C.05 Direct visualization of the human zona incerta region using ultra-high field imaging: implications for stereotactic neurosurgery
Bibliographic record
Abstract
Background: The zona incerta (ZI) is a small structure in the deep brain first identified by Auguste Forel for which robust in vivo visualization has remained elusive. The increased inherent signal from ultra-high field (7-Tesla or greater; 7T) magnetic resonance imaging (MRI) presents an opportunity to see structures not previously visible. In this study, we investigated the possibility of using quantitative T1 mapping at 7T to visualize the ZI region. Methods: We recruited healthy participants (N=32) and patients being considered for deep brain stimulation therapy as part of a prospective imaging study at 7T. Computational methods were used to process and fuse images to produce a high-resolution group average from which ZI anatomy could be delineated. Results: We pooled 7T data using image fusion methods and found that the contrast from quantitative T1 mapping was strikingly similar to classic histological staining, permitting facile identification of the ZI and nearby structures in reference to conventional stereotactic atlases. Conclusions: Using computational neuroimaging techniques, we demonstrate for the first time that the ZI is visible in vivo. Furthermore, we determined that this nuclear region can be decoupled from surrounding fibre pathways. This work paves the way for more accurate patient-specific optimization of deep brain targets for neuromodulation.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".