1. Neuroscience

Human Neocortical Neurosolver (HNN), a new software tool for interpreting the cellular and network origin of human MEG/EEG data

  1. Samuel A Neymotin  Is a corresponding author
  2. Dylan S Daniels
  3. Blake Caldwell
  4. Robert A McDougal
  5. Nicholas T Carnevale
  6. Mainak Jas
  7. Christopher I Moore
  8. Michael L Hines
  9. Matti Hämäläinen
  10. Stephanie R Jones  Is a corresponding author
  1. Brown University, United States
  2. Yale University, United States
  3. Massachusetts General Hospital, United States
https://doi.org/10.7554/eLife.51214
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Cite this article
  1. Samuel A Neymotin
  2. Dylan S Daniels
  3. Blake Caldwell
  4. Robert A McDougal
  5. Nicholas T Carnevale
  6. Mainak Jas
  7. Christopher I Moore
  8. Michael L Hines
  9. Matti Hämäläinen
  10. Stephanie R Jones
(2020)
Human Neocortical Neurosolver (HNN), a new software tool for interpreting the cellular and network origin of human MEG/EEG data
eLife 9:e51214.
https://doi.org/10.7554/eLife.51214
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  3. Download .RIS

Abstract

Magneto- and electro-encephalography (MEG/EEG) non-invasively record human brain activity with millisecond resolution providing reliable markers of healthy and disease states. Relating these macroscopic signals to underlying cellular- and circuit-level generators is a limitation that constrains using MEG/EEG to reveal novel principles of information processing or to translate findings into new therapies for neuropathology. To address this problem, we built Human Neocortical Neurosolver (HNN, https://hnn.brown.edu) software. HNN has a graphical user interface designed to help researchers and clinicians interpret the neural origins of MEG/EEG. HNN's core is a neocortical circuit model that accounts for biophysical origins of electrical currents generating MEG/EEG. Data can be directly compared to simulated signals and parameters easily manipulated to develop/test hypotheses on a signal's origin. Tutorials teach users to simulate commonly measured signals, including event related potentials and brain rhythms. HNN's ability to associate signals across scales makes it a unique tool for translational neuroscience research.

Data availability

All source-code, model parameters, and associated data are provided in a permanent public-accessible repository on github (https://github.com/jonescompneurolab/hnn).

Article and author information

Author details

  1. Samuel A Neymotin

    Department of Neuroscience, Brown University, Providence, United States
    For correspondence
    samuel.neymotin@nki.rfmh.org
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3646-5195

  2. Dylan S Daniels

    Department of Neuroscience, Brown University, Providence, United States
    Competing interests
    The authors declare that no competing interests exist.

  3. Blake Caldwell

    Department of Neuroscience, Brown University, Providence, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6882-6998

  4. Robert A McDougal

    Department of Neuroscience, Yale University, New Haven, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6394-3127

  5. Nicholas T Carnevale

    Department of Neuroscience, Yale University, New Haven, United States
    Competing interests
    The authors declare that no competing interests exist.

  6. Mainak Jas

    Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, United States
    Competing interests
    The authors declare that no competing interests exist.

  7. Christopher I Moore

    Department of Neuroscience, Brown University, Providence, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4534-1602

  8. Michael L Hines

    Department of Neuroscience, Yale University, New Haven, United States
    Competing interests
    The authors declare that no competing interests exist.

  9. Matti Hämäläinen

    Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, United States
    Competing interests
    The authors declare that no competing interests exist.

  10. Stephanie R Jones

    Department of Neuroscience, Brown University, Providence, United States
    For correspondence
    Stephanie_Jones@brown.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6760-5301

Funding

National Institute of Biomedical Imaging and Bioengineering (BRAIN Award 5-R01-EB022889-02)

  • Samuel A Neymotin
  • Dylan S Daniels
  • Blake Caldwell
  • Robert A McDougal
  • Nicholas T Carnevale
  • Mainak Jas
  • Christopher I Moore
  • Michael L Hines
  • Matti Hämäläinen
  • Stephanie R Jones

National Institute of Biomedical Imaging and Bioengineering (BRAIN Award Supplement R01EB022889-02S1)

  • Samuel A Neymotin
  • Dylan S Daniels
  • Blake Caldwell
  • Robert A McDougal
  • Nicholas T Carnevale
  • Mainak Jas
  • Christopher I Moore
  • Michael L Hines
  • Matti Hämäläinen
  • Stephanie R Jones

National Institute on Deafness and Other Communication Disorders (5-R01DC012947-07)

  • Samuel A Neymotin

Army Research Office (W911NF-19-1-0402)

  • Samuel A Neymotin

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. The views and conclusions contained in this document are those of the authorsand should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Office or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.

Copyright

© 2020, Neymotin 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.

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  1. Samuel A Neymotin
  2. Dylan S Daniels
  3. Blake Caldwell
  4. Robert A McDougal
  5. Nicholas T Carnevale
  6. Mainak Jas
  7. Christopher I Moore
  8. Michael L Hines
  9. Matti Hämäläinen
  10. Stephanie R Jones
(2020)
Human Neocortical Neurosolver (HNN), a new software tool for interpreting the cellular and network origin of human MEG/EEG data
eLife 9:e51214.
https://doi.org/10.7554/eLife.51214

Share this article

https://doi.org/10.7554/eLife.51214

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