Human Neocortical Neurosolver (HNN), a new software tool for interpreting the cellular and network origin of human MEG/EEG data
- Brown University, United States
- Yale University, United States
- Massachusetts General Hospital, United States
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
Christopher I Moore
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.
Reviewing Editor
- Arjen Stolk, Donders Centre for Cognitive Neuroimaging, Netherlands
Publication history
- Received: August 20, 2019
- Accepted: January 22, 2020
- Accepted Manuscript published: January 22, 2020 (version 1)
- Version of Record published: February 13, 2020 (version 2)
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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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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