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

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  • Erlend Eindride Fasmer
  • Ole Bernt Fasmer
  • Jan Øystein Berle
  • Ketil J Oedegaard
  • Erik R Hauge

Abstract

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.

Suggested Citation

  • Erlend Eindride Fasmer & Ole Bernt Fasmer & Jan Øystein Berle & Ketil J Oedegaard & Erik R Hauge, 2018. "Graph theory applied to the analysis of motor activity in patients with schizophrenia and depression," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-19, April.
    • Handle: RePEc:plo:pone00:0194791
    DOI: 10.1371/journal.pone.0194791
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    References listed on IDEAS

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    1. Karoline Krane-Gartiser & Tone Elise Gjotterud Henriksen & Gunnar Morken & Arne Vaaler & Ole Bernt Fasmer, 2014. "Actigraphic Assessment of Motor Activity in Acutely Admitted Inpatients with Bipolar Disorder," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-9, February.
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    Cited by:

    1. Petter Jakobsen & Enrique Garcia-Ceja & Michael Riegler & Lena Antonsen Stabell & Tine Nordgreen & Jim Torresen & Ole Bernt Fasmer & Ketil Joachim Oedegaard, 2020. "Applying machine learning in motor activity time series of depressed bipolar and unipolar patients compared to healthy controls," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-16, August.
    2. Ole Bernt Fasmer & Erlend Eindride Fasmer & Kristin Mjeldheim & Wenche Førland & Vigdis Elin Giæver Syrstad & Petter Jakobsen & Jan Øystein Berle & Tone E G Henriksen & Zahra Sepasdar & Erik R Hauge &, 2020. "Diurnal variation of motor activity in adult ADHD patients analyzed with methods from graph theory," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-18, November.

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