Human Neocortical Neurosolver
Challenge in Human Neuroscience
Electro- and magneto-encephalography (EEG/MEG) are among the most powerful technologies to non-invasively record human brain activity with millisecond resolution. They provide reliable markers of healthy brain function and disease states. A major limitation is that it is often difficult to connect the macroscopic scale-measured signals to the underlying cellular and circuit-level neural generators. This difficulty limits the translation of EEG/MEG studies into novel principles of information processing, or into new treatment modalities for neural pathologies.
Solution: Human Neocortical Neurosolver
Human Neocortical Neurosolver (HNN) is a user-friendly software tool that provides a novel solution to this challenge. HNN gives researchers and clinicians the ability to test and develop hypotheses on the circuit mechanism underlying their EEG/MEG data in an easy-to-use environment. The foundation of HNN is a preconstructed biophysically principled neocortical column model that simulates the electrical activity of the neocortical cells and circuits that generate the primary electrical currents underlying EEG/MEG recordings. HNN is designed with workflows to activate the neocortical model with activity from exogenous thalamic and intracortical sources and tutorials on how to begin to study the neural origin of source localized event-related potentials and low-frequency brain rhythms**. Out-of-the-box users can quickly begin to get an intuition of how cells and circuits contribute to macroscale signal generation. HNN can be run through an interactive graphical user interface (HNN-GUI) or, for more advanced users, through a command line python interface (HNN-Core). We provide tutorials on how to import your data and begin to understand the underlying circuit mechanisms for both versions of the software.
We are still in the early days of understanding the contributors to these important macroscale human signals. HNN is designed as a starting point for developing and testing predictions on the detailed neural mechanisms contributing to EEG/MEG signals based on the known biophysics of their generation. The multi-scale simulation (layer and cell-specific spiking, LFP/CSD, etc..) possible with HNN facilitates testing of model-derived predictions across multiple recordings scales and species. HNN is designed in a modular fashion so that it can be easily expanded as new knowledge of the cells and circuits that contribute to these signals is learned. We are applying best practices in an open-source software design to encourage widespread use and community development. Over time, we hope that crowdsourced knowledge using and expanding HNN will lead to biophysically principled and validated theories of human information processing and will help guide therapeutics for neuropathology.
* HNN is not a source localization software but rather a method for interpreting the cell and network level generators of a source signal from a localized brain area after you have an estimate of the source location, time course, and orientation. There are many excellent source localization software for this estimation process. For an example of how to perform source localization using MNE-Python followed by cell and circuit level interpretation with HNN, see our example in HNN-Core.
** The tutorials on use of HNN are based on our prior published studies of ERPs and low-frequency oscillations from the primary somatosensory cortex. We provide data and initial parameter sets along with workflows to teach users how to do what we did to develop and test hypotheses on the neural origin of these signals. Once this workflow is learned, users will be able to use HNN to test their own hypotheses. Our goal is not to promote singular theories on the origin of any particular signal, but rather to provide a tool based on known biophysics and training resources where researchers can develop testable predictions on the neural origin of their data.
Before beginning to use HNN, please read this important getting started information.
Once you have read this document, you can begin to use HNN with the step-by-step instructions on our tutorial pages. In practice, we strongly recommend starting with the HNN-GUI tutorials, after which understanding the corresponding HNN-Core python tutorials will be more accessible.
Citing Our Software
Neymotin SA, Daniels DS, Caldwell B, McDougal RA, Carnevale NT, Jas M, Moore CI, Hines ML, Hämäläinen M, Jones SR. Human Neocortical Neurosolver (HNN), a new software tool for interpreting the cellular and network origin of human MEG/EEG data (2020). eLife 2020;9:e51214 DOI: 10.7554/eLife.51214 (https://doi.org/10.7554/eLife.51214)
Get in Touch
Stephanie Jones Lab
Carney Institute for Brain Sciences
164 Angell St., Floor 4
Providence, RI 02906