Concurrent TMS and functional magnetic resonance imaging: methods and current advances
Bibliographic record
Abstract
Transcranial magnetic stimulation is used for a wide range of applications in cognitive, clinical, and neuroscience. However, the precise physiological mechanisms by which TMS influences brain function are only partially understood. Combining TMS with functional magnetic resonance imaging (fMRI) provides a more complete picture of the neural underpinnings of TMS effects. This article gives an overview of methodology and technical aspects concerned with combining TMS with fMRI. Furthermore, it explains the challenges involved with the combination of TMS with fMRI and proposes solutions to the same. It also focuses on recent applications of concurrent TMS-fMRI. Combining TMS with fMRI may allow a new noninvasive probe technique for the human brain. TMS-fMRI can be used to compare TMS-evoked effective connectivity in health and disease. It can potentially be used to investigate connectivity changes during different states, with different degrees of involvement for interconnected brain regions during different tasks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".