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Scaling behavior of human locomotor activity amplitude: association with bipolar disorder.

Indic P, Salvatore P, Maggini C, Ghidini S, Ferraro G, Baldessarini RJ, Murray G - PLoS ONE (2011)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurology, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America. Premananda.Indic@umassmed.edu

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.

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Probability distribution P(A) of amplitude, A, obtained by wavelet analysis of motility data.(a) Rescaled distributions, normalized to provide unit area by rescaling using Pmax, of amplitude at a range of time-scales up to 2 h from a subject considered to be at low risk for BD by GBI criteria. (b) The same data, log-transformed and showing a long-tail, which share most of the values of log (APmax). (c) Amplitude distribution at a specific scale (s = 0.54 h) for 35 subjects considered to be at low risk for BD by GBI criteria. (d) The same for 35 other subjects at high risk for BD. (e) Amplitude distribution up to 2 h for a subject at high risk for BD (blue line) and the corresponding surrogate data (red line); the amplitude distribution (black line) obtained from the wavelet analysis of data derived from a Gaussian distribution follows the amplitude distribution of surrogate data. (f) Log-transformed amplitude from panel e; here, wavelet amplitudes of Gaussian as well as surrogate data have identical distributions without a long-tail, and differ from the distribution of the original data.
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pone-0020650-g002: Probability distribution P(A) of amplitude, A, obtained by wavelet analysis of motility data.(a) Rescaled distributions, normalized to provide unit area by rescaling using Pmax, of amplitude at a range of time-scales up to 2 h from a subject considered to be at low risk for BD by GBI criteria. (b) The same data, log-transformed and showing a long-tail, which share most of the values of log (APmax). (c) Amplitude distribution at a specific scale (s = 0.54 h) for 35 subjects considered to be at low risk for BD by GBI criteria. (d) The same for 35 other subjects at high risk for BD. (e) Amplitude distribution up to 2 h for a subject at high risk for BD (blue line) and the corresponding surrogate data (red line); the amplitude distribution (black line) obtained from the wavelet analysis of data derived from a Gaussian distribution follows the amplitude distribution of surrogate data. (f) Log-transformed amplitude from panel e; here, wavelet amplitudes of Gaussian as well as surrogate data have identical distributions without a long-tail, and differ from the distribution of the original data.

Mentions: Rhythms were observed at circadian (∼24 h) as well as other temporal ranges (minutes or hours). Figure 1 represents an example of motility data along with the multi-scale rhythms obtained using wavelet analysis. The amplitude of rhythms at shorter time scales appeared to be random; to check whether such fluctuation was simply due to noise in the data, we plotted the distribution of amplitudes at a range of time-scales. The distribution of amplitudes obtained at very short time-scales (≤2.0 h) had an apparent long-tail and was nearly collapsed (Figure 2 (a–d)). Such a long-tail distribution is characteristic of nonlinear complex systems near critical points, and the collapse of amplitude distribution represents the scale-invariant feature of such systems [25].


Scaling behavior of human locomotor activity amplitude: association with bipolar disorder.

Indic P, Salvatore P, Maggini C, Ghidini S, Ferraro G, Baldessarini RJ, Murray G - PLoS ONE (2011)

Probability distribution P(A) of amplitude, A, obtained by wavelet analysis of motility data.(a) Rescaled distributions, normalized to provide unit area by rescaling using Pmax, of amplitude at a range of time-scales up to 2 h from a subject considered to be at low risk for BD by GBI criteria. (b) The same data, log-transformed and showing a long-tail, which share most of the values of log (APmax). (c) Amplitude distribution at a specific scale (s = 0.54 h) for 35 subjects considered to be at low risk for BD by GBI criteria. (d) The same for 35 other subjects at high risk for BD. (e) Amplitude distribution up to 2 h for a subject at high risk for BD (blue line) and the corresponding surrogate data (red line); the amplitude distribution (black line) obtained from the wavelet analysis of data derived from a Gaussian distribution follows the amplitude distribution of surrogate data. (f) Log-transformed amplitude from panel e; here, wavelet amplitudes of Gaussian as well as surrogate data have identical distributions without a long-tail, and differ from the distribution of the original data.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3105113&req=5

pone-0020650-g002: Probability distribution P(A) of amplitude, A, obtained by wavelet analysis of motility data.(a) Rescaled distributions, normalized to provide unit area by rescaling using Pmax, of amplitude at a range of time-scales up to 2 h from a subject considered to be at low risk for BD by GBI criteria. (b) The same data, log-transformed and showing a long-tail, which share most of the values of log (APmax). (c) Amplitude distribution at a specific scale (s = 0.54 h) for 35 subjects considered to be at low risk for BD by GBI criteria. (d) The same for 35 other subjects at high risk for BD. (e) Amplitude distribution up to 2 h for a subject at high risk for BD (blue line) and the corresponding surrogate data (red line); the amplitude distribution (black line) obtained from the wavelet analysis of data derived from a Gaussian distribution follows the amplitude distribution of surrogate data. (f) Log-transformed amplitude from panel e; here, wavelet amplitudes of Gaussian as well as surrogate data have identical distributions without a long-tail, and differ from the distribution of the original data.
Mentions: Rhythms were observed at circadian (∼24 h) as well as other temporal ranges (minutes or hours). Figure 1 represents an example of motility data along with the multi-scale rhythms obtained using wavelet analysis. The amplitude of rhythms at shorter time scales appeared to be random; to check whether such fluctuation was simply due to noise in the data, we plotted the distribution of amplitudes at a range of time-scales. The distribution of amplitudes obtained at very short time-scales (≤2.0 h) had an apparent long-tail and was nearly collapsed (Figure 2 (a–d)). Such a long-tail distribution is characteristic of nonlinear complex systems near critical points, and the collapse of amplitude distribution represents the scale-invariant feature of such systems [25].

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurology, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America. Premananda.Indic@umassmed.edu

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.

Show MeSH
Related in: MedlinePlus