Preliminary investigation on EEG phase-triggered TMS with concurrent fMRI
Bibliographic record
Abstract
This approach ensures that stimulation is directed toward relevant neural pathways, improving the precision of dual-site TMS.Synchronization of paired-pulse stimulation across both sites is tightly controlled, ensuring accurate interstimulus intervals (ISIs).In a pilot experiment, motor evoked potentials were recorded after stimulation of the abductor pollicis brevis (APB) muscles in both hemispheres, with the robotic systems maintaining both coils position.Integrating machine learning and tractography enabled more accurate and automated identification of stimulation targets, further refining the targeting process.Compared to manual methods, our system demonstrated superior accuracy (0.3 mm in distance and 0.2 in angle deviations) and reduced variability.This innovative approach minimizes reliance on operator expertise and enhances stimulation reproducibility.By combining machine learning, tractography, and robotic precision, this dual-site TMS system enables advanced brain stimulation techniques, including motor mapping, hotspot identification, and network-based stimulation protocols, providing a more effective and reliable platform for both research and clinical applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".