Approximate entropy of self-reported mood prior to episodes in bipolar disorder
University of Cologne, Köln, North Rhine-Westphalia, Germany Bipolar Disorders
(Impact Factor: 4.97).
11/2006; 8(5 Pt 1):424-9. DOI: 10.1111/j.1399-5618.2006.00373.x
Approximate entropy (ApEn) measures regularity in time series data, while traditional linear statistics measure variability. Using self-reported mood data from patients with bipolar disorder, this preliminary study addressed whether ApEn could distinguish (i) the 60 days prior to the start of a manic or depressed episode from the 60 days prior to a month of euthymia, and (ii) the 60 days prior to a manic episode from the 60 days prior to a depressed episode.
Self-reported mood data from 49 outpatients with bipolar disorder receiving standard treatment were analysed. The data contained 27 episodes (12 manic and 15 depressed), and 43 periods of 1 month of euthymia. For the 60 days prior to episode or euthymia, the ApEn, linear statistics and the correlation between linear and non-linear measures were calculated.
ApEn was significantly greater in the 60 days prior to a manic or depressive episode than the 60 days prior to a month of euthymia. The onset of an episode was associated with greater irregularity in mood. Variability was also significantly larger and correlated with ApEn. ApEn was significantly greater in the 60 days prior to a manic episode than in the 60 days prior to a depressed episode, whereas measures of variability were not significantly different. Mood in the 60 days prior to mania was more irregular than prior to depression.
Non-linear measures may complement traditional linear measures in the analysis of longitudinal data in bipolar disorder. A larger study is indicated.
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Available from: Albert Yang
- "Schizophrenia (Koukkou et al., 1993) EEG Dimensional complexity Increased dimensional complexity (Roschke et al., 1995) EEG Lyapunov exponent Increased fractal complexity (Paulus et al., 1996) Consecutive binary choice task Dynamical entropy Both fixed and random behavioral sequence responses. (Hoffmann et al., 1996) EEG Dimensional complexity Reduced dimensional complexity (Na et al., 2002) EEG Mutual information analysis Reduced complexity (Mujica-Parodi et al., 2005) ECG Symbolic dynamic complexity Reduced symbolic dynamic complexity (Bar et al., 2007) ECG Approximate entropy Reduced approximate entropy (Li et al., 2008) EEG Lempel–Ziv complexity Increased Lempel–Ziv complexity (Takahashi et al., 2010) EEG Multiscale entropy Increased multiscale entropy for long-term scales (Fernandez et al., 2011) MEG Lempel–Ziv complexity Increased Lempel–Ziv complexity Mood disorder (Nandrino et al., 1994) EEG Nonlinear forecasting Reduced complexity (Gottschalk et al., 1995) Daily self-rated mood Correlation dimension Low-dimensional chaotic process (Glenn et al., 2006) Daily self-rated mood Approximate entropy Increased approximate entropy 60 days before a mood episode (Leistedt et al., 2011) ECG Multiscale entropy Reduced heart rate complexity (Mendez et al., 2012) MEG Lempel–Ziv complexity Increased Lempel–Ziv complexity Anxiety disorder (Srinivasan et al., 2002) ECG Lyapunov exponent Reduced fractal complexity (Caldirola et al., 2004) Respiration Approximate entropy Increased approximate entropy for respiratory parameters (Chae et al., 2004) EEG Lyapunov exponent Reduced fractal complexity Dementia (Escudero et al., 2006; Mizuno et al., 2010) EEG Multiscale entropy Decreased and increased multiscale entropy in short-and long-term scales, respectively (Fernandez et al., 2010b) MEG Lempel–Ziv complexity Reduced Lempel–Ziv complexity Autism (Lai et al., 2011) Resting fMRI Hurst exponent Shift to randomness Attention-deficit/hyperactivity disorder (Fernandez et al., 2009) MEG Lempel–Ziv complexity Reduced Lempel–Ziv complexity Sleep disorder (Yang et al., 2011) ECG Multiscale entropy Reduced multiscale entropy during sleep period regions related to social, motor organization, and connection hubs (Lai et al., 2011). We have recently applied the MSE complexity method to the analysis of blood oxygen level dependent (BOLD) signals obtained from resting state fMRI in older and younger normal adults, and found that complexity of BOLD signals was reduced in older adults, compared with younger people (Yang et al., 2012). "
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ABSTRACT: A defining but elusive feature of the human brain is its astonishing complexity. This complexity arises from the interaction of numerous neuronal circuits that operate over a wide range of temporal and spatial scales, enabling the brain to adapt to the constantly changing environment and to perform various amazing mental functions. In mentally ill patients, such adaptability is often impaired, leading to either ordered or random patterns of behavior. Quantification and classification of these abnormal human behaviors exhibited during mental illness is one of the major challenges of contemporary psychiatric medicine. In the past few decades, attempts have been made to apply concepts adopted from complexity science to better understand complex human behavior. Although considerable effort has been devoted to studying the abnormal dynamic processes involved in mental illness, unfortunately, the primary features of complexity science are typically presented in a form suitable for mathematicians, physicists, and engineers; thus, they are difficult for practicing psychiatrists or neuroscientists to comprehend. Therefore, this paper introduces recent applications of methods derived from complexity science for examining mental illness. We propose that the complexity of mental illness can be studied under a general framework by quantifying the order and randomness of dynamic macroscopic human behavior and microscopic neuronal activity. Additionally, substantial effort is required to identify the link between macroscopic behaviors and microscopic changes in the neuronal dynamics within the brain.
