A normalized dataset of 1821 cortical and subcortical functional responses collected during direct electrical stimulation in patients undergoing awake brain surgery
Bibliographic record
Abstract
In this data article, we provide the dataset which served as the basis for our related research article "Mapping critical cortical hubs and white matter pathways by direct electrical stimulation: an original functional atlas of the human brain" [1], which represents the first probabilistic cortical and subcortical atlas of critical structures mediating human brain functions based on direct electrical stimulation (DES) in patients undergoing awake brain surgery. 1162 cortical and 659 subcortical DES-derived responses were recorded during testing of 16 functional domains in 256 patients undergoing awake surgery. Normalized [Montreal Neurological Institute (MNI) 152] spatial coordinates for cortical and subcortical responses, and probabilistic heat maps for each functional domain, were computed using methods previously developed by our group [2,3]. Source data, including the MNI-normalized coordinates of all 1821 DES-derived cortical and subcortical data points, and multi-planar (MNI-152, T1 1mm) videos showing the probabilistic distribution of each functional domain are provided. This novel dataset can improve and refine our understanding about the functional anatomy of critical brain networks, and these data are made available for medical and neuroscience applications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".