1. Neuroscience
Download icon

A state space modeling approach to real-time phase estimation

  1. Anirudh Wodeyar  Is a corresponding author
  2. Mark Schatza
  3. Alik S Widge
  4. Uri T Eden
  5. Mark A Kramer
  1. Boston University, United States
  2. University of Minnesota, United States
Tools and Resources
  • Cited 0
  • Views 386
  • Annotations
Cite this article as: eLife 2021;10:e68803 doi: 10.7554/eLife.68803

Abstract

Brain rhythms have been proposed to facilitate brain function, with an especially important role attributed to the phase of low frequency rhythms. Understanding the role of phase in neural function requires interventions that perturb neural activity at a target phase, necessitating estimation of phase in real-time. Current methods for real-time phase estimation rely on bandpass filtering, which assumes narrowband signals and couples the signal and noise in the phase estimate, adding noise to the phase and impairing detections of relationships between phase and behavior. To address this, we propose a state space phase estimator for real-time tracking of phase. By tracking the analytic signal as a latent state, this framework avoids the requirement of bandpass filtering, separately models the signal and the noise, accounts for rhythmic confounds, and provides credible intervals for the phase estimate. We demonstrate in simulations that the state space phase estimator outperforms current state-of-the-art real-time methods in the contexts of common confounds such as broadband rhythms, phase resets and co-occurring rhythms. Finally, we show applications of this approach to in vivo data. The method is available as a ready-to-use plug-in for the OpenEphys acquisition system, making it widely available for use in experiments.

Data availability

All data generated or analyzed during this study, or were used to create the figures are included in the supporting files, or are available through already public data archives (https://gin.g-node.org/bnplab/phastimate, 10.6084/m9.figshare.14374355).

The following previously published data sets were used
    1. Zrenner C
    2. Galevska D
    3. Nieminen JO
    4. Baur D
    5. Stefanou MI
    6. Ziemann U
    (2020) Phastimate
    https://gin.g-node.org/bnplab/phastimate/src/master/murhythmdataset.mat, https://doi.org/10.1016/j.neuroimage.2020.116761.

Article and author information

Author details

  1. Anirudh Wodeyar

    Mathematics and Statistics, Boston University, Boston, United States
    For correspondence
    wodeyar@bu.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2577-5139
  2. Mark Schatza

    Department of Psychiatry, University of Minnesota, Minneapolis, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Alik S Widge

    Department of Psychiatry, University of Minnesota, Minneapolis, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8510-341X
  4. Uri T Eden

    Department of Mathematics and Statistics, Boston University, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Mark A Kramer

    Department of Mathematics and Statistics, Boston University, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.

Funding

National Institutes of Health (R01 EB026938)

  • Anirudh Wodeyar
  • Alik S Widge
  • Uri T Eden
  • Mark A Kramer

National Institutes of Health (R01 MH119384)

  • Alik S Widge

National Institutes of Health (R01 MH123634)

  • Alik S Widge

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Frances K Skinner, Krembil Research Institute, University Health Network, Canada

Publication history

  1. Received: March 26, 2021
  2. Accepted: September 24, 2021
  3. Accepted Manuscript published: September 27, 2021 (version 1)

Copyright

© 2021, Wodeyar et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 386
    Page views
  • 63
    Downloads
  • 0
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Neuroscience
    Roni O Maimon-Mor et al.
    Research Article Updated

    The study of artificial arms provides a unique opportunity to address long-standing questions on sensorimotor plasticity and development. Learning to use an artificial arm arguably depends on fundamental building blocks of body representation and would therefore be impacted by early life experience. We tested artificial arm motor-control in two adult populations with upper-limb deficiencies: a congenital group—individuals who were born with a partial arm, and an acquired group—who lost their arm following amputation in adulthood. Brain plasticity research teaches us that the earlier we train to acquire new skills (or use a new technology) the better we benefit from this practice as adults. Instead, we found that although the congenital group started using an artificial arm as toddlers, they produced increased error noise and directional errors when reaching to visual targets, relative to the acquired group who performed similarly to controls. However, the earlier an individual with a congenital limb difference was fitted with an artificial arm, the better their motor control was. Since we found no group differences when reaching without visual feedback, we suggest that the ability to perform efficient visual-based corrective movements is highly dependent on either biological or artificial arm experience at a very young age. Subsequently, opportunities for sensorimotor plasticity become more limited.

    1. Cell Biology
    2. Neuroscience
    Shahzad S Khan et al.
    Research Advance

    Activating LRRK2 mutations cause Parkinson's disease, and pathogenic LRRK2 kinase interferes with ciliogenesis. Previously, we showed that cholinergic interneurons of the dorsal striatum lose their cilia in R1441C LRRK2 mutant mice (Dhekne et al., 2018). Here, we show that cilia loss is seen as early as 10 weeks of age in these mice and also in two other mouse strains carrying the most common human G2019S LRRK2 mutation. Loss of the PPM1H phosphatase that is specific for LRRK2-phosphorylated Rab GTPases yields the same cilia loss phenotype seen in mice expressing pathogenic LRRK2 kinase, strongly supporting a connection between Rab GTPase phosphorylation and cilia loss. Moreover, astrocytes throughout the striatum show a ciliation defect in all LRRK2 and PPM1H mutant models examined. Hedgehog signaling requires cilia, and loss of cilia in LRRK2 mutant rodents correlates with dysregulation of Hedgehog signaling as monitored by in situ hybridization of Gli1 and Gdnf transcripts. Dopaminergic neurons of the substantia nigra secrete a Hedgehog signal that is sensed in the striatum to trigger neuroprotection; our data support a model in which LRRK2 and PPM1H mutant mice show altered responses to critical Hedgehog signals in the nigrostriatal pathway.