Investigators
Sergey N Makarov/Aapo Nummenmaa/William A Wartman
Contact info (email)
makarov@wpi.edu/nummenma@nmr.mgh.harvard.edu/wawartman@wpi.edu
1. Define context(s)
aid in FDA decision making
aid in clinical decision making
aid in clinical trial design
identify/explore new therapies
reveal new biological insights
Current Conformance Level / Target Conformance Level
3.0/5
Primary goal of the model/tool/database
Develop, validate, disseminate, and apply a new high-resolution numerical simulation tool for bioelectromagnetic modeling of human brain (including both neurophysiological recordings - EEG/MEG/iEEG and neurostimulation - TMS/TES/DBS) based on the new formulation of the boundary element fast multipole method - BEM-FMM.
This formulation significantly outperforms the finite element method (FEM) in both accuracy and speed (by the factor up to 100) for piecewise-homogeneous models such the standard multi-compartment head models obtained from T1/T2 MRI data.
Additionally, the tool is capable of computing electric and magnetic fields in ensembles of morphologically reconstructed realistic cortical neurons, which is impossible with the finite element method.
The tool cannot yet handle arbitrary spatial medium anisotropy but major efforts are made to solve this problem.
Biological domain of the model
Human organisms
Structure(s) of interest in the model
Central nervous system
Spatial scales included in the model
Centimeter, millimeter, micrometer
Time scales included in the model
~1 Microsecond or greater: the models are quasi-static
Other uses for the model (optional)
1. Linearized Computational Fluid Dynamics (CFD) of Cerebrospinal Fluid (CSF)
2. Linearized Computational Fluid Dynamics (CFD) of brain vascular supply
3. Linearized Computational Fluid Dynamics (CFD) of upper respiratory in application to modeling viral infections
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.) | YES | NO | NIH supported research | 3.5/5 |
| ex vivo (excised tissues) | ||||
| in vivo pre-clinical (lower-level organism or small animal) | ||||
| in vivo pre-clinical (large animal) | ||||
| Human subjects/clinical | YES | NO | NIH supported research | 3.5/5 |
| 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) | ||||
| in vivo pre-clinical (large animal) | ||||
| Human subjects/clinical | YES | NO | NIH supported research | 3.5/5 |
| Other: ________________________ |
3. Validate within context(s)
| Who does it? | When does it happen? | How is it done? | Current Conformance Level / Target Conformance Level | |
|---|---|---|---|---|
| Verification | team of developers, independent researchers | 2019-2024 | comparison against experiement and other software packages | 4/5 |
| Validation | independent third party researchers | 2019-2024 | application to unique needs of different research teams | 3/5 |
| Uncertainty quantification | team of developers, independent researchers | 2019-2024 | evaluation of low-order moments of the outputs, i.e. mean and variance | 2.5/5 |
| Sensitivity analysis | team of developers, independent researchers | 2019-2024 | One-at-a-time (OAT), linear regression | 2.5/5 |
| Other:__________ | ||||
| Additional Comments |
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 |
|---|---|---|---|
| https://tmscorelab.github.io/TMS-Modeling-Website/Terms%20and%20Conditions.html | any TMS user | publications/GitHub | 4/5 |
| https://tmscorelab.github.io/TES-Modeling-Website/Terms%20and%20Conditions.html | any TES user | publications/GitHub | 4/5 |
| https://tmscorelab.github.io/EEG_MEG-Modeling-Website/Terms%20and%20Conditions.html | any EEG/MEG user | publications/GitHub | 4/5 |
5. Version control
| Current Conformance Level / Target Conformance Level |
|---|
| 2/5 |
| Naming Conventions? | Repository? | Code Review? | |
|---|---|---|---|
| individual modeler | v.X.Y | GitHub | every 6 months |
| within the lab | v.X.Y | GitHub | |
| collaborators | v.X.Y | GitHub |
