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Scaling Behavior of Human Locomotor Activity Amplitude: Association with Bipolar Disorder

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  • Premananda Indic
  • Paola Salvatore
  • Carlo Maggini
  • Stefano Ghidini
  • Gabriella Ferraro
  • Ross J Baldessarini
  • Greg Murray

Abstract

Scale invariance is a feature of complex biological systems, and abnormality of multi-scale behaviour may serve as an indicator of pathology. The hypothalamic suprachiasmatic nucleus (SCN) is a major node in central neural networks responsible for regulating multi-scale behaviour in measures of human locomotor activity. SCN also is implicated in the pathophysiology of bipolar disorder (BD) or manic-depressive illness, a severe, episodic disorder of mood, cognition and behaviour. Here, we investigated scaling behaviour in actigraphically recorded human motility data for potential indicators of BD, particularly its manic phase. A proposed index of scaling behaviour (Vulnerability Index [VI]) derived from such data distinguished between: [i] healthy subjects at high versus low risk of mood disorders; [ii] currently clinically stable BD patients versus matched controls; and [iii] among clinical states in BD patients.

Suggested Citation

  • Premananda Indic & Paola Salvatore & Carlo Maggini & Stefano Ghidini & Gabriella Ferraro & Ross J Baldessarini & Greg Murray, 2011. "Scaling Behavior of Human Locomotor Activity Amplitude: Association with Bipolar Disorder," PLOS ONE, Public Library of Science, vol. 6(5), pages 1-8, May.
    • Handle: RePEc:plo:pone00:0020650
    DOI: 10.1371/journal.pone.0020650
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    References listed on IDEAS

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    1. Erik R Hauge & Jan Øystein Berle & Ketil J Oedegaard & Fred Holsten & Ole Bernt Fasmer, 2011. "Nonlinear Analysis of Motor Activity Shows Differences between Schizophrenia and Depression: A Study Using Fourier Analysis and Sample Entropy," PLOS ONE, Public Library of Science, vol. 6(1), pages 1-10, January.
    2. Urs Frey & Tanja Brodbeck & Arnab Majumdar & D. Robin Taylor & G. Ian Town & Michael Silverman & Béla Suki, 2005. "Risk of severe asthma episodes predicted from fluctuation analysis of airway function," Nature, Nature, vol. 438(7068), pages 667-670, December.
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    1. Hadaeghi, Fatemeh & Hashemi Golpayegani, Seyed Mohammad Reza & Gardini, Laura, 2016. "A non-autonomous system leading to cyclic chaotic sets to model physiological rhythms," Applied Mathematics and Computation, Elsevier, vol. 281(C), pages 343-355.

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