scholarly journals Nonlinear time-series approaches in characterizing mood stability and mood instability in bipolar disorder

2011 ◽  
Vol 279 (1730) ◽  
pp. 916-924 ◽  
Author(s):  
M. B. Bonsall ◽  
S. M. A. Wallace-Hadrill ◽  
J. R. Geddes ◽  
G. M. Goodwin ◽  
E. A. Holmes
Keyword(s):  
Bipolar Disorder ◽  
Time Series ◽  
Depressive Symptomatology ◽  
Nonlinear Time Series ◽  
Mood Variability ◽  
Mood Instability ◽  
Time Series Approach ◽  
Longitudinal Pattern ◽  
Patient Groups ◽  
Deterministic Processes

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.

scholarly journals Nonlinear time-series approaches in characterizing mood stability and mood instability in bipolar disorder

2012 ◽  
Vol 279 (1742) ◽  
pp. 3632-3632 ◽  
Author(s):  
Michael B. Bonsall ◽  
Sophie M. A. Wallace-Hadrill ◽  
John R. Geddes ◽  
Guy M. Goodwin ◽  
Emily A. Holmes
Keyword(s):  
Bipolar Disorder ◽  
Time Series ◽  
Nonlinear Time Series ◽  
Mood Instability

scholarly journals Analysis of cyclical fish landings through ESTAR nonlinear time-series approach

2016 ◽  
Vol 63 (2) ◽  
Author(s):  
Bishal Gurung ◽  
K. N. Singh ◽  
. Prajneshu ◽  
Avnish Grover
Keyword(s):  
Time Series ◽  
Nonlinear Time Series ◽  
Time Series Approach

scholarly journals Associations between mood instability and emotional processing in a large cohort of bipolar patients

2016 ◽  
Vol 46 (15) ◽  
pp. 3151-3160 ◽  
Author(s):  
A. C. Bilderbeck ◽  
Z. E. Reed ◽  
H. C. McMahon ◽  
L. Z. Atkinson ◽  
J. Price ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Emotional Processing ◽  
Memory Task ◽  
Large Cohort ◽  
Mood Variability ◽  
Manic Symptoms ◽  
Mood Instability ◽  
Current Medication ◽  
Emotional Bias ◽  
Bipolar Patients

BackgroundAberrant emotional biases have been reported in bipolar disorder (BD), but results are inconsistent. Despite the clinical relevance of chronic mood variability in BD, there is no previous research investigating how the extent of symptom fluctuations in bipolar disorder might relate to emotional biases. This exploratory study investigated, in a large cohort of bipolar patients, whether instability in weekly mood episode symptoms and other clinical and demographic factors were related to emotional bias as measured in a simple laboratory task.MethodParticipants (N = 271, BDI = 206, BDII = 121) completed an ‘emotional categorization and memory’ task. Weekly self-reported symptoms of depression and mania were collected prospectively. In linear regression analyses, associations between cognitive bias and mood variability were explored together with the influence of demographic and clinical factors, including current medication.ResultsGreater accuracy in the classification of negative words relative to positive words was associated with greater instability in depressive symptoms. Furthermore, greater negative bias in free recall was associated with higher instability in manic symptoms. Participants diagnosed with BDII, compared with BDI, showed overall better word recognition and recall. Current antipsychotic use was associated with reduced instability in manic symptoms but this did not impact on emotional processing performance.ConclusionsEmotional processing biases in bipolar disorder are related to instability in mood. These findings prompt further investigation into the underpinnings as well as clinical significance of mood instability.

scholarly journals Analysis of cyclical fish landings through ESTAR nonlinear time-series approach

2016 ◽  
Vol 63 (2) ◽  
Author(s):  
Bishal Gurung ◽  
K. N. Singh ◽  
. Prajneshu ◽  
Avnish Grover
Keyword(s):  
Time Series ◽  
Nonlinear Time Series ◽  
Time Series Approach

Nonlinear Time Series Analysis with R

2018 ◽  
Author(s):  
Ray Huffaker ◽  
Marco Bittelli ◽  
Rodolfo Rosa
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Particle Physics ◽  
Linear Time ◽  
Nonlinear Time Series ◽  
Nonlinear Time Series Analysis ◽  
Series Analysis ◽  
Linear Behavior ◽  
Non Linear ◽  
Scientific Principles

In the process of data analysis, the investigator is often facing highly-volatile and random-appearing observed data. A vast body of literature shows that the assumption of underlying stochastic processes was not necessarily representing the nature of the processes under investigation and, when other tools were used, deterministic features emerged. Non Linear Time Series Analysis (NLTS) allows researchers to test whether observed volatility conceals systematic non linear behavior, and to rigorously characterize governing dynamics. Behavioral patterns detected by non linear time series analysis, along with scientific principles and other expert information, guide the specification of mechanistic models that serve to explain real-world behavior rather than merely reproducing it. Often there is a misconception regarding the complexity of the level of mathematics needed to understand and utilize the tools of NLTS (for instance Chaos theory). However, mathematics used in NLTS is much simpler than many other subjects of science, such as mathematical topology, relativity or particle physics. For this reason, the tools of NLTS have been confined and utilized mostly in the fields of mathematics and physics. However, many natural phenomena investigated I many fields have been revealing deterministic non linear structures. In this book we aim at presenting the theory and the empirical of NLTS to a broader audience, to make this very powerful area of science available to many scientific areas. This book targets students and professionals in physics, engineering, biology, agriculture, economy and social sciences as a textbook in Nonlinear Time Series Analysis (NLTS) using the R computer language.

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