Development of FMRI techniques for planning in functional neurosurgery for Parkinson’s disease
Bibliographic record
Abstract
Pre-operative neurosurgical planning often uses data from functional magnetic resonance imaging (fMRI) to identify areas of eloquent cortex, such as the primary and secondary somatosensory cortices, to be spared during surgery. However, the in-vivo visualization of subcortical neurosurgical targets has typically involved the warping of subcortical atlases or T2- and diffusion-weighted imaging techniques to help define the anatomical borders. We propose a novel vibrotactile stimulation technique to activate the somatosensory pathway, and particularly the sensory thalamus. Experiments were executed on two MRI scanners (1.5T and 3.0T). A sensitivity analysis demonstrated that statistically significant functional activations of the sensory thalamus can be in achieved in clinically acceptable time (32 minutes at 1.5T and 12 minutes at 3.0T), thus enabling this technique to be used for pre-operative planning in patients.
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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 | 0.001 | 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".