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

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 computational neural model that simulates the electrical activity of the neocortical cells and circuits that generate the primary electrical currents underlying EEG/MEG recordings. We provide tutorials on how to import your data and to begin to understand the underlying circuit mechanisms.

Getting Started

Begin by reading the Overview and Uniqueness of HNN and then review the step-by-step instructions of use in our Tutorials. Our tutorials are based on prior studies of primary somatosensory cortex and focus on simulating some of the most commonly measured EEG/MEG signals including sensory evoked response (ERPs), and low frequency alpha (10-14 Hz), beta (15-30 Hz) and gamma (30-80 Hz) rhythms (Jones et al., 2007; Jones et al., 2009; Sherman et al., 2016; Lee & Jones 2013).  By learning how to simulate these basic signals, you will gain insight into how to adjust the parameters in our model for your hypothesis-testing needs.

Citations

Samuel A Neymotin, Dylan S Daniels, Noam Peled, Robert A McDougal, Nicholas T Carnevale, Christopher I Moore, Michael L Hines, Matti Hamalainen, Stephanie R Jones. Human Neocortical Neurosolver (2018),  DOI 10.5281/zenodo.1446517 

We’re Open-Source – Join in Growing our Mission

Our software was created to be expandable and generative. While designed for use by researchers without computational neuroscience expertise, if you know a bit about computational neuroscience, we encourage you to contribute to the developments. Our software is open-source. It was created in the NEURON-Python simulation environment and designed to be flexibly expanded to include other cell types, or circuits. We have a user’s forum and online resources to answer questions and expand the utility of our tool.

Coming Soon!

We are planning several expansions to our code. We value the input of the user to guide our developments. Please join our user’s forum to submit your suggestions to extend HNN’s capabilities.

Next step expansion (in no particular order):

  • Integration with MNE-Python inverse solution software will enable estimation of the location, timecourse and circuit mechanisms underlying signal generation all in one easy-to-use software package.
  • MNE-Python will soon enable source localization of ECoG signals, expanding the utility of HNN to subdural recordings.
  • Biophysically principled estimation of local field potentials signals in different cortical layers.
  • Dynamic interaction of primary current sources in multiple cortical areas, and thalamic nuclei.
  • Integration with the Neuroscience Gateway Portal will allow users to run simulations directly on the web to avoid downloading and installing the software and to reduce simulation run-times.
  • Parameter optimization will allow users to specify a set of parameters that can be automatically optimized to fit recorded data.

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Get In Touch

Jones Laboratory
Department of Neuroscience
Brown University, Sidney Frank Hall, Providence, RI 02912
hnneurosolver@gmail.com