Rhythmo: A Python toolbox for mapping multiday physiological rhythms and brain dynamics
Poster No:
1526
Submission Type:
Abstract Submission
Authors:
Rochelle De Silva1, Shivam Puri1, Rachel Stirling2, Jodie Naim-Feil1, Philippa Karoly1
Institutions:
1University of Melbourne, Melbourne, Victoria, 2University of Melbourne, Carlton, Victoria
First Author:
Co-Author(s):
Introduction:
Circadian rhythms encompass 24-hour cycles that govern physiological processes within the body, including sleep-wake patterns and hormone secretion (Foster, 2020). Recent research has increasingly focused on infradian, or multiday rhythms, over longer timescales (e.g., weekly, monthly and seasonal), particularly in the context of epilepsy (Karoly, Rao, et al., 2021). These investigations into multiday cycles have revealed that epileptic brain activity exhibits distinct patterns operating across longer time scales in humans and animal models (canines, rodents) (Karoly, Rao, et al., 2021). In addition, multiday rhythms of resting heart rate (measured from wearable devices) were discovered in both people with epilepsy and in healthy individuals, which appeared to be comodulated with cortical excitability (Gregg et al., 2023; Karoly, Stirling, et al., 2021). Similarly, heart rate variability is considered as a promising early biomarker for cognitive impairment in populations without dementia or stroke (Forte et al., 2019) and has been associated with decision-making, executive functioning and emotion-regulation (Arakaki et al., 2023).
Little is known about multiday rhythms in human physiology outside of epilepsy research (Karoly, Stirling, et al., 2021). Deepening our understanding of multiday rhythms in the general population is crucial, as it can provide insights into the links between our physiology, behavior, and brain dynamics. The onset of wearable devices has allowed for continuous and easy access to rich, long-term datasets and personalized health insights. Rhythmo is a Python toolbox that extracts individuals' physiological multiday cycles from any long-term datasets (e.g., wearable devices, EEG/ECG and other ExG datasets) and allows for study of longer-term brain dynamics at different phases of these cycles. Through Rhythmo, we aim to provide a toolkit for researchers to capture these changes in brain states which have not previously been discerned.
Little is known about multiday rhythms in human physiology outside of epilepsy research (Karoly, Stirling, et al., 2021). Deepening our understanding of multiday rhythms in the general population is crucial, as it can provide insights into the links between our physiology, behavior, and brain dynamics. The onset of wearable devices has allowed for continuous and easy access to rich, long-term datasets and personalized health insights. Rhythmo is a Python toolbox that extracts individuals' physiological multiday cycles from any long-term datasets (e.g., wearable devices, EEG/ECG and other ExG datasets) and allows for study of longer-term brain dynamics at different phases of these cycles. Through Rhythmo, we aim to provide a toolkit for researchers to capture these changes in brain states which have not previously been discerned.
Methods:
Rhythmo analyzes continuous, long-term timeseries data from physiological signals to extract their multiday cycles and forecast the expected future cyclical trend in the data (see Figure 1 for an example). It accepts a .csv file of timestamped values as input. Rhythmo allows users to tailor the selection of sampling rates, forecasting methods, cycle projection length, and required samples. Rhythmo pre-processes the raw data, finds the strongest cycle and filters it before projecting the future cycle. Based on the strongest cycle, Rhythmo provides sample times for researchers to schedule participants for brain imaging, brain stimulation and/or behavioral tasks to map how the brain changes along various phases of their cycle.
An example application of Rhythmo utilizing heart rate data is displayed in Figure 1.
An example application of Rhythmo utilizing heart rate data is displayed in Figure 1.
·Figure 1
Results:
The result of this project is to present the Rhythmo toolbox for use by other researchers. Rhythmo is available on GitHub for the public to upload their data and to learn more about their intrinsic rhythms: https://github.com/riplresearch/rhythmo
Rhythmo is designed to extract long-term trends in physiological data that may impact brain function. This toolbox can analyze any dataset with time-stamped values, enabling detection and characterization of cycles within the data. Rhythmo provides visualizations (and .csv files) of the dominant physiological cycle and projections of this cycle with corresponding sampling times.
Rhythmo is designed to extract long-term trends in physiological data that may impact brain function. This toolbox can analyze any dataset with time-stamped values, enabling detection and characterization of cycles within the data. Rhythmo provides visualizations (and .csv files) of the dominant physiological cycle and projections of this cycle with corresponding sampling times.
Conclusions:
Rhythmo is a publicly accessible Python toolbox designed to enhance understanding of the relationship between physiological cycles and brain states through the integration of data from long-term datasets.
Modeling and Analysis Methods:
Methods Development 1
Other Methods 2
Physiology, Metabolism and Neurotransmission:
Physiology, Metabolism and Neurotransmission Other
Keywords:
Cognition
Data analysis
Design and Analysis
Experimental Design
Modeling
Open-Source Software
Other - Physiological rhythms
1|2Indicates the priority used for review
Abstract Information
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Provide references using APA citation style.
Forte, G., Favieri, F., & Casagrande, M. (2019). Heart Rate Variability and Cognitive Function: A Systematic Review. Frontiers in Neuroscience, 13. https://doi.org/10.3389/fnins.2019.00710
Foster, R. G. (2020). Sleep, Circadian Rhythms and Health. Interface Focus, 10(3), 20190098. https://doi.org/10.1098/rsfs.2019.0098
Gregg, N. M., Tal Pal Attia, Nasseri, M., Joseph, B., Karoly, P. J., Cui, J., Stirling, R. E., Viana, P., Richner, T. J., Nurse, E. S., Schulze-Bonhage, A., Cook, M., Worrell, G. A., Richardson, M., Freestone, D. R., & Brinkmann, B. H. (2023). Seizure occurrence is linked to multiday cycles in diverse physiological signals. Epilepsia, 64(6), 1627–1639. https://doi.org/10.1111/epi.17607
Karoly, P. J., Rao, V. R., Gregg, N. M., Worrell, G. A., Bernard, C., Cook, M. J., & Baud, M. O. (2021). Cycles in epilepsy. Nature Reviews Neurology, 17(5), 267–284. https://doi.org/10.1038/s41582-021-00464-1
Karoly, P. J., Stirling, R. E., Freestone, D. R., Nurse, E. S., Maturana, M. I., Halliday, A. J., Neal, A., Gregg, N. M., Brinkmann, B. H., Richardson, M. P., La Gerche, A., Grayden, D. B., D’Souza, W., & Cook, M. J. (2021). Multiday cycles of heart rate are associated with seizure likelihood: An observational cohort study. EBioMedicine, 72, 103619. https://doi.org/10.1016/j.ebiom.2021.103619