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

The Oscillatory ReConstruction Algorithm (ORCA) adaptively identifies frequency bands to improve spectral decomposition in human and rodent neural recordings

View ORCID ProfileAndrew J Watrous, Robert Buchanan
doi: https://doi.org/10.1101/855288
Andrew J Watrous
Department of Neurology, Dell Medical School, The University of Texas at Austin, Austin, TX 78712Institute for Neuroscience, The University of Texas at Austin, Austin, TX 78712Center for Learning and Memory, The University of Texas at Austin, Austin, TX 78712Department of Psychology, The University of Texas at Austin, Austin, TX 78712Seton Brain and Spine Institute, Division of Neurosurgery, Austin, TX 78701
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Andrew J Watrous
  • For correspondence: andrew.j.watrous@gmail.com
Robert Buchanan
Institute for Neuroscience, The University of Texas at Austin, Austin, TX 78712Department of Psychology, The University of Texas at Austin, Austin, TX 78712Seton Brain and Spine Institute, Division of Neurosurgery, Austin, TX 78701Department of Neurosurgery, Dell Medical School, The University of Texas at Austin, 78712Department of Psychiatry, Dell Medical School, The University of Texas at Austin, 78712
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Data/Code
  • Preview PDF
Loading

Abstract

Neural oscillations are routinely analyzed using methods that measure activity in canonical frequency bands (e.g. alpha, 8-12 Hz), though the frequency of neural signals is not fixed and varies within and across individuals based on numerous factors including neuroanatomy, behavioral demands, and species. Further, band-limited activity is an often assumed, typically unmeasured model of neural activity and band definitions vary considerably across studies. These factors together mask individual differences and can lead to noisy spectral estimates and interpretational problems when linking electrophysiology to behavior. We developed the Oscillatory ReConstruction Algorithm (“ORCA”), an unsupervised method to measure the spectral characteristics of neural signals in adaptively identified bands which incorporates two new methods for frequency band identification. ORCA uses the instantaneous power, phase, and frequency of activity in each band to reconstruct the signal and directly quantify spectral decomposition performance using each of four different models. To reduce researcher bias, ORCA provides spectral estimates derived from the best model and requires minimal hyperparameterization. Analyzing human scalp EEG data during eyes open and eyes-closed “resting” conditions, we first identify variability in the frequency content of neural signals across subjects and electrodes. We demonstrate that ORCA significantly improves spectral decomposition compared to conventional methods and captures the well-known increase in low-frequency activity during eyes closure in electrode- and subject-specific frequency bands. We further illustrate the utility of our method in rodent CA1 recordings. ORCA is a novel analytic tool that will allow researchers to investigate how non-stationary neural oscillations vary across behaviors, brain regions, individuals, and species.

Footnotes

  • https://github.com/andrew-j-watrous/ORCA

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 4.0 International license.
Back to top
PreviousNext
Posted November 25, 2019.
Download PDF
Data/Code
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.
The Oscillatory ReConstruction Algorithm (ORCA) adaptively identifies frequency bands to improve spectral decomposition in human and rodent neural recordings
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
Share
The Oscillatory ReConstruction Algorithm (ORCA) adaptively identifies frequency bands to improve spectral decomposition in human and rodent neural recordings
Andrew J Watrous, Robert Buchanan
bioRxiv 855288; doi: https://doi.org/10.1101/855288
Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Citation Tools
The Oscillatory ReConstruction Algorithm (ORCA) adaptively identifies frequency bands to improve spectral decomposition in human and rodent neural recordings
Andrew J Watrous, Robert Buchanan
bioRxiv 855288; doi: https://doi.org/10.1101/855288

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 (1647)
  • Biochemistry (2738)
  • Bioengineering (1907)
  • Bioinformatics (10243)
  • Biophysics (4183)
  • Cancer Biology (3218)
  • Cell Biology (4538)
  • Clinical Trials (135)
  • Developmental Biology (2840)
  • Ecology (4460)
  • Epidemiology (2041)
  • Evolutionary Biology (7231)
  • Genetics (5476)
  • Genomics (6813)
  • Immunology (2388)
  • Microbiology (7483)
  • Molecular Biology (2992)
  • Neuroscience (18584)
  • Paleontology (136)
  • Pathology (472)
  • Pharmacology and Toxicology (780)
  • Physiology (1149)
  • Plant Biology (2706)
  • Scientific Communication and Education (680)
  • Synthetic Biology (888)
  • Systems Biology (2846)
  • Zoology (468)