6. Documentation
| Current Conformance Level / Target Conformance Level | |
|---|---|
| Code commented? | YES |
| Scope and intended use described? | YES |
| User’s guide? | YES, all in GitHub - see three links above |
| Developer’s guide? | NO |
7. Dissemination
| Current Conformance Level / Target Conformance Level |
|---|
| 2.5/5 |
| Target Audience(s): | “Inner circle” | Scientific community | Public |
|---|---|---|---|
| Simulations | X | X | |
| Models | X | X | X |
| Software | X | X | |
| Results | X | X | |
| Implications of results | X | X | X |
8. Independent reviews
| Current Conformance Level / Target Conformance Level |
|---|
| 2.5/5 |
| Reviewer(s) name & affiliation: | Dr. Shelley Fried/Massachusetts General Hospital Boston |
|---|---|
| When was review performed? | 2019-2020 |
| How was review performed and outcomes of the review? | Using software - software adopted |
9. Test competing implementations
| Current Conformance Level / Target Conformance Level |
|---|
| 2.5/5 - Open-source FEM: SimNIBS, Roast |
| Yes or No (briefly summarize) | |
|---|---|
| Were competing implementations tested? | YES, extensively |
| Did this lead to model refinement or improvement? | YES |
10. Conform to standards
| Current Conformance Level / Target Conformance Level |
|---|
| 2/5 |
| 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? | Contacting hardware providers/implementing correct device parameters/dimensions |
11. (optional) Additional information to support items 1-10
GitHub links:
https://tmscorelab.github.io/TMS-Modeling-Website/
https://tmscorelab.github.io/TES-Modeling-Website/
https://tmscorelab.github.io/EEG_MEG-Modeling-Website/
Relevant publications:
- Makarov SN, Hämäläinen M, Okada Y, Noetscher GM, Ahveninen J, Nummenmaa A. Boundary Element Fast Multipole Method for Enhanced Modeling of Neurophysiological Recordings. IEEE Trans. Biomed. Eng. 2020 June 1. doi: 10.1109/TBME.2020.2999271, PubMed PMID: 32746015.
- Makarov SN, Wartman WA, Daneshzand M, Fujimoto K, Raij T, Nummenmaa A. A software toolkit for TMS electric-field modeling with boundary element fast multipole method: An efficient MATLAB implementation. J Neural Eng. 2020 Aug 4;17(4):046023. doi: 10.1088/1741-2552/ab85b3, PubMed PMID: 32235065.
- Htet AT, Saturnino GB, Burnham EH, Noetscher G, Nummenmaa A, Makarov SN. Comparative performance of the finite element method and the boundary element fast multipole method for problems mimicking transcranial magnetic stimulation (TMS). J Neural Eng. 2019 Apr;16(2):024001. doi: 10.1088/1741-2552/aafbb9, PubMed PMID: 30605893, PubMed Central PMCID: PMC6546501.
- Makarov SN, Noetscher GM, Raij T, Nummenmaa A. A Quasi-Static Boundary Element Approach with Fast Multipole Acceleration for High-Resolution Bioelectromagnetic Models. IEEE Trans Biomed Eng. 2018 Mar 7. doi: 10.1109/TBME.2018.2813261, PubMed PMID: 29993385; PubMed Central PMCID: PMC7388683.
The model context of use includes:
1. Applications to transcranial magnetic stimulation (TMS) - high resolution individualized TMS targeting
https://tmscorelab.github.io/TMS-Modeling-Website/
2. Applications to transcranial electrical stimulation (TES) - high resolution individualized TES targeting
https://tmscorelab.github.io/TES-Modeling-Website/
3. Application to deep brain stimulation (DBS) - computing initial polarization of axonal fiber on the microlevel
GitHub under construction
4. Application to electroencephalography (EEG) - solving the direct/inverse EEG problems for a cortical equivalent dipole layer or for more realistic ensembles of cortical neurons with the unlimited number of sources
https://tmscorelab.github.io/EEG_MEG-Modeling-Website/
5. Application to magnetoencephalography (MEG) - solving the direct/inverse MEG problems for a cortical equivalent dipole layer or for more realistic ensembles of cortical neurons with the unlimited number of sources
https://tmscorelab.github.io/EEG_MEG-Modeling-Website/