Development and validation of a 3D-printed neuronavigation headset for therapeutic brain stimulation
Bibliographic record
Abstract
BACKGROUND: Accurate neuronavigation is essential for optimal outcomes in therapeutic brain stimulation. MRI-guided neuronavigation, the current gold standard, requires access to MRI and frameless stereotaxic equipment, which is not available in all settings. Scalp-based heuristics depend on operator skill, with variable reproducibility across operators and sessions. An intermediate solution would offer superior reproducibility and ease-of-use to scalp measurements, without requiring MRI and frameless stereotaxy. OBJECTIVE: We present and assess a novel neuronavigation method using commercially-available, inexpensive 3D head scanning, computer-aided design, and 3D-printing tools to fabricate form-fitted headsets for individuals that hold a stimulator, such as an rTMS coil, in the desired position over the scalp. METHODS: 20 individuals underwent scanning for fabrication of individualized headsets designed for rTMS of the left dorsolateral prefrontal cortex (DLPFC). An experienced operator then performed three trials per participant of three neuronavigation methods: MRI-guided, scalp-measurement (BeamF3 method), and headset placement, and marked the sites obtained. Accuracy (versus MRI-guidance) and reproducibility were measured for each trial of each method. RESULTS: Within-subject accuracy (against a gold-standard centroid of three MRI-guided localizations) for MRI-guided, scalp-measurement, and headset methods was 3.7 ± 1.6 mm, 14.8 ± 7.1 mm, and 9.7 ± 5.2 mm respectively, with headsets significantly more accurate (M = 5.1, p = 0.008) than scalp-measurement methods. Within-subject reproducibility (against the centroid of 3 localizations in the same modality) was 3.7 ± 1.6 mm (MRI), 4.2 ± 1.4 (scalp-measurement), and 1.4 ± 0.7 mm (headset), with headsets achieving significantly better reproducibility than either other method (p < 0.0001). CONCLUSIONS: 3D-printed headsets may offer good accuracy, superior reproducibility and greater ease-of-use for stimulator placement over DLPFC, in settings where MRI-guidance is impractical.
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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".