Human Neocortical Neurosolver (HNN), a new software tool for interpreting the cellular and network origin of human MEG/EEG data
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 22, 2020
- Source ID
- 10.7554/elife.51214
Entities
People
- Blake Caldwell
- Christopher I. Moore
- Dylan S Daniels
- Mainak Jas
- Matti Hämäläinen
- Michael L Hines
- Nicholas T. Carnevale
- Robert A McDougal
- Samuel A. Neymotin
- Stephanie Jones
Organizations
- Army Research Office
- Brown University
- Harvard Medical School
- Massachusetts General Hospital
- Nathan Kline Institute for Psychiatric Research
- National Institute of Biomedical Imaging and Bioengineering
- National Institute on Deafness and Other Communication Disorders
- Yale University