Assessment of Corticospinal Excitability in Awake Rodents Using EMG-Controlled Intracortical Stimulation
Bibliographic record
Abstract
Assessment of corticospinal excitability (CSE) is an essential component of experiments designed to induce or study neuronal plasticity in the motor system. Common examples are paired associative stimulation (PAS), theta-burst stimulation (TBS), intensive motor training, or any methods aimed at potentiating the corticomotor system in the hope of promoting better recovery after neurological insult. To date, rodent models of CSE assessment have mostly been completed under anaesthesia, which greatly affects the level of CSE, as well as the mechanisms of plasticity. Experiments in awake animals are difficult because the ongoing state of behavior affects the excitability of the motor system and complicates the assessment of CSE. To address this issue, we have designed a novel approach for CSE assessment in awake behaving rodents, enabling a reliable measure of evoked motor responses obtained from cortical microstimulation in repeatable conditions of ongoing motor activity. The system relies on chronically implanted intracortical and intramuscular electrodes and a custom-made software control system, enabling the user to require that precise parameters of EMG activity be met before cortical stimulation probes are delivered. This approach could be used for further studies of PAS, TBS or other interventions requiring the assessment of CSE under repeatable conditions. We provide fabrication schematics and a list of materials for the implant, as well as instructions for running a custom-made MATLAB codebase, customizing the PAS protocol, and performing the complete analysis of experimental data. We hope these tools can further facilitate animal research in the field of neuroplasticity and neurorehabilitation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".