Robust S1, S2, and thalamic activations in individual subjects with vibrotactile stimulation at 1.5 and 3.0 T
Bibliographic record
Abstract
Functional magnetic resonance imaging (fMRI) is often used to enhance visualization and provide target localization during the planning phase of neurosurgical procedures. Although parametric maps have been used to identify areas of eloquent cortex such as the primary (S1) and secondary (S2) somatosensory areas for tumor surgery, to date, few fMRI methods exist to localize subcortical targets for surgical interventions used to treat movement disorders. The scanning time required to obtain statistically significant functional signals must be balanced against the possibility of movement artifacts and patient discomfort. We propose a vibrotactile stimulation technique to activate the somatosensory pathway for neurosurgical planning and perform a sensitivity analysis to determine the amount of time required to achieve significant activations of S1, S2, and sensory thalamus in individual subjects. Bilateral stimulation experiments were carried out on two MRI scanners (n = 13 at 1.5 T; n = 5 at 3.0 T). The analysis demonstrates that statistically significant functional activations can be achieved in clinically acceptable times: 16 min at 1.5 T (26/26 experiments) and 6 min at 3.0 T (10/10) for S1 activations; 24 min at 1.5 T (22/26) and 18 min at 3.0 T for S2 activations (9/10); and 32 min at 1.5 T (15/26) and 18 min at 3.0 T (10/10) for activation of thalamic nuclei. These results demonstrate that S1 and S2 activations are robust at 1.5 and 3.0 T, and that robust thalamic activations in individual subjects are possible at 3.0 T. These techniques demonstrate that this technique can be used for preoperative planning for surgical candidates.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".