Modeling Unipolar Depression as a Chaotic Process.

Heiby, Elaine M.; Pagano, Ian; Blaine, Daniel D.; Nelson, Karl; Heath, Rachel A.

doi:10.1037/1040-3590.15.3.426

Cited by 28 publications

(31 citation statements)

References 30 publications

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“…One research group [27] studied mood ratings in depression and found a possible underlying chaotic process. A different group [28] studied mood ratings in affective instability and found that the patients showed more variability than controls on the Mean Squared Successive Difference, while the value of the Fractal Dimension was reduced.…”

Section: Discussionmentioning

confidence: 99%

Nonlinear Analysis of Motor Activity Shows Differences between Schizophrenia and Depression: A Study Using Fourier Analysis and Sample Entropy

Hauge¹,

et al. 2011

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The purpose of this study has been to describe motor activity data obtained by using wrist-worn actigraphs in patients with schizophrenia and major depression by the use of linear and non-linear methods of analysis. Different time frames were investigated, i.e., activity counts measured every minute for up to five hours and activity counts made hourly for up to two weeks. The results show that motor activity was lower in the schizophrenic patients and in patients with major depression, compared to controls. Using one minute intervals the depressed patients had a higher standard deviation (SD) compared to both the schizophrenic patients and the controls. The ratio between the root mean square successive differences (RMSSD) and SD was higher in the schizophrenic patients compared to controls. The Fourier analysis of the activity counts measured every minute showed that the relation between variance in the low and the high frequency range was lower in the schizophrenic patients compared to the controls. The sample entropy was higher in the schizophrenic patients compared to controls in the time series from the activity counts made every minute. The main conclusions of the study are that schizophrenic and depressive patients have distinctly different profiles of motor activity and that the results differ according to period length analysed.

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Section: Discussionmentioning

confidence: 99%

Nonlinear Analysis of Motor Activity Shows Differences between Schizophrenia and Depression: A Study Using Fourier Analysis and Sample Entropy

Hauge¹,

et al. 2011

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“…There has been a tendency over the past decade to interpret individual self-recorded mood sequences in terms of chaos theory, which would suggest that the time development of mood might be deterministic (Gottschalk et al 1995(Gottschalk et al , 1998Heiby et al 2003). The random-mood model takes an opposite point of view and has been shown to be capable of reproducing the same mood-sequence characteristics (van der Werf et al 2006).…”

Section: Introductionmentioning

confidence: 99%

Random-mood interpretation of determinants for major depression

et al. 2007

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“…Recently, however, this common-cause view has been challenged (7-9). The alternative view is that the correlated symptoms should be regarded as the result of interactions of components of a complex dynamical system (7,(10)(11)(12). Consequently, new models of the etiology of depression involve a network of interactions between components, such as emotions, cognitions, and behaviors (8,9).…”

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confidence: 99%

Critical slowing down as early warning for the onset and termination of depression

Leemput

Wichers

Cramer

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

629

577

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About 17% of humanity goes through an episode of major depression at some point in their lifetime. Despite the enormous societal costs of this incapacitating disorder, it is largely unknown how the likelihood of falling into a depressive episode can be assessed. Here, we show for a large group of healthy individuals and patients that the probability of an upcoming shift between a depressed and a normal state is related to elevated temporal autocorrelation, variance, and correlation between emotions in fluctuations of autorecorded emotions. These are indicators of the general phenomenon of critical slowing down, which is expected to occur when a system approaches a tipping point. Our results support the hypothesis that mood may have alternative stable states separated by tipping points, and suggest an approach for assessing the likelihood of transitions into and out of depression. D epression is one of the main mental health hazards of our time. It can be viewed as a continuum with an absence of depressive symptoms at the low endpoint and severe and debilitating complaints at the high end (1). (Throughout this manuscript, the term "depression" refers to this continuum of depressive symptoms.) The diagnosis major depressive disorder (MDD) defines individuals at the high end of this continuum. Approximately 10-20% (2) of the general population will experience at least one episode of MDD during their lives, but even subclinical levels of depression may considerably reduce quality of life and work productivity (3). Depressive symptoms are therefore associated with substantial personal and societal costs (4,5). The onset of MDD in an individual can be quite abrupt, and similarly rapid shifts from depression into a remitted state, so-called sudden gains, are common (6). However, despite the high prevalence and associated societal costs of depression, we have little insight into how such critical transitions from health to depression (and vice versa) in individuals might be foreseen. Traditionally, the broad array of correlated symptoms found in depressed people (e.g., depressed mood, insomnia, fatigue, concentration problems, loss of interest, suicidal ideation, etc.) was thought to stem from some common cause, much as a lung tumor is the common cause of symptoms such as shortness of breath, chest pain, and coughing up blood. Recently, however, this common-cause view has been challenged (7-9). The alternative view is that the correlated symptoms should be regarded as the result of interactions of components of a complex dynamical system (7,(10)(11)(12). Consequently, new models of the etiology of depression involve a network of interactions between components, such as emotions, cognitions, and behaviors (8,9). This implies, for instance, that a person may become depressed through a causal chain of feelings and experiences, such as the following: stress → negative emotions → sleep problems → anhedonia (9, 13-15). However, the network view also implies that there can be positive feedback mechanisms between symptoms, such...

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Modeling Unipolar Depression as a Chaotic Process.

Cited by 28 publications

References 30 publications

Nonlinear Analysis of Motor Activity Shows Differences between Schizophrenia and Depression: A Study Using Fourier Analysis and Sample Entropy

Nonlinear Analysis of Motor Activity Shows Differences between Schizophrenia and Depression: A Study Using Fourier Analysis and Sample Entropy

Random-mood interpretation of determinants for major depression

Critical slowing down as early warning for the onset and termination of depression

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