Characterizing rhythmic wheel-turning behavioral patterns in cockroaches Rhyparobia maderae using machine learning
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Organisms must adapt to environmental changes occurring across multiple time scales, with endogenous multiscale clocks coordinating physiology and behavior with recurring environmental rhythms, including the dominant 24-hour cycle and faster ultradian rhythms. The Madeira cockroach ( Rhyparobia maderae ) provides a suitable model for investigating such multiscale temporal organization. Here, locomotor activity was recorded in running-wheel experiments under constant darkness. While the endogenous circadian clock produces a clearly visible 24-hour rhythm, it remains unknown whether locomotor behavior also exhibits temporal patterns at additional time scales. These temporal patterns cannot be found by classical frequency analysis, as they are veiled by higher harmonics of the circadian rhythm which are in the same frequency range. Unsupervised machine learning methods such as K-Means clustering, self-organizing maps and Gaussian mixture are used in search for fast ultradian rhythms possibly linked to circadian cycles in locomotor activity. Prior to applying these methods, data metrics are defined which characterize bouts of activity (called activity impulses) compared to periods of reduced activity. A stochastic pattern was found in these activity metrics which characterizes the time distance between activity impulses. Across all approaches, a consistent ultradian rhythm of approximately one hour was identified in the timing of the activity maxima. This rhythm was mainly detected during the subjective night, suggesting circadian control, and appears to consist of two components with periods of approximately 40 minutes and 1.5 hours. The method proposed in this paper is applied to two cockroach groups with different levels of activity, and is generalizable to diverse datasets occurring in the form of a time series with a dominant rhythm.
Author Summary
Detecting and predicting regular environmental changes is essential for the survival of organisms. Geophysical rhythms, such as the 24-hour light–dark cycle caused by the Earth’s rotation, have therefore driven the evolution of endogenous circadian clocks. Besides the daily light–dark cycle, organisms also respond to faster and slower environmental rhythms, such as recurring food availability or mating opportunities, signaled via patterned chemical stimuli. These rhythms are likely more flexible than the circadian rhythm, allowing organisms to adapt to local conditions. Because they are shorter, more frequent, and less regular, they are harder to detect and remain poorly understood despite their roles in processes such as hormone regulation and brain function. The ancient species of the Madeira cockroach possesses multiscale endogenous clocks that regulate physiology and behavior. Using a machine learning framework, we detected hidden faster rhythms beyond the circadian cycle and identified a consistent temporal pattern in the intervals between activity bursts, pointing to a potential ultradian rhythm that had not previously been described. This framework may be applicable beyond cockroaches, enabling the detection of similar rhythms in other species and the analysis of temporal patterns in diverse time series datasets.
