Consensus guidelines for the use of concurrent TMS-fMRI in cognitive and clinical neuroscience
Bibliographic record
Abstract
Concurrent transcranial magnetic stimulation (TMS) and functional magnetic resonance imaging (TMS-fMRI) provides a step-change in the toolkit of neuroscience research. TMS enables the noninvasive perturbation of ongoing human brain activity, and when coupled to fMRI for the simultaneous read-out of its effects across the brain, concurrent TMS-fMRI enables studies aimed at determining the causal inference of human brain-behavior relationships, with implications for both fundamental research and clinical application. Many of the technical barriers to TMS-fMRI implementation, such as hardware design and setups, have now been overcome, and the research community in the field is rapidly growing. Here, we present the guidelines set by an international consensus, from researchers at all levels and across the fields of cognitive and applied human neuroscience, for the experimental design and practical considerations of concurrent TMS-fMRI via 12 detailed use cases. These guidelines may facilitate the uptake of this approach and simplify the experimental design and planning stages.
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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 | 0.050 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.012 | 0.005 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".