Corrigendum to Nonlinear dynamics of mood regulation in unaffected first-degree relatives of bipolar disorder patients [Journal of Affective disorders 243 (2019) 274-279]

Ortiz, Abigail; Brádler, Kamil; Garnham, Julie; Slaney, Claire; MacLean, Stephane; Alda, Martin

doi:10.1016/j.jad.2018.10.103

Cited by 7 publications

(16 citation statements)

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“…With proportions of participants completing >84 days of consecutive entries of 96% in the HC group, 93.3% in the BD group, and 86.6% in the FDR groups, we analyzed 14,980 datapoints (5200 in the HC group, 4970 in the BD group and 4810 in the FDR group). Please refer to our original paper for the complete description of the sample and distribution of individual measures (Ortiz et al 2019 ).…”

Section: Resultsmentioning

confidence: 99%

“…As per our previous papers (Ortiz et al 2015 , 2019 ), we obtained data from 90 participants: 30 HC, 30 euthymic patients with BD, and 30 unaffected FDR. Euthymia was operationalized as at least 3 months of a Young Mania Rating Scale (YMRS) score ≤ 5 (Young et al 1978 ) and a Hamilton Depression Rating Scale (HDRS) score ≤ 7 (Hamilton 1960 ).…”

Section: Methodsmentioning

confidence: 99%

“…Its special feature is that successive observations are not independent, and that the analysis must take into account the time order of the observations. Using time-series analysis, we have recently described higher regularity—a “pathologically stable” or organized mood in patients with BD (Ortiz et al 2015 ) and their unaffected first-degree relatives (FDR) (Ortiz et al 2019 ). Other authors have also described that mood in patients with BD is a process that becomes more organized over a longer time scale, as opposed to processes in healthy controls (Gottschalk et al 1995 ; Bayani et al 2017 ; Bonsall et al 2012 ).…”

Section: Introductionmentioning

confidence: 99%

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

Ortiz

Brádler²,

Mowete

et al. 2021

Int J Bipolar Disord

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Ortiz

Brádler²,

Mowete

et al. 2021

Int J Bipolar Disord

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“…We briefly discuss measurement of heterogeneity in time-series data by indices often known as "complexity" measures. Psychiatric studies have employed geometric indices (such as the Largest Lyapunov Exponent and recurrence plot analysis) [130,131], entropic indices (such as Kolmogorov-Sinai or metric entropy [132], approximate entropy [133], sample and multiscale entropies [134][135][136], and Lempel-Ziv complexity [137,138]), and various fractal dimension indices to electrophysiological, functional neuroimaging [139], and other time-series [134,135,140]. Numerous clinical and technical reviews of these indices exist [13,133,[141][142][143][144][145], so we merely note that numbers equivalent can also be of use in this domain.…”

Section: A Note On Heterogeneity Indices For Time-series and Dynamicamentioning

confidence: 99%

“…For instance, our group has investigated the temporal dynamics of mood in patients with bipolar disorder. The overall complexity of mood fluctuations is ostensibly reduced among probands and their unaffected relatives [134,135], with increases observed within 60 days of a mood episode [140]. Unfortunately, on the whole it can be difficult to interpret time-series complexity between studies, since the large number of indices (each with their own units), experimental conditions, data modalities, and disorders can interact to yield various conclusions.…”

Section: A Note On Heterogeneity Indices For Time-series and Dynamicamentioning

confidence: 99%

The Definition and Measurement of Heterogeneity

Nunes¹,

Trappenberg²,

Alda³

2020

Preprint

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Heterogeneity is an important concept in psychiatric research and science more broadly. It negatively impacts effect size estimates under case-control paradigms, and it exposes important flaws in our existing categorical nosology. Yet, our field has no precise definition of heterogeneity proper. We tend to quantify heterogeneity by measuring associated correlates such as entropy or variance: practices which are akin to accepting the radius of a sphere as a measure of its volume. Under a definition of heterogeneity as the degree to which a system deviates from perfect conformity, this paper argues that its proper measure roughly corresponds to the size of a system’s event/sample space, and has units known as numbers equivalent. We arrive at this conclusion through focused review of more than 100 years of (re)discoveries of indices by ecologists, economists, statistical physicists, and others. In parallel, we review psychiatric approaches for quantifying heterogeneity, including but not limited to studies of symptom heterogeneity, microbiome biodiversity, cluster-counting, and time-series analyses. We argue that using numbers equivalent heterogeneity measures could improve the interpretability and synthesis of psychiatric research on heterogeneity. However, significant limitations must be overcome for these measures—largely developed for economic and ecological research—to be useful in modern translational psychiatric science.

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A critical evaluation of dynamical systems models of bipolar disorder

et al. 2022

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Bipolar disorder (BD) is a mood disorder involving recurring (hypo)manic and depressive episodes. The inherently temporal nature of BD has inspired its conceptualization using dynamical systems theory, which is a mathematical framework for understanding systems that evolve over time. In this paper, we provide a critical review of the dynamical systems models of BD. Owing to the heterogeneity of methodological and experimental designs in computational modeling, we designed a structured approach that parallels the appraisal of animal models by their face, predictive, and construct validity. This tool, the validity appraisal guide for computational models (VAG-CM), is not an absolute measure of validity, but rather a guide for a more objective appraisal of models in this review. We identified 26 studies published before November 18, 2021 that proposed generative dynamical systems models of time-varying signals in BD. Two raters independently applied the VAG-CM to the included studies, obtaining a mean Cohen’s κ of 0.55 (95% CI [0.45, 0.64]) prior to establishing consensus ratings. Consensus VAG-CM ratings revealed three model/study clusters: data-driven models with face validity, theory-driven models with predictive validity, and theory-driven models lacking all forms of validity. We conclude that future modeling studies should employ a hybrid approach that first operationalizes BD features of interest using empirical data to achieve face validity, followed by explanations of those features using generative models with components that are homologous to physiological or psychological systems involved in BD, to achieve construct validity. Such models would be best developed alongside long-term prospective cohort studies involving a collection of multimodal time-series data. We also encourage future studies to extend, modify, and evaluate the VAG-CM approach for a wider breadth of computational modeling studies and psychiatric disorders.

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Corrigendum to Nonlinear dynamics of mood regulation in unaffected first-degree relatives of bipolar disorder patients [Journal of Affective disorders 243 (2019) 274-279]

Cited by 7 publications

References 0 publications

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

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

The Definition and Measurement of Heterogeneity

A critical evaluation of dynamical systems models of bipolar disorder

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