Mood dynamics in bipolar disorder

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• 作者：Paul J Moore (1)
  Max A Little (2)
  Patrick E McSharry (3)
  Guy M Goodwin (4)
  John R Geddes (4)
  
  1. Mathematical Institute ; University of Oxford ; Woodstock Road ; Oxford ; OX2 6GG ; UK
  2. Aston University ; Birmingham ; B4 7ET ; UK
  3. School of Geography and the Environment ; University of Oxford ; South Parks Road ; Oxford ; OX1 3QY ; UK
  4. Department of Psychiatry ; University of Oxford ; Oxford ; OX3 7JX ; UK
  
• 关键词：Bipolar disorder ; Mood dynamics ; Time series analysis ; Public healthcare
• 刊名：International Journal of Bipolar Disorders
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：2
• 期：1
• 全文大小：443 KB
• 参考文献：Diagnostic and statistical manual of mental disorders: DSM- IV-TR. American Psychiatric Association, Washington, DC.
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• 刊物主题：Psychiatry; Psychotherapy; Psychopharmacology; Behavioral Therapy; Neurology; Clinical Psychology;
• 出版者：Springer Berlin Heidelberg
• ISSN：2194-7511

文摘

The nature of mood variation in bipolar disorder has been the subject of relatively little research because detailed time series data has been difficult to obtain until recently. However some papers have addressed the subject and claimed the presence of deterministic chaos and of stochastic nonlinear dynamics. This study uses mood data collected from eight outpatients using a telemonitoring system. The nature of mood dynamics in bipolar disorder is investigated using surrogate data techniques and nonlinear forecasting. For the surrogate data analysis, forecast error and time reversal asymmetry statistics are used. The original time series cannot be distinguished from their linear surrogates when using nonlinear test statistics, nor is there an improvement in forecast error for nonlinear over linear forecasting methods. Nonlinear sample forecasting methods have no advantage over linear methods in out-of-sample forecasting for time series sampled on a weekly basis. These results can mean that either the original series have linear dynamics, the test statistics for distinguishing linear from nonlinear behaviour do not have the power to detect the kind of nonlinearity present, or the process is nonlinear but the sampling is inadequate to represent the dynamics. We suggest that further studies should apply similar techniques to more frequently sampled data.