Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Standard multiscale entropy reflects spectral power at mismatched temporal scales: What’s signal irregularity got to do with it?

View ORCID ProfileJulian Q. Kosciessa, View ORCID ProfileNiels A. Kloosterman, View ORCID ProfileDouglas D. Garrett
doi: https://doi.org/10.1101/752808
Julian Q. Kosciessa
1Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin/London
2Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany
3Department of Psychology, Humboldt-Universität zu Berlin, Rudower Chaussee 18, 12489 Berlin, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Julian Q. Kosciessa
  • For correspondence: kosciessa@mpib-berlin.mpg.de garrett@mpib-berlin.mpg.de
Niels A. Kloosterman
1Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin/London
2Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Niels A. Kloosterman
Douglas D. Garrett
1Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin/London
2Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Douglas D. Garrett
  • For correspondence: kosciessa@mpib-berlin.mpg.de garrett@mpib-berlin.mpg.de
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract:

The (ir)regularity of neural time series patterns as assessed via Multiscale Sample Entropy (MSE; e.g., Costa et al., 2002) has been proposed as a complementary measure to signal variance, but the con- and divergence between these measures often remains unclear in applications. Importantly, the estimation of sample entropy is referenced to the magnitude of fluctuations, leading to a trade-off between variance and entropy that questions unique entropy modulations. This problem deepens in multi-scale implementations that aim to characterize signal irregularity at distinct timescales. Here, the normalization parameter is traditionally estimated in a scale-invariant manner that is dominated by slow fluctuations. These issues question the validity of the assumption that entropy estimated at finer/coarser time scales reflects signal irregularity at those same scales. While accurate scale-wise mapping is critical for valid inference regarding signal entropy, systematic analyses have been largely absent to date. Here, we first simulate the relations between spectral power (i.e., frequency-specific signal variance) and MSE, highlighting a diffuse reflection of rhythms in entropy time scales. Second, we replicate known cross-sectional age differences in EEG data, while highlighting how timescale-specific results depend on the spectral content of the analyzed signal. In particular, we note that the presence of both low- and high-frequency dynamics leads to the reflection of power spectral density slopes in finer time scales. This association co-occurs with previously reported age differences in both measures, suggesting a common, power-based origin. Furthermore, we highlight that age differences in high frequency power can account for observed entropy differences at coarser scales via the traditional normalization procedure. By systematically assessing the impact of spectral signal content and normalization choice, our findings highlight fundamental biases in traditional MSE implementations. We make multiple recommendations for future work to validly interpret estimates of signal irregularity at time scales of interest.

Highlights

  • Multiscale sample entropy (MSE) links to spectral power via an internal similarity criterion.

  • Counterintuitively, traditional MSE implementations lead to slow-frequency reflections in fine-scale entropy, and high-frequency biases on coarse-scale entropy.

  • Fine-scale entropy reflects power spectral density slopes, a multi-scale property.

  • Narrowband sample entropy indexes (non-stationary) rhythm (ir)regularity at matching time scales.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted September 02, 2019.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Standard multiscale entropy reflects spectral power at mismatched temporal scales: What’s signal irregularity got to do with it?
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Standard multiscale entropy reflects spectral power at mismatched temporal scales: What’s signal irregularity got to do with it?
Julian Q. Kosciessa, Niels A. Kloosterman, Douglas D. Garrett
bioRxiv 752808; doi: https://doi.org/10.1101/752808
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Standard multiscale entropy reflects spectral power at mismatched temporal scales: What’s signal irregularity got to do with it?
Julian Q. Kosciessa, Niels A. Kloosterman, Douglas D. Garrett
bioRxiv 752808; doi: https://doi.org/10.1101/752808

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (4383)
  • Biochemistry (9599)
  • Bioengineering (7094)
  • Bioinformatics (24865)
  • Biophysics (12615)
  • Cancer Biology (9958)
  • Cell Biology (14354)
  • Clinical Trials (138)
  • Developmental Biology (7950)
  • Ecology (12107)
  • Epidemiology (2067)
  • Evolutionary Biology (15989)
  • Genetics (10926)
  • Genomics (14743)
  • Immunology (9870)
  • Microbiology (23676)
  • Molecular Biology (9485)
  • Neuroscience (50872)
  • Paleontology (369)
  • Pathology (1539)
  • Pharmacology and Toxicology (2683)
  • Physiology (4016)
  • Plant Biology (8657)
  • Scientific Communication and Education (1509)
  • Synthetic Biology (2397)
  • Systems Biology (6436)
  • Zoology (1346)