Investigators
Stavros Zanos, MD PhD; Theodoros Zanos, PhD
Contact info (email)
szanos@northwell.edu
1. Define context(s)
identify/explore new therapies
reveal new biological insights
Current Conformance Level / Target Conformance Level
4
Primary goal of the model/tool/database
With this program, we aim to generate quantitative, nonparametric models for the estimation of compound action potentials of different fiber types, recorded spontaneously or in response to vagal stimuli, by looking at concurrent modulations in cardiopulmonary function captured by changes in heart rate, heart rate variability, breathing pattern and systemic and pulmonary hemodynamics. We also aim to understand possible sources of variability in these models related to species, sex and the micro-anatomy of the vagus nerve, to make them translatable to clinical neuromodulation therapies of cardiopulmonary disorders.
Biological domain of the model
Nerve stimulation
Structure(s) of interest in the model
Vagus nerve
Spatial scales included in the model
1 um to 3 cm
Time scales included in the model
0.1 ms to 12 hrs
Other uses for the model (optional)
Our models may assist VNS-based therapies of cardiopulmonary (CP) disorders over the next few years in 2 ways: First, by optimizing VNS parameters to engage specific fiber types, resulting in desirable physiological actions. Second, by optimizing the timing of delivery of VNS, e.g. stimulating during periods of low vagal tone and withholding stimulation during periods of high vagal tone.
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) | yes (partially) | Specific subjects are private, Averaged data is available | Measurements in individual subjects were reported several times throughout each experiments. | 4 |
| in vivo pre-clinical (large animal) | no | Specific subjects are private, Averaged data is available | Measurements in individual subjects were reported several times throughout each experiments. | 4 |
| Human subjects/clinical | ||||
| 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) | yes (partially) | Specific subjects are private, Averaged data is available | The source data is confirmed to meet detailed data requirements for consistency and source description | 4 |
| in vivo pre-clinical (large animal) | no | Specific subjects are private, Averaged data is available | The source data is confirmed to meet detailed data requirements for consistency and source description | 4 |
| Human subjects/clinical | ||||
| Other: ________________________ |
3. Validate within context(s)
| Who does it? | When does it happen? | How is it done? | Current Conformance Level / Target Conformance Level | |
|---|---|---|---|---|
| Verification | Software Engineer | Throughout development of the models with an independent review prior to release | Line by line review of code, testing of computed estimates through preset and precalculated example pairs | Extensive |
| Validation | Model developers and neurophysiologists will do independent validations | Throughout development and after each new experiment session | Models are used to reproduce vagal CAP activity during experimental scenarios. Retrospective validation on the currently available data will be done in a k-fold procedure. Prospective validation will be done using newly acquired data using preset parameters that will attempt to optimize CP function and fiber recruitment. In both retrospective and prospective validation, the model predictions will be quantitatively compared to the observed CAPs and resulting CP responses. | Adequate |
| Uncertainty quantification | Software Engineer | Throughout development of the model and after new experiments are completed | Computing confidence bounds of model coefficients estimated through least squares, monte carlo sampling to establish random predictors and establish 95% confidence interval | Adequate |
| Sensitivity analysis | Software Engineer | At the end of retrospective model development and after prospective model validation and re-training using new experimental data | Ranking of model inputs is done through a post-development ranking of coefficients for normalized inputsanking of model inputs is done through a post-development | Adequate |
| 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 |
|---|---|---|---|
| For all models, we assume that the subject is under general anesthesia | Healthcare providers, Experimentalists | A list of limitations is provided with each report from each model run and stated in the accompanying documentation. | Extensive |
| For all models, the hemodynamic state of the subject is assumed to be stable i.e. no significant changes in hemodynamic state that elicit compensatory responses | Healthcare providers, Experimentalists | A list of limitations is provided with each report from each model run and stated in the accompanying documentation. | Extensive |
5. Version control
| Current Conformance Level / Target Conformance Level |
|---|
| Extensive |
| Naming Conventions? | Repository? | Code Review? | |
|---|---|---|---|
| individual modeler | Yes | Yes | Yes |
| within the lab | Yes | Yes | Yes |
| collaborators | NA | NA | NA |
6. Documentation
| Current Conformance Level / Target Conformance Level | |
|---|---|
| Code commented? | Extensive: Python code commenting follows Docstring Conventions PEP 257 and Matlab code commenting follows MATLAB Style Guidelines 2.0, Richard Johnson) |
| Scope and intended use described? | Comprehensive: Yes - in both internal documentation and supporting material in publications |
| User’s guide? | Adequate |
| Developer’s guide? | Insufficient |
7. Dissemination
| Current Conformance Level / Target Conformance Level |
|---|
| Extensive |
| Target Audience(s): | “Inner circle” | Scientific community | Public |
|---|---|---|---|
| Simulations | |||
| Models | Yes | ||
| Software | Yes | ||
| Results | Yes | ||
| Implications of results | Yes |
8. Independent reviews
| Current Conformance Level / Target Conformance Level |
|---|
| Extensive |
| Reviewer(s) name & affiliation: | Kevin Tracey (Feinstein Inst), Stanisa Raspopovic (ETH) |
|---|---|
| When was review performed? | NA |
| How was review performed and outcomes of the review? | Presentation of concepts, data providence, results |
9. Test competing implementations
| Current Conformance Level / Target Conformance Level |
|---|
| Adequate |
| Yes or No (briefly summarize) | |
|---|---|
| Were competing implementations tested? | No |
| 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? | No |
| How do your modeling efforts conform? | NA |
11. (optional) Additional information to support items 1-10
The models proposed are not yet developed and are part of a research proposal.
HRV is particularly promising as a marker of vagal activity, as it is generally considered a proxy for vagal tone, unfortunately without direct empirical evidence. Our models will directly test whether afferent and/or efferent vagal fiber activity correlates with HRV and, if so, will quantify their relationship.