The mechanisms of electrical neuromodulation
Bibliographic record
Abstract
The central and peripheral nervous systems are specialized to conduct electrical currents that underlie behaviour. When this multidimensional electrical system is disrupted by degeneration, damage, or disuse, externally applied electrical currents may act to modulate neural structures and provide therapeutic benefit. The administration of electrical stimulation can exert precise and multi-faceted effects at cellular, circuit and systems levels to restore or enhance the functionality of the central nervous system by providing an access route to target specific cells, fibres of passage, neurotransmitter systems, and/or afferent/efferent communication to enable positive changes in behaviour. Here we examine the neural mechanisms that are thought to underlie the therapeutic effects seen with current neuromodulation technologies. To gain further insights into the mechanisms associated with electrical stimulation, we summarize recent findings from genetic dissection studies conducted in animal models. KEY POINTS: Electricity is everywhere around us and is essential for how our nerves communicate within our bodies. When nerves are damaged or not working properly, using exogenous electricity can help improve their function at distinct levels - inside individual cells, within neural circuits, and across entire systems. This method can be tailored to target specific types of cells, nerve fibres, neurotransmitters and communication pathways, offering significant therapeutic potential. This overview explains how exogenous electricity affects nerve function and its potential benefits, based on research in animal studies. Understanding these effects is important because electrical neuromodulation plays a key role in medical treatments for neurological conditions.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".