Closed-Loop Estimation of Neurostimulation Strength-Duration Curve Using Fisher Information Optimization and Comparison With Uniform and Random Methods
Bibliographic record
Abstract
Abstract Background Strength-duration (SD) curve, rheobase and chronaxie parameters provide insights about the interdependence between stimulus strength and stimulus duration (or pulse width), and the neural activation dynamics such as the membrane time constant, which are useful for diagnostics and therapeutic applications. The existing SD curve estimation methods are based on open-loop uniform and/or random selection of the pulse widths. Objective To develop a method for closed-loop estimation of the SD curve. Method In the proposed method, after the selection of each pulse width through Fisher information matrix (FIM) optimization, the corresponding motor threshold (MT) is computed, the SD curve estimation is updated, and the process continues until satisfaction of a stopping rule based on the successive convergence of the SD curve parameters. The results are compared with various uniform methods where pulse widths are chosen in ascending, descending and random orders, and with methods with two and all non-uniform random pulse widths. Results 160 simulation cases were run. The FIM method satisfied the stopping rule in 144 runs, and estimated the rheobase (chronaxie in parenthesis) with an average absolute relative error (ARE) of 1.73% (2.46%), with an average of 82 samples. At this point, methods with two and all random pulse widths, and uniform methods with descending, ascending and random orders led to 5.66% (20.27%), 2.15% (4.51%), 8.57% (54.96%), 3.52% (5.45%), and 2.19% (4.40%) AREs, which are greater than that achieved through the FIM method. In all 160 runs, The FIM method has chosen the minimum and maximum pulse widths as the optimal pulse widths. Conclusions The SD curve is identifiable by acquiring the SD data from the minimum and maximum pulse widths achieved through the FIM optimization. The SD data at random or uniform pulse widths from only the vertical area or lower plateau of the curve might not result in satisfactory estimation. Significance This paper provides insights about pulse widths selection in closed-loop and open-loop SD curve estimation methods.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".