Toward a standard preoperative MRI protocol for functional neurosurgery
Bibliographic record
Abstract
Abstract Deep Brain Stimulation (DBS) is a well-established approach to treat movement disorders such as Parkinson’s Disease, dystonia or essential tremor. For optimal therapy response, accurate electrode placement is critical requiring high signal-to-noise of target areas in preoperative MRI. Currently, imaging protocols vary considerably between DBS centers, making it difficult to compare results or pool data for research purposes. Here, various currently employed MRI sequences from several DBS centers are evaluated regarding their suitability for DBS targeting and a protocol is suggested taking image quality and practical considerations into account. Two healthy subjects (52-year-old female and a 37-year-old male) were each scanned with various sequences (5 T2w, 1 PDw, 4 T2FLAIRw, 2 T2*w, 5 SWI, 2 FGATIR, 1 T1TIR, and 2 QSM techniques) that then were rated by 12 experienced DBS surgeons for their suitability for targeting the subthalamic nucleus (STN), the internal globus pallidus internus (GPi), and the ventrointermediate (VIM) thalamic nucleus. For a subset of sequences, surgeons were asked to identify the optimal DBS target in the STN and GPi. Contrast-to-noise ratios (CNR) were calculated and correlated to intra-rater z-scores and distances of target coordinates. For STN-DBS, surgeons rated T2w, most SWI, QSM, and T2FLAIRw the highest. For GPi-DBS, FGATIR, PDw, and SWI and for VIM-DBS, FGATIR were deemed the most suitable. Higher CNR correlated with higher intra-rater z-scores (R2 = 0.29, p < .005) which improved targeting (R2 = 0.18, p < .05). Our MRI protocol suggestion is a first step toward standardizing preoperative imaging. All imaging data, MRI sequence parameters, and protocol files are made openly available.
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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".