Modulation of motoneuronal firing behavior after spinal cord injury using intraspinal microstimulation current pulses: a modeling study
Bibliographic record
Abstract
We simulated the effects of delivering focal electrical stimuli to the central nervous system to modulate the firing rate of neurons and alleviate motor disorders. Application of these stimuli to the spinal cord to reduce the increased excitability of motoneurons and resulting spasticity after spinal cord injury (SCI) was examined by means of a morphologically detailed computer model of a spinal motoneuron. High-frequency sinusoidal and rectangular pulses as well as biphasic charge-balanced and charge-imbalanced pulses were examined. Our results suggest that suprathreshold high-frequency sinusoidal or rectangular current pulses could inactivate the Na+ channels in the soma and initial segment, and block action potentials from propagating through the axon. Subthreshold biphasic charge-imbalanced pulses reduced the motoneuronal firing rate significantly (up to approximately 25% reduction). The reduction in firing rate was achieved through stimulation-induced hyperpolarization generated in the first node of Ranvier. Because of their low net DC current, these pulses could be tolerated safely by the tissue. To deliver charge-imbalanced pulses with the lowest net DC current and induce the largest reduction in motoneuronal firing rate, we studied the effect of various charge-imbalanced pulse parameters. Short pulse durations were found to induce the largest reduction in firing rate for the same net DC level. Subthreshold high-frequency sinusoidal and rectangular current pulses and low-frequency biphasic charge-balanced pulses, on the other hand, were ineffective in reducing the motoneuronal firing rate. In conclusion, the proposed electrical stimulation paradigms could provide potential rehabilitation interventions for suppressing the excitability of neurons to reduce the severity of motor disorders after injury to the central nervous system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".