P.175 Direct Visualization of Thalamic Nuclei using 7 Tesla MRI and quantification in patients with temporal lobe epilepsy.
Bibliographic record
Abstract
Background: Most individual thalamic nuclei cannot be directly visualized on routine clinical MRI. Stereotactic targeting techniques are indirect, relying on histological atlases and electrophysiological recording. We investigate whether high-field MRI can directly visualize the thalamic nuclei in vivo and allow for analysis of disease-related changes. Methods: Thirty-two healthy individuals were imaged with 7T MRI at a resolution of 0.7mm3. To obtain a high-resolution composite image, these were registered across subjects and averaged together. Three thalamic structures closely integrated in seizure propagation, the anterior thalamic nucleus (ATN), mammillothalamic tract (MTT), and centromedian nucleus (CM) were manually segmented in a subset of healthy subjects and patients with temporal lobe epilepsy (TLE). Results: There is sufficient resolution within the thalamus at 7T for visualization of the ATN, CM, and MTT. In the small subset of 5 controls and 5 TLE patients examined, there was no significant difference (p>0.05) in volume or mean T1map for the three thalamic sturctures of interest. Conclusions: MRI at 7T provides a method of direct visualization of thalamic nuclei, uncovering substructures not previously identifiable in vivo. These advances will enable quantitative analysis of disease-related changes to these structures and improved clinical targeting as demonstrated in this initial ‘proof-of-concept’ subset analysis.
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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 | 0.000 |
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".