The cortical organization of language: distilling human connectome insights for supratentorial neurosurgery
Bibliographic record
Abstract
Connectomics is the production and study of detailed "connection" maps within the nervous system. With unprecedented advances in imaging and high-performance computing, the construction of individualized connectomes for routine neurosurgical use is on the horizon. Multiple projects, including the Human Connectome Project (HCP), have unraveled new and exciting data describing the functional and structural connectivity of the brain. However, the abstraction from much of these data to clinical relevance remains elusive. In the context of preserving neurological function after supratentorial surgery, abstracting surgically salient points from the vast computational data in connectomics is of paramount importance. Herein, the authors discuss four interesting observations from the HCP data that have surgical relevance, with an emphasis on the cortical organization of language: 1) the existence of a motor speech area outside of Broca's area, 2) the eloquence of the frontal aslant tract, 3) the explanation of the medial frontal cognitive control networks, and 4) the establishment of the second ventral stream of language processing. From these connectome observations, the authors discuss the anatomical basis of their insights as well as relevant clinical applications. Together, these observations provide a firm platform for neurosurgeons to advance their knowledge of the cortical networks involved in language and to ultimately improve surgical outcomes. It is hoped that this report encourages neurosurgeons to explore new vistas in connectome-based neurosurgery.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".