Optimal blocking of the cerebral cortex for cytoarchitectonic examination: a neuronavigation-based approach
Bibliographic record
Abstract
Certain sulci of the human cerebral cortex hold consistent relationships to cytoarchitectonic areas (e.g. the primary motor cortical area 4 and the somatosensory cortical area 3 occupy the anterior and posterior banks of the central sulcus, respectively). Recent research has improved knowledge of the cortical sulci and their variability across individuals. However, other than the so-called primary sulci, understanding of the precise relationships cortical folds hold with many cytoarchitectonic areas remains elusive. To examine these relationships, the cortex must be blocked, sectioned, and histologically processed in a manner that allows the cytoarchitectonic layers to be clearly observed. The optimal strategy to view the cytoarchitecture is to block and section the cortex perpendicular to the sulcal orientation. Most cytoarchitectonic investigations of the cortex, however, have been conducted on specimens cut along a single axis (e.g. the coronal plane), which distorts the appearance of the cytoarchitectonic layers within parts of the cortical ribbon not sectioned optimally. Thus, to understand further the relationships between sulci and cytoarchitectonic areas, the cortex should be sectioned optimally to the sulci of interest. A novel approach for blocking the cortex optimally using structural magnetic resonance imaging (MRI) and surgical neuronavigation tools is presented here.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".