Sulci and gyri are topological cerebral landmarks in individual subjects: a study of brain navigation during tumour resection
Bibliographic record
Abstract
Surgical resection of brain tumours aims at the maximal safe resection of the pathological tissue with minimal functional impairment. To achieve this objective, reliable anatomical landmarks are indispensable to navigate into the brain. The neuronavigation system can provide information to target the location of the patient's lesion, but after the craniotomy, a brain shift and relaxation mismatch with it often occur. By contrast, sulci/gyri are topological cerebral landmarks in individual patients and do shift with the brain parenchyma during lesion removal, but remain independent from brain shift in relation to the sulci/gyri. Here, we present a case report of a novel strategy based on anatomical landmarks to guide intraoperative brain tumour resection, without using a standard neuronavigation system. A preoperative brain mapping of the peri-tumoural sulci by the MRI and surface reconstruction was followed by confirmation of the anatomical landmarks for the motor cortex using navigated transcranial magnetic stimulation. The resulting location was used as a seed for diffusion tensor imaging tractography to reconstruct the corticospinal tracts. These selected cortical landmarks (sulci/gyri) delimited the margins of the two lesions and the specific location under which the corticospinal tract courses, thus facilitating monitoring of the peri-tumoural region during brain resection. In this case, 96% of the brain tumour from the pericentral somatomotor region was successfully removed without chronic post-operative motor impairments. This approach is based on cortical anatomy that is fixed during surgery and does not suffer from the brain shift that could misplace the lesion according to the neuronavigation system.
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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) | 0.001 | 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".