubMed DOI
Scheffer M, Bascompte J, Brock WA, Brovkin V, Carpenter SR, Dakos V, et al. Early-warning signals for critical transitions. Nature. 2009;461(7260):53–59. doi: 10.1038/nature08227. PubMed DOI
Scheffer M, Bolhuis JE, Borsboom D, Buchman TG, Gijzel SMW, Goulson D, et al. Quantifying resilience of humans and other animals. Proc Natl Acad Sci USA. 2018;115(47):11883–11890. doi: 10.1073/pnas.1810630115. PubMed DOI PMC
Skinner JE, Molnar M, Vybiral T, Mitra M. Application of chaos theory to biology and medicine. Integr Physiol Behav Sci. 1992;27(1):39–53. doi: 10.1007/BF02691091. PubMed DOI
van de Leemput IA, Wichers M, Cramer AO, Borsboom D, Tuerlinckx F, Kuppens P, et al. Critical slowing down as early warning for the onset and termination of depression. Proc Natl Acad Sci USA. 2014;111(1):87–92. doi: 10.1073/pnas.1312114110. PubMed DOI PMC
van der Werf SY, Kaptein KI, de Jonge P, Spijker J, de Graaf R, Korf J. Major depressive episodes and random mood. Arch Gen Psychiatry. 2006;63(5):509–518. doi: 10.1001/archpsyc.63.5.509. PubMed DOI
van Nes EH, Arani BMS, Staal A, van der Bolt B, Flores BM, Bathiany S, et al. What do you mean, 'tipping point'? Trends Ecol Evol. 2016;31(12):902–904. doi: 10.1016/j.tree.2016.09.011. PubMed DOI
Varshney LR, Sun JZ. Why do we perceive logarithmically? Significance. 2013;10(1):28–31. doi: 10.1111/j.1740-9713.2013.00636.x. DOI
Wu HT, Liu CC, Lo MT, Hsu PC, Liu AB, Chang KY, et al. Multiscale cross-approximate entropy analysis as a measure of complexity among the aged and diabetic. Comput Math Methods in Med. 2013;2013:324325. PubMed PMC
Young RC, Biggs JT, Ziegler VE, Meyer DA. A rating scale for mania: reliability, validity and sensitivity. Br J Psychiatry. 1978;133:429–435. doi: 10.1192/bjp.133.5.429. PubMed DOI
Zhong Y, Jan KM, Ju KH, Chon KH. Representation of time-varying nonlinear systems with time-varying principal dynamic modes. IEEE Trans Biomed Eng. 2007;54(11):1983–1992. doi: 10.1109/TBME.2007.895748. PubMed DOI
Mood regulation in euthymic patients with a history of antidepressant-induced mania