Non-conductive vs. conductive cell membranes – a reassessment of this assumption when modeling cells under magnetic field stimulation
Bibliographic record
Abstract
Experimental and modeling studies have shown that the amount of neuronal cell polarization by electric and magnetic stimulation is dependent on the stimulation parameters and the properties of the target neuronal tissue. This cell-field (i.e. directly applied or magnetically-induced electric field) interdependency determines the efficacy of a cellular response to electric and magnetic stimulation, such as during deep brain stimulation (DBS) or transcranial magnetic stimulation (TMS). To study this cell-field interaction, particularly when computing the amount of tissue polarization induced by an applied field, several modeling works assume a non-conductive cell membrane. This assumption is based on the fact that the membrane conductivity is several orders lower compared to that of the extracellular medium or cytoplasm. Although this assumption greatly simplifies the computation of the field-induced transmembrane potential, its impact on the dependency of the transmembrane potential to the stimulus parameters (i.e., field intensity and frequency, and its orientation to the target tissue), as well as to the tissue properties (electric and geometric), have not been addressed. We have previously computed the transmembrane potential induced by a low frequency magnetic field in a single cell model with a conductive membrane [ 1 ]. Here, we have extended this analysis to a cell model with a non-conductive membrane, and investigated the relationship between the transmembrane potential with that of the stimulus parameters and tissue properties under this assumption. 1. The assumption of a non-conductive membrane did not cause a noticeable difference in the transmembrane potential (both pattern and amplitude) as compared with a conductive membrane. 2. Similarly, this assumption did not compromise the linear relationship between the transmembrane potential and the intensity, frequency and cell-coil distance. 3. However, a non-conductive membrane changed the dependency of the transmembrane potential to geometrical tissue properties (i.e. cell radius and membrane thickness) from a nonlinear to linear relationship. 4. Furthermore, assuming the membrane to be non-conductive completely compromised the dependency of the transmembrane potential to electrical tissue properties. The transmembrane potential became insensitive to the conductivities in the extracellular medium and the cytoplasm, which is in disagreement with several experimental studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".