Progress in Neuro-Psychopharmacology and Biological Psychiatry 10/2012; 45. DOI:10.1016/j.pnpbp.2012.09.015 · 3.69 Impact Factor
Available from: Emily Holmes
- "However, given the temporal nature of this sort of data, here we develop specific hypotheses based on these time-series fluctuations with the aim of statistically understanding mood variability. Several studies suggest that many psychiatric conditions, including bipolar disorder are nonlinear over time [31,32]. Thus, change in mood cannot simply be predicted from simple linear relationships and developing methods for the analysis of longitudinal patient data will complement information from traditional statistical analyses . "
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ABSTRACT: Bipolar disorder is a psychiatric condition characterized by episodes of elevated mood interspersed with episodes of depression. While treatment developments and understanding the disruptive nature of this illness have focused on these episodes, it is also evident that some patients may have chronic week-to-week mood instability. This is also a major morbidity. The longitudinal pattern of this mood instability is poorly understood as it has, until recently, been difficult to quantify. We propose that understanding this mood variability is critical for the development of cognitive neuroscience-based treatments. In this study, we develop a time-series approach to capture mood variability in two groups of patients with bipolar disorder who appear on the basis of clinical judgement to show relatively stable or unstable illness courses. Using weekly mood scores based on a self-rated scale (quick inventory of depressive symptomatology-self-rated; QIDS-SR) from 23 patients over a 220-week period, we show that the observed mood variability is nonlinear and that the stable and unstable patient groups are described by different nonlinear time-series processes. We emphasize the necessity in combining both appropriate measures of the underlying deterministic processes (the QIDS-SR score) and noise (uncharacterized temporal variation) in understanding dynamical patterns of mood variability associated with bipolar disorder.
Proceedings of the Royal Society B: Biological Sciences 08/2011; 279(1730):916-24. DOI:10.1098/rspb.2011.1246 · 5.05 Impact Factor
Available from: Jen-Chuen Hsieh
- "One is to analyze the time course of self-report mood data. For instance, Glen et al. calculated the approximate entropy of self-reported mood in bipolar patients and found that approximate entropy was significantly greater in the 60 days prior to a manic or depressive episode than the 60 days prior to a month of euthymia . This result implicated that irregularity in mood could be viewed as an indicator of onset of an episode. "
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ABSTRACT: Recent functional imaging studies demonstrated that brain exhibit coherent, synchronized activities during resting state and the dynamics may be impaired in various psychiatric illnesses. In order to investigate the change of neural dynamics in bipolar disorder, we used a new nonlinear measurement "similarity index" to analyze the magnetoencephalography (MEG) recordings and test the hypothesis that there are synchronization changes within different frequency bands in the frontal cortex of patients with bipolar disorder. Ten patients with bipolar I disorder during euthymic phase and ten normal controls underwent 2min eye-closed resting recording with a whole-head 306-channel MEG system. Eleven channels of MEG data from frontal area were selected for analysis. Synchronization level in the delta (2-4Hz), theta (4-8Hz), alpha (8-12Hz) and beta (12-24Hz) bands was calculated for each subject and compared across group. The results showed that significant dynamic changes in bipolar patients can be characterized by increased synchronization of slow frequency oscillations (delta) and decreased synchronization of fast frequency oscillations (beta). Furthermore, the positive correlation between beta synchronization level and preservative errors in Wisconcin card sorting task was found which would implicate the deficit of executive function in bipolar patients. Our findings indicate that analysis of spontaneous MEG recordings at resting state using nonlinear dynamic approaches may disclose the subtle regional changes of neural dynamics in BD.
Neuroscience Letters 10/2008; 445(2):174-8. DOI:10.1016/j.neulet.2008.08.080 · 2.03 Impact Factor
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