Pharmacological adjuncts and transcranial magnetic stimulation-induced synaptic plasticity: a systematic review
Bibliographic record
Abstract
BACKGROUND: Transcranial magnetic stimulation (TMS) is a noninvasive neurostimulation modality that has been used to study human synaptic plasticity. Leveraging work in ex vivo preparations, mechanistically informed pharmacological adjuncts to TMS have been used to improve our fundamental understanding of TMS-induced synaptic plasticity. METHODS: We systematically reviewed the literature pairing pharmacological adjuncts with TMS plasticity-induction protocols in humans. We searched MEDLINE, PsycINFO, and Embase from 2013 to Mar. 10, 2023. Studies published before 2013 were extracted from a previous systematic review. We included studies using repetitive TMS, theta-burst stimulation, paired associative stimulation, and quadripulse stimulation paradigms in healthy and clinical populations. RESULTS: -methyl-d-aspartate (NMDA; 15 studies) or dopamine receptors (13 studies). The NMDA receptor is necessary for TMS-induced plasticity; however, sufficiency has not been shown across protocols. Dopaminergic modulation of TMS-induced plasticity appears to be dose-dependent. The GABAergic, cholinergic, noradrenergic, and serotonergic neurotransmitter systems have small evidence bases supporting modulation of TMS-induced plasticity, as do voltage-gated calcium and sodium channels. Studies in clinical populations suggest that pharmacological adjuncts to TMS may rescue motor cortex plasticity, with implications for therapeutic applications of TMS and a promising clinical trial in depression. LIMITATIONS: This review is limited by the predominance in the literature of studies with small sample sizes and crossover designs. CONCLUSION: Pharmacologically enhanced TMS largely parallels findings from ex vivo preparations. As this area expands and novel targets are tested, adequately powered samples in healthy and clinical populations will inform the mechanisms of TMS-induced plasticity in health and disease.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".