Anatomic Study of the Central Core of the Cerebrum Correlating 7-T Magnetic Resonance Imaging and Fiber Dissection With the Aid of a Neuronavigation System
Bibliographic record
Abstract
BACKGROUND: Different strategies have been used to study the fiber tract anatomy of the human brain in vivo and ex vivo. Nevertheless, the ideal method to study white matter anatomy has yet to be determined because it should integrate information obtained from multiple sources. OBJECTIVE: We developed an anatomic method in cadaveric specimens to study the central core of the cerebrum combining traditional white matter dissection with high-resolution 7-T magnetic resonance imaging (MRI) of the same specimen coregistered using a neuronavigation system. METHODS: Ten cerebral hemispheres were prepared using the traditional Klingler technique. Before dissection, a structural ultrahigh magnetic field 7-T MRI study was performed on each hemisphere specifically prepared with surface fiducials for neuronavigation. The dissection was then performed from the medial hemispheric surface using the classic white fiber dissection technique. During each step of the dissection, the correlation between the anatomic findings and the 7-T MRI was evaluated with the neuronavigation system. RESULTS: The anatomic study was divided in 2 stages: diencephalic and limbic. The diencephalic stage included epithalamic, thalamic, hypothalamic, and subthalamic components. The limbic stage consisted of extending the dissection to complete the Papez circuit. The detailed information given by the combination of both methods allowed us to identify and validate the position of fibers that may be difficult to appreciate and dissect (ie, the medial forebrain bundle). CONCLUSION: The correlation of high-definition 7-T MRI and the white matter dissection technique with neuronavigation significantly improves the understanding of the structural connections in complex areas of the human cerebrum.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".