Investigators
Stephanie Jones, Matti Hamalainen, Michael Hinesnie
Contact info (email)
Stephanie_Jones@Brown.edu
1. Define context(s)
identify/explore new therapies
reveal new biological insights
Current Conformance Level / Target Conformance Level
extensive
Primary goal of the model/tool/database
Our model was developed for researchers and clinician who may not have neural modeling experience to be able to develop and test predictions on the neural origin of fast time scale EEG/MEG data (ms resolution) . The model represents a detailed canonical cortical circuit, with layer specific input from thalamus and higher-order cortex. The model simulates the primary electrical currents underlying EEG/MEG based on their biophysical origin from the intracellular current flow in pyramidal neuron dendrites. The model represents a patch of cortex in a single brain area. The model is embedded in a GUI and there is extensive document and tutorials of use distributed on our website https://hnn.brown.edu. Our tutorials are focussed on simulating the most commonly measured EEG/MEG signals including event related potentially (ERPs) and low frequency brain rhythms base on our prior published studies.
Biological domain of the model
Cortical circuit, with layer specific thalamic/cortical drive
Structure(s) of interest in the model
pyramidal neurons and interneurons
Spatial scales included in the model
patch of cortex from a signal brain area
Time scales included in the model
ms
Other uses for the model (optional)
In addition to the primary electrical currents producing EEG/MEG signals, users can visualize cell spiking activity and voltage responses. Methods are being developed to also visualize LFP and CSD signals to facilitate comparison to animal data. A unique feature of HNN is the ability to interpret signals across scales for testing and informing model prediction with animal studies or other human imaging modalities.
2. Data for building and validating the model
| Data for building the model | Published? | Private? | How is credibility checked? | Current Conformance Level / Target Conformance Level |
|---|---|---|---|---|
| in vitro (primary cells cell, lines, etc.) | ||||
| ex vivo (excised tissues) | ||||
| in vivo pre-clinical (lower-level organism or small animal) | X | publication | extensive | |
| in vivo pre-clinical (large animal) | X | publication | extensive | |
| Human subjects/clinical | X | publication | extensive | |
| Other: ________________________ |
| Data for validating the model | Published? | Private? | How is credibility checked? | Current Conformance Level / Target Conformance Level |
|---|---|---|---|---|
| in vitro (primary cells cell, lines, etc.) | ||||
| ex vivo (excised tissues) | ||||
| in vivo pre-clinical (lower-level organism or small animal) | X | publication | extensive | |
| in vivo pre-clinical (large animal) | X | publication | extensive | |
| Human subjects/clinical | X | publication | extensive | |
| Other: ________________________ |
3. Validate within context(s)
| Who does it? | When does it happen? | How is it done? | Current Conformance Level / Target Conformance Level | |
|---|---|---|---|---|
| Verification | ||||
| Validation | end user | after developing predictions | comparison to human EEG/MEG data, animal studies, other imaging modalities | adequate |
| Uncertainty quantification | end user | during optimization available for event related potential simulations | automatically using parameter optimization routines | adequate |
| Sensitivity analysis | ||||
| Other:__________ | ||||
| Additional Comments | We are in the process of developing tools for sensitivity analysis of the parameters. |
4. Limitations
| Disclaimer statement (explain key limitations) | Who needs to know about this disclaimer? | How is this disclaimer shared with that audience? | Current Conformance Level / Target Conformance Level |
|---|---|---|---|
| HNN was created to be a hypothesis development and testing tool. While the underlying neural model is biophysically principled and based on generalizable features of cortical networks, and also includes neurons with realistic physiology and morphology (see details in Under the Hood), the model is nonetheless a reduced model of neocortical network dynamics. Any conclusions made are based on the underlying model assumptions, which are outlined in detail in our Under the Hood page. Users should become familiar with these assumptions at the start of using HNN. We have found the model to be useful for generating novel and testable hypotheses on the circuit origin of some of the most commonly measured signals, including ERPs and low-frequency oscillations3-7, which provide the basis for the tutorials | any users | website documentation and in our methods publication | extensive |
5. Version control
| Current Conformance Level / Target Conformance Level |
|---|
| adequate |
| Naming Conventions? | Repository? | Code Review? | |
|---|---|---|---|
| individual modeler | yes | https://github.com/orgs/jonescompneurolab/teams/hnn | we are in the process integrating continuous testing of any contribution to the software |
| within the lab | yes | https://github.com/orgs/jonescompneurolab/teams/hnn-core | we are in the process of streamlining the underlying code and creating and API |
| collaborators | yes | https://github.com/orgs/jonescompneurolab/teams/hnn |
6. Documentation
| Current Conformance Level / Target Conformance Level | |
|---|---|
| Code commented? | extensive |
| Scope and intended use described? | extensive |
| User’s guide? | extensive |
| Developer’s guide? | adequate |
7. Dissemination
| Current Conformance Level / Target Conformance Level |
|---|
| extensive |
| Target Audience(s): | “Inner circle” | Scientific community | Public |
|---|---|---|---|
| Simulations | https://github.com/orgs/jonescompneurolab/teams/hnn-core | https://github.com/orgs/jonescompneurolab/teams/hnn | https://hnn.brown.edu |
| Models | https://github.com/orgs/jonescompneurolab/teams/hnn-core | https://github.com/orgs/jonescompneurolab/teams/hnn | https://hnn.brown.edu |
| Software | https://github.com/orgs/jonescompneurolab/teams/hnn-core | https://github.com/orgs/jonescompneurolab/teams/hnn | https://hnn.brown.edu |
| Results | publications listed on website: https://hnn.brown.edu/index.php/publications/ | publications listed on website: https://hnn.brown.edu/index.php/publications/ | https://hnn.brown.edu |
| Implications of results | publications listed on website: https://hnn.brown.edu/index.php/publications/ | publications listed on website: https://hnn.brown.edu/index.php/publications/ | https://hnn.brown.edu |
8. Independent reviews
| Current Conformance Level / Target Conformance Level |
|---|
| extensive |
| Reviewer(s) name & affiliation: | Arjen Stolk, Donders Centre for Cognitive Neuroimaging, Netherlands |
|---|---|
| When was review performed? | Jan-Feb 2020 |
| How was review performed and outcomes of the review? | eLife publication - reviewing editor disclosed |
9. Test competing implementations
| Current Conformance Level / Target Conformance Level |
|---|
| insufficient |
| Yes or No (briefly summarize) | |
|---|---|
| Were competing implementations tested? | no - I'm not sure how this would be done. |
| Did this lead to model refinement or improvement? | no |
10. Conform to standards
| Current Conformance Level / Target Conformance Level |
|---|
| adequate |
| Yes or No (briefly summarize) | |
|---|---|
| Are there operating procedures, guidelines, or standards for this type of multiscale modeling? | yes |
| How do your modeling efforts conform? | we are using Neuron/Python which is a well supported and documented framework for simulating neural networks, our construction to simulate EEG.MEG is unique. |