Graph theory applied to the analysis of motor activity in patients with schizophrenia and depression

Depression and schizophrenia are defined only by their clinical features, and diagnostic separation between them can be difficult. Disturbances in motor activity pattern are central features of both types of disorders. We introduce a new method to analyze time series, called the similarity graph algorithm. Time series of motor activity, obtained from actigraph registrations over 12 days in depressed and schizophrenic patients, were mapped into a graph and we then applied techniques from graph theory to characterize these time series, primarily looking for changes in complexity. The most marked finding was that depressed patients were found to be significantly different from both controls and schizophrenic patients, with evidence of less regularity of the time series, when analyzing the recordings with one hour intervals. These findings support the contention that there are important differences in control systems regulating motor behavior in patients with depression and schizophrenia. The similarity graph algorithm we have described can easily be applied to the study of other types of time series.

[1]  Lucas Lacasa,et al.  Feigenbaum Graphs: A Complex Network Perspective of Chaos , 2011, PloS one.

[2]  Paul M. Thompson,et al.  Support Vector Machine Classification of Major Depressive Disorder Using Diffusion-Weighted Neuroimaging and Graph Theory , 2015, Front. Psychiatry.

[3]  M. Åsberg,et al.  A New Depression Scale Designed to be Sensitive to Change , 1979, British Journal of Psychiatry.

[4]  Ole Bernt Fasmer,et al.  Distribution of Active and Resting Periods in the Motor Activity of Patients with Depression and Schizophrenia , 2015, Psychiatry investigation.

[5]  Maria Wolters,et al.  Activity monitoring in patients with depression: a systematic review. , 2013, Journal of affective disorders.

[6]  T. Pozzo,et al.  Psychomotor Retardation in Depression: A Systematic Review of Diagnostic, Pathophysiologic, and Therapeutic Implications , 2013, BioMed research international.

[7]  I. Hickie,et al.  Circadian rhythmicity in emerging mood disorders: state or trait marker? , 2016, International Journal of Bipolar Disorders.

[8]  J. C. Nuño,et al.  The visibility graph: A new method for estimating the Hurst exponent of fractional Brownian motion , 2009, 0901.0888.

[9]  D F Swaab,et al.  Bright light therapy: improved sensitivity to its effects on rest-activity rhythms in Alzheimer patients by application of nonparametric methods. , 1999, Chronobiology international.

[10]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[11]  H. Sackeim,et al.  Psychomotor symptoms of depression. , 1997, The American journal of psychiatry.

[12]  H. Yim,et al.  The Clinical Research Center for Depression Study: Baseline Characteristics of a Korean Long-Term Hospital-Based Observational Collaborative Prospective Cohort Study , 2010, Psychiatry investigation.

[13]  Lucas Lacasa,et al.  From time series to complex networks: The visibility graph , 2008, Proceedings of the National Academy of Sciences.

[14]  Ian Anderson.  A first course in discrete mathematics , 2001, Springer undergraduate mathematics series.

[15]  W. Strik,et al.  The Longitudinal Course of Gross Motor Activity in Schizophrenia – Within and between Episodes , 2015, Front. Psychiatry.

[16]  J. Overall,et al.  The Brief Psychiatric Rating Scale , 1962 .

[17]  Ole Bernt Fasmer,et al.  Actigraphic Assessment of Motor Activity in Acutely Admitted Inpatients with Bipolar Disorder , 2014, PloS one.

[18]  Fred Holsten,et al.  Actigraphic registration of motor activity reveals a more structured behavioural pattern in schizophrenia than in major depression , 2010, BMC Research Notes.

[19]  P. Thomann,et al.  Motor dysfunction within the schizophrenia-spectrum: A dimensional step towards an underappreciated domain , 2015, Schizophrenia Research.

[20]  R. Roos,et al.  Where to record motor activity: an evaluation of commonly used sites of placement for activity monitors. , 1993, Electroencephalography and clinical neurophysiology.

[21]  U. Hegerl,et al.  Early report on brain arousal regulation in manic vs depressive episodes in bipolar disorder , 2016, Bipolar disorders.

[22]  Eileen M. Joyce,et al.  Sleep and circadian rhythm disruption in schizophrenia† , 2012, The British journal of psychiatry : the journal of mental science.

[23]  Ole Bernt Fasmer,et al.  Nonlinear Analysis of Motor Activity Shows Differences between Schizophrenia and Depression: A Study Using Fourier Analysis and Sample Entropy , 2011, PloS one.

[24]  Martín Gómez Ravetti,et al.  Distinguishing Noise from Chaos: Objective versus Subjective Criteria Using Horizontal Visibility Graph , 2014, PloS one.

[25]  C L Ehlers,et al.  Chaos and Complexity: Can It Help Us to Understand Mood and Behavior? , 1995 .

[26]  M. Littner,et al.  Practice parameters for the use of auto-titrating continuous positive airway pressure devices for titrating pressures and treating adult patients with obstructive sleep apnea syndrome. An American Academy of Sleep Medicine report. , 2002, Sleep.

[27]  J. Bae,et al.  Factors Affecting Treatment Discontinuation and Treatment Outcome in Patients with Schizophrenia in Korea: 10-Year Follow-Up Study , 2010, Psychiatry investigation.

[28]  G. Morken,et al.  Actigraphically assessed activity in unipolar depression: a comparison of inpatients with and without motor retardation. , 2015, The Journal of clinical psychiatry.

[29]  A Gottschalk,et al.  Evidence of chaotic mood variation in bipolar disorder. , 1995, Archives of general psychiatry.

[30]  L. Guzmán-Vargas,et al.  Earthquake magnitude time series: scaling behavior of visibility networks , 2013 .

[31]  B. Luque,et al.  Horizontal visibility graphs: exact results for random time series. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[32]  C. Vieira,et al.  Performance of Glutamate Dehydrogenase and Triose Phosphate Isomerase Genes in the Analysis of Genotypic Variability of Isolates of Giardia duodenalis from Livestocks , 2013, BioMed research international.