Direct visualization of deep brain stimulation targets in Parkinson disease with the use of 7-tesla magnetic resonance imaging
Bibliographic record
Abstract
OBJECT: A challenge associated with deep brain stimulation (DBS) in treating advanced Parkinson disease (PD) is the direct visualization of brain nuclei, which often involves indirect approximations of stereotactic targets. In the present study, the authors compared T2*-weighted images obtained using 7-T MR imaging with those obtained using 1.5- and 3-T MR imaging to ascertain whether 7-T imaging enables better visualization of targets for DBS in PD. METHODS: The authors compared 1.5-, 3-, and 7-T MR images obtained in 11 healthy volunteers and 1 patient with PD. RESULTS: With 7-T imaging, distinct images of the brain were obtained, including the subthalamic nucleus (STN) and internal globus pallidus (GPi). Compared with the 1.5- and 3-T MR images of the STN and GPi, the 7-T MR images showed marked improvements in spatial resolution, tissue contrast, and signal-to-noise ratio. CONCLUSIONS: Data in this study reveal the superiority of 7-T MR imaging for visualizing structures targeted for DBS in the management of PD. This finding suggests that by enabling the direct visualization of neural structures of interest, 7-T MR imaging could be a valuable aid in neurosurgical procedures.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".