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Wavelet analysis of circadian and ultradian behavioral rhythms.

Leise TL - J Circadian Rhythms (2013)

Bottom Line: The discrete wavelet transform decomposes a time series into components associated with distinct frequency bands, thereby facilitating the removal of noise and trend or the isolation of a particular frequency band of interest.To demonstrate the wavelet-based analysis, we apply the transforms to a numerically-generated example and also to a variety of hamster behavioral records.When used appropriately, wavelet transforms can reveal patterns that are not easily extracted using other methods of analysis in common use, but they must be applied and interpreted with care.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Mathematics, Amherst College, Amherst, MA 01002 USA. tleise@amherst.edu.

ABSTRACT
: We review time-frequency methods that can be useful in quantifying circadian and ultradian patterns in behavioral records. These records typically exhibit details that may not be captured through commonly used measures such as activity onset and so may require alternative approaches. For instance, activity may involve multiple bouts that vary in duration and magnitude within a day, or may exhibit day-to-day changes in period and in ultradian activity patterns. The discrete Fourier transform and other types of periodograms can estimate the period of a circadian rhythm, but we show that they can fail to correctly assess ultradian periods. In addition, such methods cannot detect changes in the period over time. Time-frequency methods that can localize frequency estimates in time are more appropriate for analysis of ultradian periods and of fluctuations in the period. The continuous wavelet transform offers a method for determining instantaneous frequency with good resolution in both time and frequency, capable of detecting changes in circadian period over the course of several days and in ultradian period within a given day. The discrete wavelet transform decomposes a time series into components associated with distinct frequency bands, thereby facilitating the removal of noise and trend or the isolation of a particular frequency band of interest. To demonstrate the wavelet-based analysis, we apply the transforms to a numerically-generated example and also to a variety of hamster behavioral records. When used appropriately, wavelet transforms can reveal patterns that are not easily extracted using other methods of analysis in common use, but they must be applied and interpreted with care.

No MeSH data available.


Related in: MedlinePlus

Example of the AWT applied to detect changes in period and amplitude over time. (A) Time series of wheel running (counts per 6 minute bin) for a female hamster in constant darkness. (B) Heat map of the magnitude of the AWT coefficients. The black curve is the wavelet ridge that indicates the instantaneous period, while the color of the heat map indicates amplitude. (C) Curves showing the amplitude (in blue) and period (in black), extracted from the wavelet ridge in (B), revealing that the oscillation of the amplitude is nearly antiphase to the oscillation of the period in this example. Hamster record courtesy of Eric Bittman and Emily Manoogian.
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Figure 5: Example of the AWT applied to detect changes in period and amplitude over time. (A) Time series of wheel running (counts per 6 minute bin) for a female hamster in constant darkness. (B) Heat map of the magnitude of the AWT coefficients. The black curve is the wavelet ridge that indicates the instantaneous period, while the color of the heat map indicates amplitude. (C) Curves showing the amplitude (in blue) and period (in black), extracted from the wavelet ridge in (B), revealing that the oscillation of the amplitude is nearly antiphase to the oscillation of the period in this example. Hamster record courtesy of Eric Bittman and Emily Manoogian.

Mentions: The estrous cycle in hamsters typically results in an approximately 4-day pattern in the amplitude and period of activity (“scalloping”), due in part to the effects of estradiol [24]. The AWT can be effective in tracking these changes in amplitude and period over time, if the record is sufficiently long. The difficulty is that edge effects can distort the AWT heat map, so that 1-2 days at the beginning and end are not reliable. If a 4-day pattern is being sought, then the activity record should cover at least 2 uninterrupted weeks, preferably more, for the AWT to yield good results. A further disadvantage of the AWT is that missing data in the record can also distort the results. Nevertheless, on uninterrupted records of sufficient length, the AWT can provide a spectacular visualization of the effects of the estrous cycle on activity. See Figure 5 for an example. For other examples of using wavelet analysis to detect period and amplitude changes across the estrous cycle, see [9] (in mice) and [11] (in hamsters).


