Concordance Between BeamF3 and MRI-neuronavigated Target Sites for Repetitive Transcranial Magnetic Stimulation of the Left Dorsolateral Prefrontal Cortex
Bibliographic record
Abstract
BACKGROUND: The dorsolateral prefrontal cortex (DLPFC) is a common target for repetitive transcranial magnetic stimulation (rTMS) in major depression, but the conventional "5 cm rule" misses DLPFC in >1/3 cases. Another heuristic, BeamF3, locates the F3 EEG site from scalp measurements. MRI-guided neuronavigation is more onerous, but can target a specific DLPFC stereotaxic coordinate directly. The concordance between these two approaches has not previously been assessed. OBJECTIVE: To quantify the discrepancy in scalp site between BeamF3 versus MRI-guided neuronavigation for left DLPFC. METHODS: Using 100 pre-treatment MRIs from subjects undergoing left DLPFC-rTMS, we localized the scalp site at minimum Euclidean distance from a target MNI coordinate (X - 38 Y + 44 Z + 26) derived from our previous work. We performed nasion-inion, tragus-tragus, and head-circumference measurements on the same subjects' MRIs, and applied the BeamF3 heuristic. We then compared the distance between BeamF3 and MRI-guided scalp sites. RESULTS: BeamF3-to-MRI-guided discrepancies were <0.65 cm in 50% of subjects, <0.99 cm in 75% of subjects, and <1.36 cm in 95% of subjects. The angle from midline to the scalp site did not differ significantly using MRI-guided versus BeamF3 methods. However, the length of the radial arc from vertex to target site was slightly but significantly longer (mean 0.35 cm) with MRI-guidance versus BeamF3. CONCLUSIONS: The BeamF3 heuristic may provide a reasonable approximation to MRI-guided neuronavigation for locating left DLPFC in a majority of subjects. A minor optimization of the heuristic may yield additional concordance.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".