P.063 Stereotactic targeting of hippocampal substructures using ultra-high field magnetic resonance imaging: Feasibility study in patients with epilepsy
Bibliographic record
Abstract
Background: The hippocampus can be divided longitudinally into the head, body, and tail; and unfolded medial-to-laterally into the subiculum, cornu ammonis (CA) sectors, and the dentate gyrus. Ultra-high field (≥ 7 Tesla; 7T) magnetic resonance imaging (MRI) enables submillimetric visualization of these hippocampal substructures which could be valuable for surgical targeting. Here, we assess the feasibility of using 7T MRI in conjunction with a novel computational unfolding method for image-based stereotactic targeting of hippocampal substructures. Methods: 53 patients with drug-resistant epilepsy were identified undergoing first-time implantation of the hippocampus. An image processing pipeline was created for computationally transforming post-operative electrode contact locations into our hippocampal coordinate system. Results: Of 178 implanted hippocampal electrodes (88 left; 49.4%), 25 (14.0%) were predominantly in the subiculum, 85 (47.8%) were in CA1, 23 (12.9%) were in CA2, 18 (10.1%) were in CA3/CA4, and 27 (15.2%) were in dentate gyrus. Along the longitudinal axis, hippocampal electrodes were most commonly implanted in the body (92; 51.7%) followed by the head (86; 48.3%). Conclusions: 7T MRI enables high-resolution anatomical imaging on the submillimeter scale in in vivo subjects. Here, we demonstrate the utility of 7T imaging for identifying the relative location of SEEG electrode implantations within hippocampal substructures for the invasive investigation of epilepsy.
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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 | 0.001 |
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".