Wavelet analysis of circadian and ultradian behavioral rhythms.

Leise TL - J Circadian Rhythms (2013)

Example of the AWT applied to detect changes in period and amplitude over time. (A) Time series of wheel running (counts per 6 minute bin) for a female hamster in constant darkness. (B) Heat map of the magnitude of the AWT coefficients. The black curve is the wavelet ridge that indicates the instantaneous period, while the color of the heat map indicates amplitude. (C) Curves showing the amplitude (in blue) and period (in black), extracted from the wavelet ridge in (B), revealing that the oscillation of the amplitude is nearly antiphase to the oscillation of the period in this example. Hamster record courtesy of Eric Bittman and Emily Manoogian.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3717080&req=5

Figure 5: Example of the AWT applied to detect changes in period and amplitude over time. (A) Time series of wheel running (counts per 6 minute bin) for a female hamster in constant darkness. (B) Heat map of the magnitude of the AWT coefficients. The black curve is the wavelet ridge that indicates the instantaneous period, while the color of the heat map indicates amplitude. (C) Curves showing the amplitude (in blue) and period (in black), extracted from the wavelet ridge in (B), revealing that the oscillation of the amplitude is nearly antiphase to the oscillation of the period in this example. Hamster record courtesy of Eric Bittman and Emily Manoogian.
Mentions: The estrous cycle in hamsters typically results in an approximately 4-day pattern in the amplitude and period of activity (“scalloping”), due in part to the effects of estradiol [24]. The AWT can be effective in tracking these changes in amplitude and period over time, if the record is sufficiently long. The difficulty is that edge effects can distort the AWT heat map, so that 1-2 days at the beginning and end are not reliable. If a 4-day pattern is being sought, then the activity record should cover at least 2 uninterrupted weeks, preferably more, for the AWT to yield good results. A further disadvantage of the AWT is that missing data in the record can also distort the results. Nevertheless, on uninterrupted records of sufficient length, the AWT can provide a spectacular visualization of the effects of the estrous cycle on activity. See Figure 5 for an example. For other examples of using wavelet analysis to detect period and amplitude changes across the estrous cycle, see [9] (in mice) and [11] (in hamsters).

Bottom Line: The discrete wavelet transform decomposes a time series into components associated with distinct frequency bands, thereby facilitating the removal of noise and trend or the isolation of a particular frequency band of interest.To demonstrate the wavelet-based analysis, we apply the transforms to a numerically-generated example and also to a variety of hamster behavioral records.When used appropriately, wavelet transforms can reveal patterns that are not easily extracted using other methods of analysis in common use, but they must be applied and interpreted with care.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Mathematics, Amherst College, Amherst, MA 01002 USA. tleise@amherst.edu.

ABSTRACT
: We review time-frequency methods that can be useful in quantifying circadian and ultradian patterns in behavioral records. These records typically exhibit details that may not be captured through commonly used measures such as activity onset and so may require alternative approaches. For instance, activity may involve multiple bouts that vary in duration and magnitude within a day, or may exhibit day-to-day changes in period and in ultradian activity patterns. The discrete Fourier transform and other types of periodograms can estimate the period of a circadian rhythm, but we show that they can fail to correctly assess ultradian periods. In addition, such methods cannot detect changes in the period over time. Time-frequency methods that can localize frequency estimates in time are more appropriate for analysis of ultradian periods and of fluctuations in the period. The continuous wavelet transform offers a method for determining instantaneous frequency with good resolution in both time and frequency, capable of detecting changes in circadian period over the course of several days and in ultradian period within a given day. The discrete wavelet transform decomposes a time series into components associated with distinct frequency bands, thereby facilitating the removal of noise and trend or the isolation of a particular frequency band of interest. To demonstrate the wavelet-based analysis, we apply the transforms to a numerically-generated example and also to a variety of hamster behavioral records. When used appropriately, wavelet transforms can reveal patterns that are not easily extracted using other methods of analysis in common use, but they must be applied and interpreted with care.

No MeSH data available.


Related in: MedlinePlus